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George Kapetanios

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. George Kapetanios & Vincent Labhard & Simon Price, 2007. "Forecast combination and the Bank of England’s suite of statistical forecasting models," Bank of England working papers 323, Bank of England.

    Mentioned in:

    1. Forecasting GDP in the presence of breaks: when is the past is a good guide to the future?
      by bankunderground in Bank Underground on 2015-08-20 11:30:00
    2. Forecasting GDP in the presence of breaks: when is the past a good guide to the future?
      by Guest Author in The Big Picture on 2015-09-01 14:00:11
  2. Giraitis, Liudas & Kapetanios, George & Price, Simon, 2013. "Adaptive forecasting in the presence of recent and ongoing structural change," Journal of Econometrics, Elsevier, vol. 177(2), pages 153-170.

    Mentioned in:

    1. Forecasting GDP in the presence of breaks: when is the past is a good guide to the future?
      by bankunderground in Bank Underground on 2015-08-20 11:30:00
    2. Forecasting GDP in the presence of breaks: when is the past a good guide to the future?
      by Guest Author in The Big Picture on 2015-09-01 14:00:11
  3. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.

    Mentioned in:

    1. Forecasting GDP in the presence of breaks: when is the past is a good guide to the future?
      by bankunderground in Bank Underground on 2015-08-20 11:30:00
    2. Forecasting GDP in the presence of breaks: when is the past a good guide to the future?
      by Guest Author in The Big Picture on 2015-09-01 14:00:11
  4. Kapetanios, George & Mumtaz, Haroon & Stevens, Ibrahim & Theodoridis, Konstantinos, 2012. "Assessing the economy-wide effects of quantitative easing," Bank of England working papers 443, Bank of England.

    Mentioned in:

    1. On QE
      by chris dillow in Stumbling and Mumbling on 2012-02-06 21:06:45
    2. The problem with promises
      by chris in Stumbling and Mumbling on 2015-04-14 18:16:07

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. George Kapetanios & Tony Yates, 2010. "Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 869-893.

    Mentioned in:

    1. Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models (Journal of Applied Econometrics 2010) in ReplicationWiki ()
  2. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.

    Mentioned in:

    1. Forecasting large datasets with Bayesian reduced rank multivariate models (Journal of Applied Econometrics 2011) in ReplicationWiki ()
  3. A. Chudik & G. Kapetanios & M. Hashem Pesaran, 2018. "A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High‐Dimensional Linear Regression Models," Econometrica, Econometric Society, vol. 86(4), pages 1479-1512, July.

    Mentioned in:

    1. A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models (ECTA 2018) in ReplicationWiki ()

Working papers

  1. Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023. "Forecasting Value-at-Risk using deep neural network quantile regression," Essex Finance Centre Working Papers 34837, University of Essex, Essex Business School.

    Cited by:

    1. Naveed, Hafiz Muhammad & Pan, Yanchun & Yao, HongXing & Al-Faryan, Mamdouh Abdulaziz Saleh, 2024. "Assessing the nexus between currency exchange rate returns, currency risk hedging and international investments: Intelligent network-based analysis," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    2. León Beleña & Ernesto Curbelo & Luca Martino & Valero Laparra, 2024. "Second-Moment/Order Approximations by Kernel Smoothers with Application to Volatility Estimation," Mathematics, MDPI, vol. 12(9), pages 1-15, May.
    3. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    4. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    5. Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024. "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, vol. 92(C).

  2. Richard T. Baillie & Francis X. Diebold & George Kapetanios & Kun Ho Kim & Aaron Mora, 2022. "On Robust Inference in Time Series Regression," Papers 2203.04080, arXiv.org, revised May 2024.

    Cited by:

    1. Richard T. Baillie & Francis X. Diebold & George Kapetanios & Kun Ho Kim, 2022. "A New Test forMarket Efficiency and Uncovered Interest Parity," PIER Working Paper Archive 22-029, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Richard K. Crump & Nikolay Gospodinov & Ignacio Lopez Gaffney, 2024. "A Jackknife Variance Estimator for Panel Regressions," Staff Reports 1133, Federal Reserve Bank of New York.

  3. Richard T. Baillie & Francis X. Diebold & George Kapetanios & Kun Ho Kim, 2022. "A New Test for Market Efficiency and Uncovered Interest Parity," Papers 2211.01344, arXiv.org.

    Cited by:

    1. Constantin Bürgi & Mengdi Song, 2024. "Do Professional Forecasters Follow Uncovered Interest Rate Parity?," CESifo Working Paper Series 11338, CESifo.

  4. Fabio Calonaci & George Kapetanios & Simon Price, 2022. "Stock returns predictability with unstable predictors," CAMA Working Papers 2022-04, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Polbin, Andrey & Skrobotov, Anton, 2022. "On decrease in oil price elasticity of GDP and investment in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 5-24.

  5. George Kapetanios & Fotis Papailias, 2022. "An Evaluation Framework for Targeted Indicators Aggregates vs. Disaggregates," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-17, Economic Statistics Centre of Excellence (ESCoE).

    Cited by:

    1. George Kapetanios & Fotis Papailias, 2022. "A Quality Assessment Framework for Maintaining & Publishing New Indicators," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-18, Economic Statistics Centre of Excellence (ESCoE).

  6. George Kapetanios & Fotis Papailias, 2022. "Real Time Indicators During the COVID-19 Pandemic Individual Predictors & Selection," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-15, Economic Statistics Centre of Excellence (ESCoE).

    Cited by:

    1. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.

  7. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.

    Cited by:

    1. Kakuho Furukawa & Ryohei Hisano, 2022. "A Nowcasting Model of Exports Using Maritime Big Data," Bank of Japan Working Paper Series 22-E-19, Bank of Japan.

  8. George Kapetanios & Fotis Papailias, 2021. "UK Economic Conditions during the Pandemic: Assessing the Economy using ONS Faster Indicators," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2021-10, Economic Statistics Centre of Excellence (ESCoE).

    Cited by:

    1. George Kapetanios & Fotis Papailias, 2022. "A Quality Assessment Framework for Maintaining & Publishing New Indicators," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-18, Economic Statistics Centre of Excellence (ESCoE).
    2. George Kapetanios & Fotis Papailias, 2022. "Real Time Indicators During the COVID-19 Pandemic Individual Predictors & Selection," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-15, Economic Statistics Centre of Excellence (ESCoE).
    3. Alex Botsis & Kevin Lee, 2022. "Nowcasting Using Firm-Level Survey Data; Tracking UK Output Fluctuations and Recessionary Events," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-20, Economic Statistics Centre of Excellence (ESCoE).
    4. George Kapetanios & Fotis Papailias, 2022. "An Evaluation Framework for Targeted Indicators Aggregates vs. Disaggregates," Economic Statistics Centre of Excellence (ESCoE) Technical Reports ESCOE-TR-17, Economic Statistics Centre of Excellence (ESCoE).

  9. Joseph, Andreas & Kalamara, Eleni & Kapetanios, George & Potjagailo, Galina & Chakraborty, Chiranjit, 2021. "Forecasting UK inflation bottom up," Bank of England working papers 915, Bank of England, revised 27 Sep 2022.

    Cited by:

    1. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    2. Gabe de Bondt & Arne Gieseck & Pablo Herrero & Zivile Zekaite, 2021. "Euro Area Income and Wealth Effects: Aggregation Issues," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1454-1474, December.
    3. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2024. "Nowcasting consumer price inflation using high-frequency scanner data: evidence from Germany," Working Paper Series 2930, European Central Bank.
    4. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    5. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    6. Simionescu, Mihaela, 2022. "Econometrics of sentiments- sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    7. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.

  10. Kalamara, Eleni & Turrell, Arthur & Redl, Chris & Kapetanios, George & Kapadia, Sujit, 2020. "Making text count: economic forecasting using newspaper text," Bank of England working papers 865, Bank of England.

    Cited by:

    1. Nyman, Rickard & Kapadia, Sujit & Tuckett, David & Gregory, David & Ormerod, Paul & Smith, Robert, 2018. "News and narratives in financial systems: exploiting big data for systemic risk assessment," Bank of England working papers 704, Bank of England.
    2. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    3. Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
    4. Erik Andres-Escayola & Corinna Ghirelli & Luis Molina & Javier J. Perez & Elena Vidal, 2024. "Using Newspapers for Textual Indicators: Guidance Based on Spanish- and Portuguese-Speaking Countries," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 643-692, August.
    5. Gerardin Mathilde, & Ranvier Martial., 2021. "Enrichment of the Banque de France’s monthly business survey: lessons from textual analysis of business leaders’ comments," Working papers 821, Banque de France.
    6. Danilo Cascaldi-Garcia & Deepa Dhume Datta & Thiago Revil T. Ferreira & Olesya V. Grishchenko & Mohammad R. Jahan-Parvar & Juan M. Londono & Francesca Loria & Sai Ma & Marius del Giudice Rodriguez & J, 2020. "What is Certain about Uncertainty?," International Finance Discussion Papers 1294, Board of Governors of the Federal Reserve System (U.S.).
      • Danilo Cascaldi-Garcia & Cisil Sarisoy & Juan M. Londono & Bo Sun & Deepa D. Datta & Thiago Ferreira & Olesya Grishchenko & Mohammad R. Jahan-Parvar & Francesca Loria & Sai Ma & Marius Rodriguez & Ilk, 2023. "What Is Certain about Uncertainty?," Journal of Economic Literature, American Economic Association, vol. 61(2), pages 624-654, June.
    7. Luca Barbaglia & Sergio Consoli & Sebastiano Manzan, 2024. "Forecasting GDP in Europe with textual data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 338-355, March.
    8. Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021. "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series 2616, European Central Bank.
    9. Pijush Kanti Das & Prabir Kumar Das, 2024. "Improvement in Inflation Forecasting: Ensembling Text Mining with Macro Data in Machine Learning Models," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 16(6), pages 1-92, June.
    10. Bondarenko, Yevheniia & Lewis, Vivien & Rottner, Matthias & Schüler, Yves, 2024. "Geopolitical risk perceptions," Discussion Papers 37/2024, Deutsche Bundesbank.
    11. Rho Caterina & Fernández Raúl & Palma Brenda, 2021. "A Sentiment-based Risk Indicator for the Mexican Financial Sector," Working Papers 2021-04, Banco de México.
    12. Zahner, Johannes & Baumgärtner, Martin, 2022. "Whatever it Takes to Understand a Central Banker – Embedding their Words Using Neural Networks," VfS Annual Conference 2022 (Basel): Big Data in Economics 264019, Verein für Socialpolitik / German Economic Association.
    13. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    14. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    15. Lange, Kai-Robin & Reccius, Matthias & Schmidt, Tobias & Müller, Henrik & Roos, Michael W. M. & Jentsch, Carsten, 2022. "Towards extracting collective economic narratives from texts," Ruhr Economic Papers 963, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    16. Mary Chen & Matthew DeHaven & Isabel Kitschelt & Seung Jung Lee & Martin Sicilian, 2023. "Identifying Financial Crises Using Machine Learning on Textual Data," International Finance Discussion Papers 1374, Board of Governors of the Federal Reserve System (U.S.).
    17. He, Yu & Lu, Shanglin & Wei, Ran & Wang, Shixuan, 2024. "Local media sentiment towards pollution and its effect on corporate green innovation," International Review of Financial Analysis, Elsevier, vol. 94(C).
    18. Efstathios Polyzos & Ghulame Rubbaniy & Mieszko Mazur, 2024. "Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency," The Financial Review, Eastern Finance Association, vol. 59(3), pages 807-829, August.
    19. Julian Ashwin & Eleni Kalamara & Lorena Saiz, 2024. "Nowcasting Euro area GDP with news sentiment: A tale of two crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 887-905, August.
    20. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    21. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Papers No 08/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    22. Hongcheng Ding & Xuanze Zhao & Zixiao Jiang & Shamsul Nahar Abdullah & Deshinta Arrova Dewi, 2024. "EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods," Papers 2408.13214, arXiv.org.
    23. Olivier De Bandt & Jean-Charles Bricongne & Julien Denes & Alexandre Dhenin & Annabelle De Gaye & Pierre-Antoine Robert, 2023. "Using the Press to Construct a New Indicator of Inflation Perceptions in France," Working papers 921, Banque de France.
    24. Massimo Ferrari Minesso & Laura Lebastard & Helena Mezo, 2023. "Text-Based Recession Probabilities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 415-438, June.
    25. Felix Drinkall & Janet B. Pierrehumbert & Stefan Zohren, 2024. "Traditional Methods Outperform Generative LLMs at Forecasting Credit Ratings," Papers 2407.17624, arXiv.org.
    26. Leonardo N. Ferreira, 2021. "Forecasting with VAR-teXt and DFM-teXt Models:exploring the predictive power of central bank communication," Working Papers Series 559, Central Bank of Brazil, Research Department.
    27. Kurov, Alexander & Sancetta, Alessio & Wolfe, Marketa Halova, 2022. "Drift Begone! Release policies and preannouncement informed trading," Journal of International Money and Finance, Elsevier, vol. 128(C).
    28. Valentina Aprigliano & Simone Emiliozzi & Gabriele Guaitoli & Andrea Luciani & Juri Marcucci & Libero Monteforte, 2021. "The power of text-based indicators in forecasting the Italian economic activity," Temi di discussione (Economic working papers) 1321, Bank of Italy, Economic Research and International Relations Area.
    29. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    30. Maureen Cowhey & Seung Jung Lee & Thomas Popeck Spiller & Cindy M. Vojtech, 2022. "Sentiment in Bank Examination Reports and Bank Outcomes," Finance and Economics Discussion Series 2022-077, Board of Governors of the Federal Reserve System (U.S.).
    31. Shrub, Yuliya & Rieger, Jonas & Müller, Henrik & Jentsch, Carsten, 2022. "Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators," Ruhr Economic Papers 964, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    32. Erik Andres-Escayola & Corinna Ghirelli & Luis Molina & Javier J. Pérez & Elena Vidal, 2022. "Using newspapers for textual indicators: which and how many?," Working Papers 2235, Banco de España.
    33. Dooruj Rambaccussing & Craig Menzies & Andrzej Kwiatkowski, 2022. "Look who’s Talking: Individual Committee members’ impact on inflation expectations," Dundee Discussion Papers in Economics 305, Economic Studies, University of Dundee.
    34. Hanjo Odendaal, 2021. "A machine learning approach to domain specific dictionary generation. An economic time series framework," Working Papers 06/2021, Stellenbosch University, Department of Economics.
    35. Stolbov, Mikhail & Shchepeleva, Maria & Karminsky, Alexander, 2022. "When central bank research meets Google search: A sentiment index of global financial stress," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    36. Andres Algaba & Samuel Borms & Kris Boudt & Brecht Verbeken, 2021. "Daily news sentiment and monthly surveys: A mixed–frequency dynamic factor model for nowcasting consumer confidence," Working Paper Research 396, National Bank of Belgium.
    37. Dorinth van Dijk & Jasper de Winter, 2023. "Nowcasting GDP using tone-adjusted time varying news topics: Evidence from the financial press," Working Papers 766, DNB.
    38. Ibrahim Filiz & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2021. "Sticky Stock Market Analysts," JRFM, MDPI, vol. 14(12), pages 1-27, December.
    39. Lin Chen & Stephanie Houle, 2023. "Turning Words into Numbers: Measuring News Media Coverage of Shortages," Discussion Papers 2023-8, Bank of Canada.
    40. Mikhaylov, Dmitry, 2023. "Macroeconomic Forecasting with the Use of News Data," Working Papers w20220250, Russian Presidential Academy of National Economy and Public Administration.
    41. María del Pilar Cruz N. & Hugo Peralta V. & Juan Pablo Cova M., 2022. "Utilización de noticias de prensa como indicador de confianza económica en tiempo real," Working Papers Central Bank of Chile 938, Central Bank of Chile.
    42. Łukasz Baszczak, 2023. "Ekonomia narracji – początki nowego nurtu," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 1, pages 66-81.
    43. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "News media versus FRED‐MD for macroeconomic forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 63-81, January.
    44. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    45. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
    46. Sonya Georgieva, 2023. "Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 177-199.

  11. Marcellino, Massimiliano & Kapetanios, George & Dendramis, Yiannis, 2020. "A Similarity-based Approach for Macroeconomic Forecasting," CEPR Discussion Papers 14469, C.E.P.R. Discussion Papers.

    Cited by:

    1. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    2. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    3. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    4. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
    5. Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).

  12. Kapetanios, G. & Price, S. & Tasiou, M. & Ventouri, A., 2020. "State-level wage Phillips curves," Working Papers 20/08, Department of Economics, City University London.

    Cited by:

    1. Rotundo, G. & Ausloos, M., 2007. "Microeconomic co-evolution model for financial technical analysis signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 569-585.
    2. Johannes Schuffels & Clemens Kool & Lenard Lieb & Tom van Veen, 2022. "Is the Slope of the Euro Area Phillips Curve Steeper than It Seems? Heterogeneity and Identification," CESifo Working Paper Series 10103, CESifo.
    3. Alderremy, A.A. & Saad, Khaled M. & Agarwal, Praveen & Aly, Shaban & Jain, Shilpi, 2020. "Certain new models of the multi space-fractional Gardner equation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    4. Li, Zhiyu & Xu, Xiwei & Jiang, Enchen & Han, Ping & Sun, Yan & Zhou, Ling & Zhong, Peidong & Fan, Xudong, 2020. "Alkane from hydrodeoxygenation (HDO) combined with in-situ multistage condensation of biomass continuous pyrolysis bio-oil via mixed supports catalyst Ni/HZSM-5-γ-Al2O3," Renewable Energy, Elsevier, vol. 149(C), pages 535-548.
    5. Schuffels, Johannes & Kool, Clemens & Lieb, Lenard & van Veen, Tom, 2024. "Is the slope of the euro area Phillips curve steeper than it seems? Heterogeneity and identification," Journal of International Money and Finance, Elsevier, vol. 148(C).
    6. El-Shagi, Makram & Tochkov, Kiril, 2024. "Regional heterogeneity and the provincial Phillips curve in China," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1036-1044.
    7. Zhu, H.B. & Zhou, Y.J. & Wu, W.J., 2020. "Modeling traffic flow mixed with automated vehicles considering drivers ’ character difference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    8. Coma, Julià & Chàfer, Marta & Pérez, Gabriel & Cabeza, Luisa F., 2020. "How internal heat loads of buildings affect the effectiveness of vertical greenery systems? An experimental study," Renewable Energy, Elsevier, vol. 151(C), pages 919-930.
    9. Nie, Binjian & Zou, Boyang & She, Xiaohui & Zhang, Tongtong & Li, Yongliang & Ding, Yulong, 2020. "Development of a heat transfer coefficient based design method of a thermal energy storage device for transport air-conditioning applications," Energy, Elsevier, vol. 196(C).
    10. Shi, Xin & Jiang, Haizhou & Li, Huan & Xu, Dong, 2020. "Maritime cluster research: Evolutionary classification and future development," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 237-254.
    11. Yoo, Yeawon & Escobedo, Adolfo R. & Skolfield, J. Kyle, 2020. "A new correlation coefficient for comparing and aggregating non-strict and incomplete rankings," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1025-1041.

  13. Marcellino, Massimiliano & Kapetanios, George & Giraitis, Liudas, 2020. "Time-Varying Instrumental Variable Estimation," CEPR Discussion Papers 15210, C.E.P.R. Discussion Papers.

    Cited by:

    1. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    2. Michele Fratianni & Federico Giri & Riccardo Lucchetti & Francesco Valentini, 2022. "Monetization, wars, and the Italian fiscal multiplier," Mo.Fi.R. Working Papers 176, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    3. Mikihito Nishi, 2024. "Estimating Time-Varying Parameters of Various Smoothness in Linear Models via Kernel Regression," Papers 2406.14046, arXiv.org, revised Oct 2024.
    4. Lucchetti, Riccardo & Valentini, Francesco, 2021. "Kernel-based Time-Varying IV estimation: handle with care," MPRA Paper 110033, University Library of Munich, Germany.
    5. Francisco Blasques & Noah Stegehuis, 2024. "A Score-Driven Filter for Causal Regression Models with Time- Varying Parameters and Endogenous Regressors," Tinbergen Institute Discussion Papers 24-016/III, Tinbergen Institute.
    6. Yu Bai & Massimiliano Marcellino & George Kapetanios, 2023. "Mean Group Instrumental Variable Estimation of Time-Varying Large Heterogeneous Panels with Endogenous Regressors," Monash Econometrics and Business Statistics Working Papers 13/23, Monash University, Department of Econometrics and Business Statistics.

  14. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2020. "Measurement of Factor Strenght: Theory and Practice," CESifo Working Paper Series 8146, CESifo.

    Cited by:

    1. Pesaran, M. H. & Smith, R. P., 2023. "The Role of Pricing Errors in Linear Asset Pricing Models with Strong, Semi-strong, and Latent Factors," Cambridge Working Papers in Economics 2317, Faculty of Economics, University of Cambridge.
    2. Pesaran, M. Hashem & Smith, Ron P., 2023. "Arbitrage pricing theory, the stochastic discount factor and estimation of risk premia from portfolios," Econometrics and Statistics, Elsevier, vol. 26(C), pages 17-30.
    3. In Choi & Rui Lin & Yongcheol Shin, 2020. "Canonical Correlation-based Model Selection for the Multilevel Factors," Working Papers 2008, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    4. Ge, Shuyi & Li, Shaoran & Linton, Oliver, 2023. "News-implied linkages and local dependency in the equity market," Journal of Econometrics, Elsevier, vol. 235(2), pages 779-815.
    5. Ge, S., 2020. "Text-Based Linkages and Local Risk Spillovers in the Equity Market," Cambridge Working Papers in Economics 20115, Faculty of Economics, University of Cambridge.
    6. M. Hashem Pesaran & Takashi Yamagata, 2017. "Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities," Discussion Papers 17/04, Department of Economics, University of York.
    7. M. Hashem Pesaran & Run Smith, 2021. "Arbitrage pricing theory, the stochastic discount factor and estimation of risk premia in portfolios," BCAM Working Papers 2108, Birkbeck Centre for Applied Macroeconomics.
    8. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
    9. Jie Wei & Yonghui Zhang, 2023. "Does Principal Component Analysis Preserve the Sparsity in Sparse Weak Factor Models?," Papers 2305.05934, arXiv.org, revised Nov 2024.
    10. Jianqing Fan & Yuling Yan & Yuheng Zheng, 2024. "When can weak latent factors be statistically inferred?," Papers 2407.03616, arXiv.org, revised Sep 2024.

  15. Yiannis Dendramis & Luidas Giraitis & George Kapetanios, 2020. "Estimation of time-varying covariance matrices for large datasets," Working Papers 916, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Hiraki, Kazuhiro & Sun, Chuanping, 2022. "A toolkit for exploiting contemporaneous stock correlations," Journal of Empirical Finance, Elsevier, vol. 65(C), pages 99-124.
    2. Yu Bai & Massimiliano Marcellino & George Kapetanios, 2023. "Mean Group Instrumental Variable Estimation of Time-Varying Large Heterogeneous Panels with Endogenous Regressors," Monash Econometrics and Business Statistics Working Papers 13/23, Monash University, Department of Econometrics and Business Statistics.

  16. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2019. "Time-varying cointegration and the UK great ratios," Bank of England working papers 789, Bank of England.

    Cited by:

    1. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2020. "Time-varying cointegration with an application to the UK Great Ratios," Economics Letters, Elsevier, vol. 193(C).

  17. George Kapetanios & Laura Serlenga & Yongcheol Shin, 2019. "Testing for Correlated Factor Loadings in Cross Sectionally Dependent Panels," SERIES 02-2019, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Jun 2019.

    Cited by:

    1. Yana Petrova & Joakim Westerlund, 2020. "Fixed effects demeaning in the presence of interactive effects in treatment effects regressions and elsewhere," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 960-964, November.
    2. Freeman, Hugo & Weidner, Martin, 2023. "Linear panel regressions with two-way unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 237(1).

  18. Kapetanios, George & Papailias, Fotis & Taylor, AM Robert, 2019. "A Generalised Fractional Differencing Bootstrap for Long Memory Processes," Essex Finance Centre Working Papers 24136, University of Essex, Essex Business School.

    Cited by:

    1. Arteche, Josu, 2024. "Bootstrapping long memory time series: Application in low frequency estimators," Econometrics and Statistics, Elsevier, vol. 29(C), pages 1-15.
    2. Marchese, Malvina & Kyriakou, Ioannis & Tamvakis, Michael & Di Iorio, Francesca, 2020. "Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models," Energy Economics, Elsevier, vol. 88(C).
    3. Lui, Yiu Lim & Phillips, Peter C.B. & Yu, Jun, 2024. "Robust testing for explosive behavior with strongly dependent errors," Journal of Econometrics, Elsevier, vol. 238(2).
    4. Peter C. B. Phillips, 2021. "Pitfalls in Bootstrapping Spurious Regression," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 163-217, December.
    5. Arteche González, Jesús María, 2020. "Frequency Domain Local Bootstrap in long memory time series," BILTOKI info:eu-repo/grantAgreeme, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).

  19. George Kapetanios & Laura Serlenga & Yongcheol Shin, 2019. "Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure," SERIES 03-2019, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Jun 2019.

    Cited by:

    1. Jiti Gao & Bin Peng & Yayi Yan, 2022. "Nonparametric Estimation and Testing for Time-Varying VAR Models," Monash Econometrics and Business Statistics Working Papers 3/22, Monash University, Department of Econometrics and Business Statistics.
    2. Guohua Feng & Jiti Gao & Bin Peng, 2021. "Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach," Monash Econometrics and Business Statistics Working Papers 16/21, Monash University, Department of Econometrics and Business Statistics.
    3. Nicholas L. Brown & Peter Schmidt & Jeffrey M. Wooldridge, 2021. "Simple Alternatives to the Common Correlated Effects Model," Papers 2112.01486, arXiv.org.
    4. Guohua Feng & Jiti Gao & Bin Peng, 2022. "Multi-Level Panel Data Models: Estimation and Empirical Analysis," Monash Econometrics and Business Statistics Working Papers 4/22, Monash University, Department of Econometrics and Business Statistics.
    5. Yang, Yimin, 2022. "A correlated random effects approach to the estimation of models with multiple fixed effects," Economics Letters, Elsevier, vol. 213(C).
    6. Hugo Freeman, 2022. "Linear multidimensional regression with interactive fixed-effects," Papers 2209.11691, arXiv.org, revised Aug 2024.

  20. Chiu, Ching-Wai (Jeremy) & hayes, simon & kapetanios, george & Theodoridis, Konstantinos, 2018. "A new approach for detecting shifts in forecast accuracy," Bank of England working papers 721, Bank of England.

    Cited by:

    1. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.

  21. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).

    Cited by:

    1. Mantas Lukauskas & Vaida Pilinkienė & Jurgita Bruneckienė & Alina Stundžienė & Andrius Grybauskas & Tomas Ruzgas, 2022. "Economic Activity Forecasting Based on the Sentiment Analysis of News," Mathematics, MDPI, vol. 10(19), pages 1-22, September.
    2. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    3. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    4. James Chapman & Ajit Desai, 2022. "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers 22-10, Bank of Canada.
    5. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    6. Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," EconomiX Working Papers 2020-11, University of Paris Nanterre, EconomiX.
    7. Jeffrey C. Chen & Abe Dunn & Kyle Hood & Alexander Driessen & Andrea Batch, 2019. "Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 373-402, National Bureau of Economic Research, Inc.
    8. Samuel N. Cohen & Silvia Lui & Will Malpass & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Andrew Reeves & Craig Scott & Emma Small & Lingyi Yang, 2023. "Nowcasting with signature methods," Papers 2305.10256, arXiv.org.
    9. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    10. Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021. "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers 446, Institute of Economic Growth.
    11. Jagjit S. Chadha & Richard Barwell, 2019. "Renewing our Monetary Vows: Open Letters to the Governor of the Bank of England," National Institute of Economic and Social Research (NIESR) Occasional Papers 58, National Institute of Economic and Social Research.

  22. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2018. "Exponent of Cross-sectional Dependence for Residuals," CESifo Working Paper Series 7223, CESifo.

    Cited by:

    1. Arnab Bhattacharjee & Jan Ditzen & Sean Holly, 2021. "Spatial and Spatio-temporal Error Correction, Networks and Common Correlated Effects," BEMPS - Bozen Economics & Management Paper Series BEMPS76, Faculty of Economics and Management at the Free University of Bozen.
    2. Keil, Sascha, 2024. "Competing for manufacturing value added: How strong is competitive cost pressure on sectoral level?," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 197-212.
    3. Arnab Bhattacharjee & Jan Ditzen & Sean Holly, 2022. "Spatial and Spatio-Temporal Error Correction, Networks and Common Correlated Effects," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, volume 43, pages 37-60, Emerald Group Publishing Limited.
    4. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    5. Telila, Henok Fasil, 2023. "Frontier markets sovereign risk: New evidence from spatial econometric models," Finance Research Letters, Elsevier, vol. 58(PD).
    6. Ge, Shuyi & Li, Shaoran & Linton, Oliver, 2023. "News-implied linkages and local dependency in the equity market," Journal of Econometrics, Elsevier, vol. 235(2), pages 779-815.
    7. Ge, S., 2020. "Text-Based Linkages and Local Risk Spillovers in the Equity Market," Cambridge Working Papers in Economics 20115, Faculty of Economics, University of Cambridge.
    8. Henok Fasil Telila, 2024. "Frontier markets sovereign risk: New evidence from spatial econometric models," French Stata Users' Group Meetings 2024 10, Stata Users Group.
    9. Arnab Bhattacharjee & Tapabrata Maiti, 2019. "P. C. Mahalanobis in the Context of Current Econometrics Research," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 1-11, September.
    10. Yu-Ke, Chen & Hassan, Muhammad Shahid & Kalim, Rukhsana & Mahmood, Haider & Arshed, Noman & Salman, Muhammad, 2022. "Testing asymmetric influence of clean and unclean energy for targeting environmental quality in environmentally poor economies," Renewable Energy, Elsevier, vol. 197(C), pages 765-775.
    11. Floros Flouros & Victoria Pistikou & Vasilios Plakandaras, 2022. "Geopolitical Risk as a Determinant of Renewable Energy Investments," Energies, MDPI, vol. 15(4), pages 1-21, February.
    12. Tullio Gregori & Marco Giansoldati, 2023. "Do current and capital account liberalizations affect economic growth in the long run?," Empirical Economics, Springer, vol. 65(1), pages 247-273, July.
    13. Kapetanios, G. & Pesaran, M.H. & Reese, S., 2021. "Detection of units with pervasive effects in large panel data models," Journal of Econometrics, Elsevier, vol. 221(2), pages 510-541.

  23. Dario Buono & George Kapetanios & Massimiliano Marcellino & Gianluigi Mazzi & Fotis Papailias, 2018. "Big Data Econometrics: Now Casting and Early Estimates," BAFFI CAREFIN Working Papers 1882, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    2. Fornaro, Paolo, 2020. "Nowcasting Industrial Production Using Uncoventional Data Sources," ETLA Working Papers 80, The Research Institute of the Finnish Economy.
    3. Irving Fisher Committee, 2023. "Data science in central banking: applications and tools," IFC Bulletins, Bank for International Settlements, number 59.
    4. George Kapetanios & Fotis Papailias, 2022. "Investigating the predictive ability of ONS big data‐based indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 252-258, March.
    5. Francisco Corona & Graciela Gonz'alez-Far'ias & Jes'us L'opez-P'erez, 2021. "A nowcasting approach to generate timely estimates of Mexican economic activity: An application to the period of COVID-19," Papers 2101.10383, arXiv.org.

  24. Petrova, Katerina & Kapetanios, George & Masolo, Riccardo & Waldron, Matthew, 2017. "A time varying parameter structural model of the UK economy," Bank of England working papers 677, Bank of England.

    Cited by:

    1. Áureo de Paula & Imran Rasul & Pedro CL Souza, 2023. "Identifying network ties from panel data: Theory and an application to tax competition," CeMMAP working papers 21/23, Institute for Fiscal Studies.
    2. Agustín Arias & Benjamín García & Ignacio Rojas, 2023. "Forward Guidance: Estimating a Behavioral DSGE Model with System Priors," Working Papers Central Bank of Chile 994, Central Bank of Chile.
    3. Siddhartha Chib & Minchul Shin & Fei Tan, 2020. "High-Dimensional DSGE Models: Pointers on Prior, Estimation, Comparison, and Prediction∗," Working Papers 20-35, Federal Reserve Bank of Philadelphia.
    4. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    5. Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Working Papers 202175, University of Pretoria, Department of Economics.
    6. Siddhartha Chib & Minchul Shin & Fei Tan, 2021. "DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors," Working Papers 21-02, Federal Reserve Bank of Philadelphia.
    7. Jinshun Wu & Luyao Wu, 2024. "Bayesian Local Likelihood Estimation of Time-Varying DSGE Models: Allowing for Indeterminacy," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2437-2476, October.
    8. Rangan Gupta & Xiaojin Sun, 2022. "Time-Varying Parameter Four-Equation DSGE Model," Working Papers 202234, University of Pretoria, Department of Economics.
    9. Xiao-Li Gong & Jin-Yan Lu & Xiong Xiong & Wei Zhang, 2022. "Higher-order dynamic effects of uncertainty risk under thick-tailed stochastic volatility," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-22, December.
    10. Aquilante, Tommaso & Chowla, Shiv & Dacic, Nikola & Haldane, Andrew & Masolo, Riccardo & Schneider, Patrick & Seneca, Martin & Tatomir, Srdan, 2019. "Market power and monetary policy," Bank of England working papers 798, Bank of England.

  25. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    2. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    3. Zheng, Tingguo & Ye, Shiqi & Hong, Yongmiao, 2023. "Fast estimation of a large TVP-VAR model with score-driven volatilities," Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
    4. Rajae Azrak & Guy Mélard, 2022. "Autoregressive Models with Time-Dependent Coefficients—A Comparison between Several Approaches," Stats, MDPI, vol. 5(3), pages 1-21, August.
    5. Sergei Seleznev, 2019. "Truncated priors for tempered hierarchical Dirichlet process vector autoregression," Bank of Russia Working Paper Series wps47, Bank of Russia.
    6. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    7. Davide Delle Monache & Ivan Petrella & Fabrizio Venditti, 2021. "Price Dividend Ratio and Long-Run Stock Returns: A Score-Driven State Space Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1054-1065, October.
    8. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    9. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    10. Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Macroeconomic Forecasting in a Multi-country Context," Working Papers 22-02, Federal Reserve Bank of Cleveland.
    11. Giacomo Rella, 2021. "The Fed, housing and household debt over time," Department of Economics University of Siena 850, Department of Economics, University of Siena.
    12. Jiménez-Rodríguez, Rebeca, 2022. "Oil shocks and global economy," Energy Economics, Elsevier, vol. 115(C).
    13. Lucchetti, Riccardo & Valentini, Francesco, 2021. "Kernel-based Time-Varying IV estimation: handle with care," MPRA Paper 110033, University Library of Munich, Germany.
    14. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    15. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    16. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    17. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
    18. Diogo de Prince & Emerson Fernandes Marçal & Pedro L. Valls Pereira, 2022. "Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy," Econometrics, MDPI, vol. 10(2), pages 1-34, June.
    19. S. Avouyi-Dovi & C. Labonne & R. Lecat & S. Ray, 2017. "Insight from a Time-Varying VAR Model with Stochastic Volatility of the French Housing and Credit Markets," Working papers 620, Banque de France.
    20. César Castro & Rebeca Jiménez-Rodríguez, 2020. "Dynamic interactions between oil price and exchange rate," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-20, August.
    21. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).

  26. Kapetanios, George & Price, Simon & Young, Garry, 2017. "A UK financial conditions index using targeted data reduction: forecasting and structural identification," Bank of England working papers 699, Bank of England.

    Cited by:

    1. Claudio Morana, 2021. "A new macro-financial condition index for the euro area," Working Papers 467, University of Milano-Bicocca, Department of Economics, revised Sep 2021.
    2. Duo Qin & Sophie van Huellen & Qing Chao Wang & Thanos Moraitis, 2022. "Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data," Econometrics, MDPI, vol. 10(2), pages 1-22, April.
    3. Syed Jawad Hussain Shahzad & Elie Bouri & Jose Arreola-Hernandez & David Roubaud & Stelios Bekiros, 2019. "Spillover across Eurozone credit market sectors and determinants," Applied Economics, Taylor & Francis Journals, vol. 51(59), pages 6333-6349, December.
    4. Mohsin, Muhammad & Ullah, Hafeez & Iqbal, Nadeem & Iqbal, Wasim & Taghizadeh-Hesary, Farhad, 2021. "How external debt led to economic growth in South Asia: A policy perspective analysis from quantile regression," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 423-437.
    5. Luke Hartigan & Michelle Wright, 2021. "Financial Conditions and Downside Risk to Economic Activity in Australia," RBA Research Discussion Papers rdp2021-03, Reserve Bank of Australia.
    6. Lodge, David & Soudan, Michel, 2019. "Credit, financial conditions and the business cycle in China," Working Paper Series 2244, European Central Bank.
    7. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2018. "Constructing Joint Confidence Bands for Impulse Response Functions of VAR Models: A Review," Discussion Papers of DIW Berlin 1762, DIW Berlin, German Institute for Economic Research.
    8. Luke Hartigan & Michelle Wright, 2023. "Monitoring Financial Conditions and Downside Risk to Economic Activity in Australia," The Economic Record, The Economic Society of Australia, vol. 99(325), pages 253-287, June.
    9. Somnath Chatterjee & Ching‐Wai (Jeremy) Chiu & Thibaut Duprey & Sinem Hacıoğlu‐Hoke, 2022. "Systemic Financial Stress and Macroeconomic Amplifications in the United Kingdom," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(2), pages 380-400, April.
    10. Bui Thanh Trung, 2022. "Measuring Monetary Policy in Emerging Economy: The Role of Monetary Condition Index," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 70(6), pages 499-522, June.

  27. Chudik, A. & Kapetanios, G. & Pesaran, Hashem, 2016. "A One-Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models," Cambridge Working Papers in Economics 1677, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios, 2022. "High Dimensional Generalised Penalised Least Squares," Papers 2207.07055, arXiv.org, revised Oct 2023.
    2. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2018. "Exponent of cross-sectional dependence for residuals," Monash Econometrics and Business Statistics Working Papers 13/18, Monash University, Department of Econometrics and Business Statistics.
    3. Viet Hoang Dinh & Didier Nibbering & Benjamin Wong, 2024. "Random Subspace Local Projections," Papers 2406.01002, arXiv.org.
    4. Christopher F Baum & Andrés Garcia-Suaza & Miguel Henry & Jesús Otero, 2024. "Drivers of COVID-19 in U.S. counties: A wave-level analysis," Boston College Working Papers in Economics 1067, Boston College Department of Economics.
    5. Mohsen Bahmani-Oskooee & Thouraya Hadj Amor & Ridha Nouira & Christophe Rault, 2019. "Political Risk and Real Exchange Rate: What Can We Learn from Recent Developments in Panel Data Econometrics for Emerging and Developing Countries?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(4), pages 741-762, December.
    6. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    7. Pesaran, H. & Yang, Cynthia Fan, 2016. "Econometric Analysis of Production Networks with Dominant Units," Cambridge Working Papers in Economics 1678, Faculty of Economics, University of Cambridge.
    8. Everett Grant & Julieta Yung, 2019. "Upstream, Downstream & Common Firm Shocks," Globalization Institute Working Papers 360, Federal Reserve Bank of Dallas.
    9. Ke-Li Xu & Junjie Guo, 2021. "A New Test for Multiple Predictive Regression," CAEPR Working Papers 2022-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    10. Andrés Garcia-Suaza & Miguel Henry & Jesús Otero & Kit Baum, 2023. "Drivers of COVID-19 deaths in the United States: A two-stage modeling approach," UK Stata Conference 2023 17, Stata Users Group.
    11. Chen, Song Xi & Guo, Bin & Qiu, Yumou, 2023. "Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding," Journal of Econometrics, Elsevier, vol. 235(2), pages 1337-1354.
    12. Ahmed, R. & Pesaran, M. H., 2020. "Regional Heterogeneity and U.S. Presidential Elections," Cambridge Working Papers in Economics 2092, Faculty of Economics, University of Cambridge.
    13. Kris James Mitchener & Gary Richardson, 2020. "Contagion of Fear," NBER Working Papers 26859, National Bureau of Economic Research, Inc.
    14. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    15. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    16. George Kapetanios & M. Hashem Pesaran & Simon Reese, 2018. "A Residual-based Threshold Method for Detection of Units that are Too Big to Fail in Large Factor Models," CESifo Working Paper Series 7401, CESifo.
    17. Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2021. "Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 290-314.
    18. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    19. Héctor M. Núñez & Jesús Otero, 2021. "A one covariate at a time, multiple testing approach to variable selection in high‐dimensional linear regression models: A replication in a narrow sense," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 833-841, September.
    20. Liang Chen & Juan Jose Dolado & Jesus Gonzalo, 2019. "Quantile Factor Models," Papers 1911.02173, arXiv.org, revised Sep 2020.
    21. Damian Kozbur, 2020. "Analysis of Testing‐Based Forward Model Selection," Econometrica, Econometric Society, vol. 88(5), pages 2147-2173, September.
    22. Holmes, Mark J. & Otero, Jesús, 2023. "Psychological price barriers, El Niño, La Niña: New insights for the case of coffee," Journal of Commodity Markets, Elsevier, vol. 31(C).
    23. Ahmed, Rashad & Pesaran, M. Hashem, 2022. "Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation," International Journal of Forecasting, Elsevier, vol. 38(2), pages 662-687.
    24. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    25. Katerina Chrysikou & George Kapetanios, 2024. "Heterogeneous Grouping Structures in Panel Data," Papers 2407.19509, arXiv.org.
    26. Alexander Chudik & Janet Koech & Mark Wynne, 2021. "The Heterogeneous Effects of Global and National Business Cycles on Employment in US States and Metropolitan Areas," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 495-517, April.
    27. M. Hashem Pesaran & Ron P. Smith, 2019. "The Role of Factor Strength and Pricing Errors for Estimation and Inference in Asset Pricing Models," CESifo Working Paper Series 7919, CESifo.
    28. Kapetanios, G. & Pesaran, M.H. & Reese, S., 2021. "Detection of units with pervasive effects in large panel data models," Journal of Econometrics, Elsevier, vol. 221(2), pages 510-541.

  28. A. Chudik & G. Kapetanios & M. Hashem Pesaran, 2016. "Big Data Analytics: A New Perspective," Cambridge Working Papers in Economics 1611, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Dr. Alain Galli & Dr. Christian Hepenstrick & Dr. Rolf Scheufele, 2017. "Mixed-frequency models for tracking short-term economic developments in Switzerland," Working Papers 2017-02, Swiss National Bank.
    2. Bai Huang & Tae-Hwy Lee & Aman Ullah, 2017. "A combined estimator of regression models with measurement errors," Indian Economic Review, Springer, vol. 52(1), pages 73-91, December.
    3. Everett Grant, 2018. "The Double-Edged Sword of Global Integration: Robustness, Fragility \& Contagion in the International Firm Network," 2018 Meeting Papers 506, Society for Economic Dynamics.
    4. Gabe J. Bondt, 2019. "A PMI-Based Real GDP Tracker for the Euro Area," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(2), pages 147-170, December.

  29. Ana Beatriz Galvão & Liudas Giraitis & George Kapetanios & Katerina Petrova, 2015. "A Time Varying DSGE Model with Financial Frictions," Working Papers 769, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2019. "Time-varying cointegration and the UK great ratios," Bank of England working papers 789, Bank of England.
    2. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2019. "Forecasting with instabilities: an application to DSGE models with financial frictions," Temi di discussione (Economic working papers) 1234, Bank of Italy, Economic Research and International Relations Area.
    3. Angelini, Giovanni & Gorgi, Paolo, 2018. "DSGE Models with observation-driven time-varying volatility," Economics Letters, Elsevier, vol. 171(C), pages 169-171.
    4. Alberto BUCCI & Simone MARSIGLIO, 2016. "Financial Development and Economic Growth: Long Run Equilibrium and Transitional Dynamics," Departmental Working Papers 2016-16, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    5. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    6. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    7. Helen Louri & Petros Migiakis, 2019. "Financing economic activity in Greece: Past challenges and future prospects," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 135, Hellenic Observatory, LSE.
    8. M.Emranul Haque & Paul Middleditch & Shuonan Zhang, 2018. "Financial development and innovation: A DSGE comparison of Chinese and US business cycles," Centre for Growth and Business Cycle Research Discussion Paper Series 244, Economics, The University of Manchester.
    9. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    10. Kapetanios, George & Masolo, Riccardo M. & Petrova, Katerina & Waldron, Matthew, 2019. "A time-varying parameter structural model of the UK economy," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.
    11. Jinshun Wu & Luyao Wu, 2024. "Bayesian Local Likelihood Estimation of Time-Varying DSGE Models: Allowing for Indeterminacy," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2437-2476, October.
    12. Helen Louri & Petros Migiakis, 2019. "Financing economic growth in Greece: lessons from the crisis," Working Papers 262, Bank of Greece.
    13. Olatunji Abdul Shobande & Oladimeji Tomiwa Shodipe, 2021. "Monetary Policy Interdependency in Fisher Effect: A Comparative Evidence," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(1), pages 203-226.
    14. Rangan Gupta & Xiaojin Sun, 2022. "Time-Varying Parameter Four-Equation DSGE Model," Working Papers 202234, University of Pretoria, Department of Economics.
    15. Giovanni Angelini & Paolo Gorgi, 2018. "DSGE Models with Observation-Driven Time-Varying parameters," Tinbergen Institute Discussion Papers 18-030/III, Tinbergen Institute.

  30. Ana Beatriz Galvão & Liudas Giraitis & George Kapetanios & Katerina Petrova, 2015. "A Bayesian Local Likelihood Method for Modelling Parameter Time Variation in DSGE Models," Working Papers 770, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Galvão, Ana Beatriz & Giraitis, Liudas & Kapetanios, George & Petrova, Katerina, 2016. "A time varying DSGE model with financial frictions," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 690-716.

  31. Liudas Giraitis & George Kapetanios & Tony Yates, 2015. "Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models," Working Papers 767, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Richard T. Baillie & Dooyeon Cho, 2016. "Assessing Euro Crises from a Time Varying International CAPM Approach," Working Paper series 16-03, Rimini Centre for Economic Analysis.
    2. Marcellino, Massimiliano & Kapetanios, George & Giraitis, Liudas, 2020. "Time-Varying Instrumental Variable Estimation," CEPR Discussion Papers 15210, C.E.P.R. Discussion Papers.
    3. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2019. "Time-varying cointegration and the UK great ratios," Bank of England working papers 789, Bank of England.
    4. Magnus Reif, 2022. "Time‐Varying Dynamics of the German Business Cycle: A Comprehensive Investigation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(1), pages 80-102, February.
    5. Chronopoulos, Ilias & Kapetanios, George & Petrova, Katerina, 2021. "Kernel-based Volatility Generalised Least Squares," Econometrics and Statistics, Elsevier, vol. 20(C), pages 2-11.
    6. Gustavo Fruet Dias & Marcelo Fernandes & Cristina M. Scherrer, 2016. "Component shares in continuous time," CREATES Research Papers 2016-25, Department of Economics and Business Economics, Aarhus University.
    7. Klein, Mathias & Linnemann, Ludger, 2020. "The time-varying effect of fiscal policy on inflation: Evidence from historical US data," Economics Letters, Elsevier, vol. 186(C).
    8. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    9. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    10. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    11. Alonso-Alvarez, Irma & Di Nino, Virginia & Venditti, Fabrizio, 2022. "Strategic interactions and price dynamics in the global oil market," Energy Economics, Elsevier, vol. 107(C).
    12. Dias, Gustavo Fruet & Fernandes, Marcelo & Scherrer, Cristina Mabel, 2017. "Improving on daily measures of price discovery," Textos para discussão 444, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    13. Laura Liu & Christian Matthes & Katerina Petrova, 2022. "Monetary Policy Across Space and Time," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 37-64, Emerald Group Publishing Limited.
    14. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    15. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2020. "Time-varying cointegration with an application to the UK Great Ratios," Economics Letters, Elsevier, vol. 193(C).
    16. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.
    17. Yoosoon Chang & Boreum Kwak, 2017. "U.S. Monetary-Fiscal Regime Changes in the Presence of Endogenous Feedback in Policy Rules," CAEPR Working Papers 2017-016, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    18. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
    19. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.
    20. Lucchetti, Riccardo & Valentini, Francesco, 2021. "Kernel-based Time-Varying IV estimation: handle with care," MPRA Paper 110033, University Library of Munich, Germany.
    21. Laura Liu & Christian Matthes & Katerina Petrova, 2018. "Monetary Policy across Space and Time," Working Paper 18-14, Federal Reserve Bank of Richmond.
    22. Zongwu Cai & Xiyuan Liu, 2021. "Solving the Price Puzzle Via A Functional Coefficient Factor-Augmented VAR Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202106, University of Kansas, Department of Economics, revised Jan 2021.
    23. Yayi Yan & Jiti Gao & Bin peng, 2020. "A Class of Time-Varying Vector Moving Average (infinity) Models," Monash Econometrics and Business Statistics Working Papers 39/20, Monash University, Department of Econometrics and Business Statistics.
    24. Yu Bai & Massimiliano Marcellino & George Kapetanios, 2023. "Mean Group Instrumental Variable Estimation of Time-Varying Large Heterogeneous Panels with Endogenous Regressors," Monash Econometrics and Business Statistics Working Papers 13/23, Monash University, Department of Econometrics and Business Statistics.
    25. Richard T. Baillie & Fabio Calonaci & George Kapetanios, 2019. "Hierarchical Time Varying Estimation of a Multi Factor Asset Pricing Model," Working Papers 879, Queen Mary University of London, School of Economics and Finance.
    26. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    27. Goodhead, Robert, 2024. "The economic impact of yield curve compression: Evidence from euro area forward guidance and unconventional monetary policy," European Economic Review, Elsevier, vol. 164(C).
    28. Camba-Méndez, Gonzalo, 2020. "On the inflation risks embedded in sovereign bond yields," Working Paper Series 2423, European Central Bank.
    29. Kapetanios, George & Zikes, Filip, 2018. "Time-varying Lasso," Economics Letters, Elsevier, vol. 169(C), pages 1-6.
    30. Yayi Yan & Jiti Gao & Bin Peng, 2020. "A Class of Time-Varying Vector Moving Average Models: Nonparametric Kernel Estimation and Application," Papers 2010.01492, arXiv.org.
    31. Philippe Goulet Coulombe, 2024. "The macroeconomy as a random forest," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 401-421, April.

  32. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2015. "Inference for Impulse Response Coefficients From Multivariate Fractionally Integrated Processes," Working Paper series 15-46, Rimini Centre for Economic Analysis.

    Cited by:

    1. Emawtee Bissoondoyal-Bheenick & Robert Brooks & Wei Chi & Hung Xuan Do, 2018. "Volatility spillover between the US, Chinese and Australian stock markets," Australian Journal of Management, Australian School of Business, vol. 43(2), pages 263-285, May.

  33. Churm, Rohan & Joyce, Mike & Kapetanios, George & Theodoridis, Konstantinos, 2015. "Unconventional monetary policies and the macroeconomy: the impact of the United Kingdom's QE2 and Funding for Lending Scheme," Bank of England working papers 542, Bank of England.

    Cited by:

    1. Dario Caldara & Etienne Gagnon & Enrique Martínez García & Christopher J. Neely, 2020. "Monetary Policy and Economic Performance since the Financial Crisis," Finance and Economics Discussion Series 2020-065, Board of Governors of the Federal Reserve System (U.S.).
    2. Gros, Daniel & Valiante, Diego & De Groen, Willem Pieter, 2016. "The ECB’s latest gimmick: Cash for loans," CEPS Papers 11425, Centre for European Policy Studies.
    3. Brian Fabo & Martina Jancokova & Elisabeth Kempf & Lubos Pastor, 2023. "Fifty Shades of QE: Robust Evidence," Working and Discussion Papers WP 4/2023, Research Department, National Bank of Slovakia.
    4. Victor Echevarria-Icaza & Simón Sosvilla-Rivero, 2017. "Yields on sovereign debt, fragmentation and monetary policy transmission in the euro area: A GVAR approach," Working Papers del Instituto Complutense de Estudios Internacionales 1703, Universidad Complutense de Madrid, Instituto Complutense de Estudios Internacionales.
    5. Vergote, Olivier & Sugo, Tomohiro, 2020. "Who takes the ECB’s targeted funding?," Working Paper Series 2439, European Central Bank.
    6. Alan S. Blinder & Michael Ehrmann & Jakob de Haan & David-Jan Jansen, 2016. "Necessity as the Mother of Invention: Monetary Policy after the Crisis," NBER Working Papers 22735, National Bureau of Economic Research, Inc.
    7. Joseph E. Gagnon, 2016. "Quantitative Easing: An Underappreciated Success," Policy Briefs PB16-4, Peterson Institute for International Economics.
    8. Forbes, Kristin & Reinhardt, Dennis & Wieladek, Tomasz, 2016. "The spillovers, interactions, and (un)intended consequences of monetary and regulatory policies," Discussion Papers 44, Monetary Policy Committee Unit, Bank of England.
    9. Ugo Panizza & Charles Wyplosz, 2018. "The Folk Theorem of Decreasing Effectiveness of Monetary Policy: What Do the Data Say?," Russian Journal of Money and Finance, Bank of Russia, vol. 77(1), pages 71-107, March.
    10. Abeer Reza & Eric Santor & Lena Suchanek, 2015. "Quantitative Easing as a Policy Tool Under the Effective Lower Bound," Discussion Papers 15-14, Bank of Canada.
    11. Domenico Lombardi & Pierre Siklos & Samantha St. Amand, 2018. "A Survey Of The International Evidence And Lessons Learned About Unconventional Monetary Policies: Is A ‘New Normal’ In Our Future?," Journal of Economic Surveys, Wiley Blackwell, vol. 32(5), pages 1229-1256, December.
    12. Sarah Drought & Roger Perry & Adam Richardson, 2018. "Aspects of implementing unconventional monetary policy in New Zealand," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 81, pages 1-22, May.
    13. Shogbuyi, Abiodun & Steeley, James M., 2017. "The effect of quantitative easing on the variance and covariance of the UK and US equity markets," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 281-291.
    14. Simone Giansante & Mahmoud Fatouh & Steven Ongena, 2019. "Does Quantitative Easing Boost Bank Lending to the Real Economy or Cause Other Bank Asset Reallocation? The Case of the UK," Swiss Finance Institute Research Paper Series 19-72, Swiss Finance Institute.
    15. Papadamou, Stephanos & Kyriazis, Νikolaos A. & Tzeremes, Panayiotis G., 2019. "Unconventional monetary policy effects on output and inflation: A meta-analysis," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 295-305.
    16. Dinara Khamitovna GALLYAMOVA & Aidar Il'darovich MIFTAKHOV, 2017. "Boosting The Autonomy Of Regional Banking Systems As A Driver Of Economic Development: The Case Of Russia," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(2), pages 55-68, December.
    17. Tetsuji Okazaki & Toshihiro Okubo & Eric Strobl, 2021. "The Bright and Dark Side of Financial Support from Local and Central Banks after a Natural Disaster: Evidence from the Great Kanto Earthquake, 1923 Japan," CIGS Working Paper Series 21-001E, The Canon Institute for Global Studies.
    18. José Dorich & Nicholas Labelle St‐Pierre & Vadym Lepetyuk & Rhys R. Mendes, 2018. "Could a higher inflation target enhance macroeconomic stability?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 1029-1055, August.
    19. Grahame Johnson & Sharon Kozicki & Romanos Priftis & Lena Suchanek & Jonathan Witmer & Jing Yang, 2020. "Implementation and Effectiveness of Extended Monetary Policy Tools: Lessons from the Literature," Discussion Papers 2020-16, Bank of Canada.
    20. Miller, Sam & Wanengkirtyo, Boromeus, 2020. "Liquidity and monetary transmission: a quasi-experimental approach," Bank of England working papers 891, Bank of England.
    21. Harimohan, Rashmi & McLeay, Michael & Young, Garry, 2016. "Pass-through of bank funding costs to lending and deposit rates: lessons from the financial crisis," Bank of England working papers 590, Bank of England.

  34. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2015. "Structural Analysis with Multivariate Autoregressive Index Models," CEPR Discussion Papers 10801, C.E.P.R. Discussion Papers.

    Cited by:

    1. Monica Billio & Roberto Casarin & Sylvia Kaufmann & Matteo Iacopini, 2018. "Bayesian Dynamic Tensor Regression," Working Papers 2018:13, Department of Economics, University of Venice "Ca' Foscari".
    2. Mike G. Tsionas, 2016. "Alternative Bayesian compression in Vector Autoregressions and related models," Working Papers 216, Bank of Greece.
    3. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    4. Gianluca Cubadda & Marco Mazzali, 2024. "The vector error correction index model: representation, estimation and identification," The Econometrics Journal, Royal Economic Society, vol. 27(1), pages 126-150.
    5. Alain Hecq & Ivan Ricardo & Ines Wilms, 2024. "Reduced-Rank Matrix Autoregressive Models: A Medium $N$ Approach," Papers 2407.07973, arXiv.org.
    6. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    7. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    8. Mike G. Tsionas, 2016. "Alternatives to large VAR, VARMA and multivariate stochastic volatility models," Working Papers 217, Bank of Greece.
    9. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    10. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    11. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    12. Gianluca Cubadda & Barbara Guardabascio, 2017. "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model," CEIS Research Paper 397, Tor Vergata University, CEIS, revised 13 Jul 2018.
    13. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    14. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.

  35. Camba-Méndez, Gonzalo & Kapetanios, George & Papailias, Fotis & Weale, Martin R., 2015. "An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for euro area economies," Working Paper Series 1773, European Central Bank.

    Cited by:

    1. Klaus Abberger & Michael Graff & Oliver Müller & Jan-Egbert Sturm, 2022. "Composite global indicators from survey data: the Global Economic Barometers," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(3), pages 917-945, August.
    2. George Kapetanios & Fotis Papailias, 2021. "UK Economic Conditions during the Pandemic: Assessing the Economy using ONS Faster Indicators," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2021-10, Economic Statistics Centre of Excellence (ESCoE).

  36. Fawcett, Nicholas & Kapetanios, George & Mitchell, James & Price, Simon, 2014. "Generalised density forecast combinations," Bank of England working papers 492, Bank of England.

    Cited by:

    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2016. "Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 15-084/III, Tinbergen Institute, revised 03 Jul 2017.
    2. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    3. Roberto Casarin, 2014. "A Note on Tractable State-Space Model for Symmetric Positive-Definite Matrices," Working Papers 2014:23, Department of Economics, University of Venice "Ca' Foscari".
    4. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
    5. Pauwels, Laurent & Radchenko, Peter & Vasnev, Andrey, 2019. "Higher Moment Constraints for Predictive Density Combinations," Working Papers BAWP-2019-01, University of Sydney Business School, Discipline of Business Analytics.
    6. Emilio Zanetti Chini, 2018. "Forecaster’s utility and forecasts coherence," CREATES Research Papers 2018-01, Department of Economics and Business Economics, Aarhus University.
    7. Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Andres Ramirez Hassan, 2020. "Optimal probabilistic forecasts: When do they work?," Monash Econometrics and Business Statistics Working Papers 33/20, Monash University, Department of Econometrics and Business Statistics.
    8. Florian Eckert & Rob J Hyndman & Anastasios Panagiotelis, 2019. "Forecasting Swiss Exports Using Bayesian Forecast Reconciliation," Monash Econometrics and Business Statistics Working Papers 14/19, Monash University, Department of Econometrics and Business Statistics.
    9. Deak, S. & Levine, P. & Mirza, A. & Pearlman, J., 2019. "Designing Robust Monetary Policy Using Prediction Pools," Working Papers 19/11, Department of Economics, City University London.
    10. Roberto Casarin & Fabrizio Leisen & German Molina & Enrique Ter Horst, 2014. "A Bayesian Beta Markov Random Field calibration of the term structure of implied risk neutral densities," Working Papers 2014:22, Department of Economics, University of Venice "Ca' Foscari".
    11. Davide Pettenuzzo & Francesco Ravazzolo, 2015. "Optimal Portfolio Choice under Decision-Based Model Combinations," Working Papers No 9/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    12. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2019. "Does a financial accelerator improve forecasts during financial crises? Evidence from Japan with prediction-pooling methods," Journal of Asian Economics, Elsevier, vol. 60(C), pages 45-68.
    13. Ruben Loaiza‐Maya & Gael M. Martin & David T. Frazier, 2021. "Focused Bayesian prediction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 517-543, August.
    14. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    15. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2019. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Working Paper 2019/2, Norges Bank.
    16. Chen, Yi-Ting & Liu, Chu-An, 2023. "Model averaging for asymptotically optimal combined forecasts," Journal of Econometrics, Elsevier, vol. 235(2), pages 592-607.
    17. Francis X. Diebold & Minchul Shin & Boyuan Zhang, 2021. "On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone In?ation and Real Interest Rates," PIER Working Paper Archive 21-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    18. Marco Del Negro & Raiden B. Hasegawa & Frank Schorfheide, 2014. "Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance," PIER Working Paper Archive 14-034, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    19. Knüppel, Malte & Krüger, Fabian, 2017. "Forecast Uncertainty, Disagreement, and Linear Pools of Density Forecasts," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168294, Verein für Socialpolitik / German Economic Association.
    20. Berrisch, Jonathan & Ziel, Florian, 2023. "CRPS learning," Journal of Econometrics, Elsevier, vol. 237(2).
    21. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    22. Kenichiro McAlinn & Kosaku Takanashi, 2019. "Mean-shift least squares model averaging," Papers 1912.01194, arXiv.org.
    23. Peter McAdam & Anders Warne, 2024. "Density forecast combinations: The real‐time dimension," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1153-1172, August.
    24. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    25. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    26. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    27. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2015. "Bayesian nonparametric calibration and combination of predictive distributions," Working Paper 2015/03, Norges Bank.
    28. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    29. Fabio Busetti, 2017. "Quantile Aggregation of Density Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 495-512, August.
    30. Tony Chernis & Gary Koop & Emily Tallman & Mike West, 2024. "Decision synthesis in monetary policy," Papers 2406.03321, arXiv.org.
    31. Lan Bai & Xiafei Li & Yu Wei & Guiwu Wei, 2022. "Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3694-3712, July.
    32. Stavroula P. Fameliti & Vasiliki D. Skintzi, 2020. "Predictive ability and economic gains from volatility forecast combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 200-219, March.
    33. Jin, Xin & Maheu, John M. & Yang, Qiao, 2022. "Infinite Markov pooling of predictive distributions," Journal of Econometrics, Elsevier, vol. 228(2), pages 302-321.
    34. Taylor, James W. & Jeon, Jooyoung, 2018. "Probabilistic forecasting of wave height for offshore wind turbine maintenance," European Journal of Operational Research, Elsevier, vol. 267(3), pages 877-890.
    35. Roberto Casarin & Giulia Mantoan & Francesco Ravazzolo, 2016. "Bayesian Calibration of Generalized Pools of Predictive Distributions," Econometrics, MDPI, vol. 4(1), pages 1-24, March.
    36. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    37. Malte Knüppel & Fabian Krüger, 2022. "Forecast uncertainty, disagreement, and the linear pool," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 23-41, January.
    38. McAlinn, Kenichiro & West, Mike, 2019. "Dynamic Bayesian predictive synthesis in time series forecasting," Journal of Econometrics, Elsevier, vol. 210(1), pages 155-169.
    39. K=osaku Takanashi & Kenichiro McAlinn, 2019. "Equivariant online predictions of non-stationary time series," Papers 1911.08662, arXiv.org, revised Jun 2023.
    40. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.

  37. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.

    Cited by:

    1. Stelios D. Bekiros & Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2016. "Dealing with Financial Instability under a DSGE modeling approach with Banking Intermediation: a predictability analysis versus TVP-VARs," Open Access publications 10197/7323, School of Economics, University College Dublin.
    2. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2019. "Forecasting with instabilities: an application to DSGE models with financial frictions," Temi di discussione (Economic working papers) 1234, Bank of Italy, Economic Research and International Relations Area.
    3. Tryphonides, Andreas, 2018. "Learning from Errors: The case of monetary and fiscal policy regimes," IRTG 1792 Discussion Papers 2018-022, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Efrem Castelnuovo & Giovanni Pellegrino, 2018. "Uncertainty-dependent Effects of Monetary Policy Shocks: A New Keynesian Interpretation," Melbourne Institute Working Paper Series wp2018n02, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    5. Michael Wickens, 2014. "How did we get to where we are now? Reflections on 50 years of macroeconomic and financial econometrics," Discussion Papers 14/17, Department of Economics, University of York.
    6. Bekiros, Stelios D.; Cardani, Roberta; Paccagnini, Alessia; Villa, Stefania, 2015. "Dealing with Financial Instability under a DSGE modeling approach with Banking Intermediation: a forecastability analysis versus TVP-VARs," Economics Working Papers ECO2015/04, European University Institute.
    7. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.
    8. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2015. "Forecasting in a DSGE Model with Banking Intermediation: Evidence from the US," Working Papers 292, University of Milano-Bicocca, Department of Economics, revised Feb 2015.
    9. Michael Wickens, 2015. "How Did We Get to Where We Are Now? Reflections on 50 Years of Macroeconomic and Financial Econometrics," Manchester School, University of Manchester, vol. 83, pages 60-82, December.
    10. Ulrich Gunter, 2019. "Estimating and forecasting with a two-country DSGE model of the Euro area and the USA: the merits of diverging interest-rate rules," Empirical Economics, Springer, vol. 56(4), pages 1283-1323, April.

  38. George Kapetanios & Michael Neumann & George Skiadopoulos, 2014. "Jumps in Option Prices and Their Determinants: Real-time Evidence from the E-mini S&P 500 Option Market," Working Papers 730, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Richard Mawulawoe Ahadzie & Nagaratnam Jeyasreedharan, 2024. "Higher‐order moments and asset pricing in the Australian stock market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 75-128, March.
    2. Kuo-Shing Chen & Yu-Chuan Huang, 2021. "Detecting Jump Risk and Jump-Diffusion Model for Bitcoin Options Pricing and Hedging," Mathematics, MDPI, vol. 9(20), pages 1-24, October.
    3. Serdengecti, Suleyman & Sensoy, Ahmet & Nguyen, Duc Khuong, 2020. "Dynamics of Return and Liquidity (Co)Jumps in Emerging Foreign Exchange Markets," MPRA Paper 105162, University Library of Munich, Germany, revised Jan 2021.
    4. Liu, Wenwen & Zhang, Chang & Qiao, Gaoxiu & Xu, Lei, 2022. "Impact of network investor sentiment and news arrival on jumps," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    5. Aysan, Ahmet Faruk & Caporin, Massimiliano & Cepni, Oguzhan, 2024. "Not all words are equal: Sentiment and jumps in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).

  39. Giraitis, Liudas & Kapetanios, George & Price, Simon, 2014. "Adaptive forecasting in the presence of recent and ongoing structural change," Bank of England working papers 490, Bank of England.

    Cited by:

    1. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    2. Hirano, Keisuke & Wright, Jonathan H., 2022. "Analyzing cross-validation for forecasting with structural instability," Journal of Econometrics, Elsevier, vol. 226(1), pages 139-154.
    3. Wang, Yudong & Hao, Xianfeng & Wu, Chongfeng, 2021. "Forecasting stock returns: A time-dependent weighted least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
    4. Duncan, Roberto & Martínez-García, Enrique, 2019. "New perspectives on forecasting inflation in emerging market economies: An empirical assessment," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1008-1031.
    5. Atsushi Inoue, 2015. "Comment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 9-11, January.
    6. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    7. Marianna Riggi & Fabrizio Venditti, 2014. "Surprise! Euro area inflation has fallen," Questioni di Economia e Finanza (Occasional Papers) 237, Bank of Italy, Economic Research and International Relations Area.
    8. Papantonis Ioannis & Rompolis Leonidas S. & Tzavalis Elias & Agapitos Orestis, 2023. "Augmenting the Realized-GARCH: the role of signed-jumps, attenuation-biases and long-memory effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(2), pages 171-198, April.
    9. Luca Nocciola, "undated". "Finite sample forecast properties and window length under breaks in cointegrated systems," Discussion Papers 19/07, University of Nottingham, Granger Centre for Time Series Econometrics.
    10. Stephen G. Hall & George S. Tavlas & Yongli Wang & Deborah Gefang, 2024. "Inflation forecasting with rolling windows: An appraisal," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 827-851, July.
    11. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    12. Jari Hännikäinen, 2016. "Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks," Working Papers 1692, Tampere University, Faculty of Management and Business, Economics.
    13. Fabio Busetti & Pietro Cova & Antonio Maria Conti & Filippo Scoccianti & Libero Monteforte & Giordano Zevi & Valentina Aprigliano & Andrea Gerali & Alberto Locarno & Alessandro Notarpietro & Massimili, 2014. "The effects of the crisis on production potential and household spending in Italy," Workshop and Conferences 18, Bank of Italy, Economic Research and International Relations Area.
    14. Luca Nocciola, 2022. "Finite Sample Forecast Properties and Window Length Under Breaks in Cointegrated Systems," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 167-196, Emerald Group Publishing Limited.
    15. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    16. Philip Hans Franses & Eva Janssens, 2018. "This Time It Is Different! Or Not? Discounting Past Data When Predicting The Future," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-34, June.
    17. Pablo Guerrón-Quintana & Molin Zhong, 2017. "Macroeconomic Forecasting in Times of Crises," Finance and Economics Discussion Series 2017-018, Board of Governors of the Federal Reserve System (U.S.).
    18. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2014. "Have Standard VARs Remained Stable since the Crisis?," Working Papers (Old Series) 1411, Federal Reserve Bank of Cleveland.
    19. Yongchen Zhao, 2015. "Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms," Working Papers 2015-04, Towson University, Department of Economics, revised Mar 2020.
    20. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.
    21. Raffaella Giacomini & Barbara Rossi, 2014. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    22. Mardi Dungey & Jan P.A.M. Jacobs & Jing Tian, 2017. "Forecasting output gaps in the G-7 countries: the role of correlated innovations and structural breaks," Applied Economics, Taylor & Francis Journals, vol. 49(45), pages 4554-4566, September.
    23. Kapetanios, G & Price, SG & Young, G, 2017. "A UK financial conditions index using targeted data reduction: forecasting and structural identification," Essex Finance Centre Working Papers 20328, University of Essex, Essex Business School.
    24. Dendramis, Y. & Tzavalis, E. & Adraktas, G., 2018. "Credit risk modelling under recessionary and financially distressed conditions," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 160-175.
    25. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
    26. Khowaja, Kainat & Saef, Danial & Sizov, Sergej & Härdle, Wolfgang Karl, 2020. "Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition," IRTG 1792 Discussion Papers 2020-026, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    27. Ana Beatriz Galvão & Liudas Giraitis & George Kapetanios & Katerina Petrova, 2015. "A Bayesian Local Likelihood Method for Modelling Parameter Time Variation in DSGE Models," Working Papers 770, Queen Mary University of London, School of Economics and Finance.
    28. Franses, Ph.H.B.F. & Janssens, E., 2017. "This time it is different! Or not?," Econometric Institute Research Papers EI2017-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    29. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    30. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.
    31. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.
    32. Andre Jungmittag, 2016. "Combination of Forecasts across Estimation Windows: An Application to Air Travel Demand," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(4), pages 373-380, July.
    33. Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2021. "Smooth Robust Multi-Horizon Forecasts," Economics Papers 2021-W01, Economics Group, Nuffield College, University of Oxford.
    34. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    35. Dendramis, Y. & Tzavalis, E. & Varthalitis, P. & Athanasiou, E., 2020. "Predicting default risk under asymmetric binary link functions," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1039-1056.
    36. Giulia Bovini & Eliana Viviano, 2018. "The Italian "employment-rich" recovery: a closer look," Questioni di Economia e Finanza (Occasional Papers) 461, Bank of Italy, Economic Research and International Relations Area.
    37. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    38. Davide De Gaetano, 2018. "Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets," Mathematics, MDPI, vol. 6(3), pages 1-19, March.
    39. Guido Bulligan & Eliana Viviano, 2017. "Has the wage Phillips curve changed in the euro area?," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 6(1), pages 1-22, December.
    40. Rossi, Barbara & Inoue, Atsushi & Jin, Lu, 2014. "Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters," CEPR Discussion Papers 10168, C.E.P.R. Discussion Papers.
    41. Davide De Gaetano, 2017. "Forecasting With Garch Models Under Structural Breaks: An Approach Based On Combinations Across Estimation Windows," Departmental Working Papers of Economics - University 'Roma Tre' 0219, Department of Economics - University Roma Tre.
    42. Mariia Artemova & Francisco Blasques & Siem Jan Koopman & Zhaokun Zhang, 2021. "Forecasting in a changing world: from the great recession to the COVID-19 pandemic," Tinbergen Institute Discussion Papers 21-006/III, Tinbergen Institute.
    43. Zhang, Xingmin & Zhang, Shuai, 2021. "Optimal time-varying tail risk network with a rolling window approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    44. Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2022. "Smooth Robust Multi-Horizon Forecasts," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 143-165, Emerald Group Publishing Limited.
    45. Kley, Tobias & Preuss, Philip & Fryzlewicz, Piotr, 2019. "Predictive, finite-sample model choice for time series under stationarity and non-stationarity," LSE Research Online Documents on Economics 101748, London School of Economics and Political Science, LSE Library.

  40. Gustavo Fruet Dias & George Kapetanios, 2014. "Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets," CREATES Research Papers 2014-37, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Mustofa Usman & M. Komarudin & Munti Sarida & Wamiliana Wamiliana & Edwin Russel & Mahatma Kufepaksi & Iskandar Ali Alam & Faiz A.M. Elfaki, 2022. "Analysis of Some Variable Energy Companies by Using VAR(p)-GARCH(r,s) Model : Study From Energy Companies of Qatar over the Years 2015 2022," International Journal of Energy Economics and Policy, Econjournals, vol. 12(5), pages 178-191, September.
    2. Procasky, William J., 2021. "Price discovery in CDS and equity markets: Default risk-based heterogeneity in the systematic investment grade and high yield sectors," Journal of Financial Markets, Elsevier, vol. 54(C).
    3. Mustafa Gülerce & Gazanfer Ünal, 2017. "Forecasting Of Oil And Agricultural Commodity Prices: Varma Versus Arma," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 1-30, September.
    4. Mustafa Gülerce & Gazanfer Ünal, 2018. "Electricity price forecasting using multiple wavelet coherence method: Comparison of ARMA versus VARMA," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-20, March.
    5. Emrah Oral & Gazanfer Unal, 2017. "Co-movement of precious metals and forecasting using scale by scale wavelet transform," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-21, March.
    6. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.

  41. Kapetanios, George & Mumtaz, Haroon & Stevens, Ibrahim & Theodoridis, Konstantinos, 2012. "Assessing the economy-wide effects of quantitative easing," Bank of England working papers 443, Bank of England.

    Cited by:

    1. Ellington, Michael & Milas, Costas, 2021. "On the economic impact of aggregate liquidity shocks: The case of the UK," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 737-752.
    2. Urbschat, Florian & Watzka, Sebastian, 2020. "Quantitative easing in the Euro Area – An event study approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 14-36.
    3. Butt, Nick & Churm, Rohan & McMahon, Michael & Morotz, Arpad & Schanz, Jochen, 2015. "QE and the Bank Lending Channel in the United Kingdom," CAGE Online Working Paper Series 244, Competitive Advantage in the Global Economy (CAGE).
    4. Haroon Mumtaz & Angeliki Theophilopoulou, 2016. "The Impact of Monetary Policy on Inequality in the UK. An Empirical Analysis," Working Papers 783, Queen Mary University of London, School of Economics and Finance.
    5. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2019. "Money, Credit, Monetary Policy, and the Business Cycle in the Euro Area: What Has Changed Since the Crisis?," International Journal of Central Banking, International Journal of Central Banking, vol. 15(5), pages 137-173, December.
    6. Philip Bunn & Paul Mizen & Pawel Smietanka, 2018. "Growing pension deficits and the expenditure decisions of UK companies," Discussion Papers 2018/05, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    7. Fabo, Brian & Jančoková, Martina & Kempf, Elisabeth & Pástor, Ľuboš, 2021. "Fifty shades of QE: Comparing findings of central bankers and academics," Journal of Monetary Economics, Elsevier, vol. 120(C), pages 1-20.
    8. G.M. Gallo & D. Lacava & E. Otranto, 2020. "On Classifying the Effects of Policy Announcements on Volatility," Working Paper CRENoS 202008, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    9. Anastasios Evgenidis & Stephanos Papadamou, 2021. "The impact of unconventional monetary policy in the euro area. Structural and scenario analysis from a Bayesian VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5684-5703, October.
    10. Phil Molyneux & Rue Xie & John Thornton & Alessio Reghezza, 2017. "Did Negative Interest Rates Impact Bank Lending?," Working Papers 17002, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    11. Deng, Kaihua & Todd, Walker, 2016. "Is the US quantitative easing more effective than China's? A second thought," China Economic Review, Elsevier, vol. 38(C), pages 11-23.
    12. Weale, Martin & Wieladek, Tomasz, 2022. "Financial effects of QE and conventional monetary policy compared," Journal of International Money and Finance, Elsevier, vol. 127(C).
    13. Demetrio Lacava & Luca Scaffidi Domianello, 2021. "The Incidence of Spillover Effects during the Unconventional Monetary Policies Era," JRFM, MDPI, vol. 14(6), pages 1-18, May.
    14. Matthieu Darracq Paries, 2018. "Financial frictions and monetary policy conduct," Erudite Ph.D Dissertations, Erudite, number ph18-01 edited by Ferhat Mihoubi.
    15. M. Hashem Pesaran & Ron P. Smith, 2012. "Counterfactual Analysis in Macroeconometrics: An Empirical Investigation into the Effects of Quantitative Easing," CESifo Working Paper Series 3879, CESifo.
    16. Markus Heckel & Kiyohiko G. Nishimura, 2022. "Unconventional Monetary Policy through Open Market Operations: A Principal Component Analysis," Asian Economic Papers, MIT Press, vol. 21(1), pages 1-28, Winter/Sp.
    17. Teng, Bin & Wang, Sicong & Shi, Yufeng & Sun, Yunchuan & Wang, Wei & Hu, Wentao & Shi, Chaojun, 2022. "Economic recovery forecasts under impacts of COVID-19," Economic Modelling, Elsevier, vol. 110(C).
    18. Fructuoso Borrallo & Ignacio Hernando & Javier Vallés, 2016. "The effects of us unconventional monetary policies in Latin America," Working Papers 1606, Banco de España.
    19. Wickens, Michael R. & Polito, Vito, 2013. "Is the UK triple-A?," CEPR Discussion Papers 9378, C.E.P.R. Discussion Papers.
    20. Christophe Blot & Jérôme Creel & Paul Hubert & Fabien Labondance, 2015. "The QE experience: Worth a try?," Post-Print hal-03568216, HAL.
    21. Baumeister, Christiane & Hamilton, James, 2020. "Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions," CEPR Discussion Papers 14271, C.E.P.R. Discussion Papers.
    22. Ellington, Michael & Milas, Costas, 2019. "Global liquidity, money growth and UK inflation," Journal of Financial Stability, Elsevier, vol. 42(C), pages 67-74.
    23. Brian Fabo & Martina Jancokova & Elisabeth Kempf & Lubos Pastor, 2023. "Fifty Shades of QE: Robust Evidence," Working and Discussion Papers WP 4/2023, Research Department, National Bank of Slovakia.
    24. Nikolay Hristov & Oliver Hülsewig & Johann Scharler, 2020. "Unconventional Monetary Policy Shocks in the Euro Area and the Sovereign-Bank Nexus," CESifo Working Paper Series 8178, CESifo.
    25. Alessio Anzuini, 2020. "The non-linear effects of the Fed's asset purchases," Temi di discussione (Economic working papers) 1280, Bank of Italy, Economic Research and International Relations Area.
    26. Chen, Haixia & Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick, 2023. "UK Monetary Policy in An Estimated DSGE Model with State-Dependent Price and Wage Contracts," Cardiff Economics Working Papers E2023/22, Cardiff University, Cardiff Business School, Economics Section.
    27. Michael Hachula & Michele Piffer & Malte Rieth, 2016. "Unconventional Monetary Policy, Fiscal Side Effects and Euro Area (Im)balances," Discussion Papers of DIW Berlin 1596, DIW Berlin, German Institute for Economic Research.
    28. Huber, Florian & Punzi, Maria Teresa, 2020. "International Housing Markets, Unconventional Monetary Policy, And The Zero Lower Bound," Macroeconomic Dynamics, Cambridge University Press, vol. 24(4), pages 774-806, June.
    29. Anna Sznajderska & Alfred A. Haug, 2023. "Bayesian VARs of the U.S. economy before and during the pandemic," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 211-236, June.
    30. Feld, Lars P. & Wieland, Volker, 2020. "The German Federal Constitutional Court ruling and the European Central Bank's strategy," IMFS Working Paper Series 145, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    31. Ursel Baumann & David Lodge & Mirela S. Miescu, 2024. "Global growth on life support? The contributions of fiscal and monetary policy since the global financial crisis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 76-90, January.
    32. David Cobham & Yue Kang, 2012. "Financial Crisis And Quantitative Easing: Can Broad Money Tell Us Anything?," Manchester School, University of Manchester, vol. 80, pages 54-76, September.
    33. Kühl, Michael, 2014. "Mitigating financial stress in a bank-financed economy: Equity injections into banks or purchases of assets?," Discussion Papers 19/2014, Deutsche Bundesbank.
    34. Böhl, Gregor & Goy, Gavin & Strobel, Felix, 2020. "A structural investigation of quantitative easing," IMFS Working Paper Series 142, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    35. Gan‐Ochir Doojav & Davaasukh Damdinjav, 2023. "The macroeconomic effects of unconventional monetary policies in a commodity‐exporting economy: Evidence from Mongolia," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4627-4654, October.
    36. Massimo Guidolin & Alexei G. Orlov & Manuela Pedio, 2014. "Understanding the Impact of Monetary Policy Shocks on the Corporate Bond Market in Good and Bad Times: A Markov Switching Model," BAFFI CAREFIN Working Papers 1623, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    37. James Morley, 2016. "Macro-Finance Linkages," Journal of Economic Surveys, Wiley Blackwell, vol. 30(4), pages 698-711, September.
    38. Churm, Rohan & Joyce, Mike & Kapetanios, George & Theodoridis, Konstantinos, 2015. "Unconventional monetary policies and the macroeconomy: the impact of the United Kingdom's QE2 and Funding for Lending Scheme," Bank of England working papers 542, Bank of England.
    39. Harrison, Richard, 2017. "Optimal quantitative easing," Bank of England working papers 678, Bank of England.
    40. Albertazzi, Ugo & Becker, Bo & Boucinha, Miguel, 2018. "Portfolio rebalancing and the transmission of large-scale asset programmes: evidence from the euro area," Working Paper Series 2125, European Central Bank.
    41. Falagiarda, Matteo & Reitz, Stefan, 2013. "Announcements of ECB unconventional programs: Implications for the sovereign risk of Italy," Kiel Working Papers 1866, Kiel Institute for the World Economy (IfW Kiel).
    42. Zaghini, Andrea, 2019. "The CSPP at work: Yield heterogeneity and the portfolio rebalancing channel," Journal of Corporate Finance, Elsevier, vol. 56(C), pages 282-297.
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    182. Breitenlechner, Max & Gründler, Daniel & Scharler, Johann, 2021. "Unconventional monetary policy announcements and information shocks in the U.S," Journal of Macroeconomics, Elsevier, vol. 67(C).
    183. Kaminska, Iryna & Mumtaz, Haroon, 2022. "Monetary policy transmission during QE times: role of expectations and term premia channels," Bank of England working papers 978, Bank of England, revised 31 Aug 2022.
    184. Jorge Alberto Fornero & Roque Esteban Montero & Andrés J. Yany, 2017. "Reassessing the Effects of Foreign Monetary Policy on Output: New Evidence from Structural and Agnostic Identification Procedures," Investigación Conjunta-Joint Research, in: Ángel Estrada García & Alberto Ortiz Bolaños (ed.), International Spillovers of Monetary Policy, edition 1, chapter 3, pages 31-72, Centro de Estudios Monetarios Latinoamericanos, CEMLA.
    185. Steeley, James M., 2015. "The side effects of quantitative easing: Evidence from the UK bond market," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 303-336.
    186. LUPU, Radu & CALIN, Adrian Cantemir, 2014. "Co-Movements Of Regime Shifts In Gbp Currency Pairs Around Boe Quantitative Easing Announcements," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 18(3), pages 89-101.

  42. James Mitchell & George Kapetanios & Yongcheol Shin, 2012. "A Nonlinear Panel Data Model of Cross-Sectional Dependence," Discussion Papers in Economics 12/01, Division of Economics, School of Business, University of Leicester.

    Cited by:

    1. Rafiq, Shuddhasattwa & Salim, Ruhul & Nielsen, Ingrid, 2016. "Urbanization, openness, emissions, and energy intensity: A study of increasingly urbanized emerging economies," Energy Economics, Elsevier, vol. 56(C), pages 20-28.
    2. Orea, Luis & Álvarez, Inmaculada C., 2019. "A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms," Journal of Econometrics, Elsevier, vol. 213(2), pages 556-577.
    3. Eunju Hwang & Dong Wan Shin, 2017. "Stationary bootstrapping for common mean change detection in cross-sectionally dependent panels," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(6), pages 767-787, November.
    4. Frohm, Erik & Gunnella, Vanessa, 2017. "Sectoral interlinkages in global value chains: spillovers and network effects," Working Paper Series 2064, European Central Bank.
    5. Saptorshee Kanto Chakraborty & Massimiliano Mazzanti, 2021. "Renewable Electricity and Economic Growth relationship in the long run: panel data econometric evidence from the OECD," SEEDS Working Papers 0421, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Apr 2021.
    6. Sinem Hacıoğlu Hoke & George Kapetanios, 2021. "Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 125-150, January.
    7. Hacioglu Hoke, Sinem & Kapetanios, George, 2017. "Common correlated effect cross-sectional dependence corrections for non-linear conditional mean panel models," Bank of England working papers 683, Bank of England.
    8. Shuddhasattwa Rafiq & Ruhul Salim & Pasquale M Sgro, 2018. "Energy, unemployment and trade," Applied Economics, Taylor & Francis Journals, vol. 50(47), pages 5122-5134, October.
    9. Yang, Qin & Du, Qiang & Razzaq, Asif & Shang, Yunfeng, 2022. "How volatility in green financing, clean energy, and green economic practices derive sustainable performance through ESG indicators? A sectoral study of G7 countries," Resources Policy, Elsevier, vol. 75(C).
    10. Efthymios G. Tsionas & Panayotis G. Michaelides, 2016. "A Spatial Stochastic Frontier Model with Spillovers: Evidence for Italian Regions," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(3), pages 243-257, July.
    11. N. Fawcett & G. Kapetanios & J. Mitchell & S. Price, 2014. "Generalised Density Forecast Combinations," CAMA Working Papers 2014-24, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Gunnella, Vanessa & Al-Haschimi, Alexander & Benkovskis, Konstantins & Chiacchio, Francesco & de Soyres, François & Di Lupidio, Benedetta & Fidora, Michael & Franco-Bedoya, Sebastian & Frohm, Erik & G, 2019. "The impact of global value chains on the euro area economy," Occasional Paper Series 221, European Central Bank.
    13. Erik Frohm & Vanessa Gunnella, 2021. "Spillovers in global production networks," Review of International Economics, Wiley Blackwell, vol. 29(3), pages 663-680, August.
    14. Christis Katsouris, 2023. "Estimation and Inference in Threshold Predictive Regression Models with Locally Explosive Regressors," Papers 2305.00860, arXiv.org, revised May 2023.
    15. Tong, Howell, 2015. "Threshold models in time series analysis—Some reflections," Journal of Econometrics, Elsevier, vol. 189(2), pages 485-491.
    16. Rafiq, Shuddhasattwa & Salim, Ruhul & Apergis, Nicholas, 2016. "Agriculture, trade openness and emissions: an empirical analysis and policy options," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 60(2), April.

  43. Bailey, N. & Kapetanios, G. & Pesaran, M. H., 2012. "Exponent of Cross-sectional Dependence: Estimation and Inference," Cambridge Working Papers in Economics 1206, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Ambrogio Cesa-Bianchi & M. Hashem Pesaran & Alessandro Rebucci, 2014. "Uncertainty and Economic Activity: A Global Perspective," CESifo Working Paper Series 4736, CESifo.
    2. Li, Raymond & Woo, Chi-Keung & Cox, Kevin, 2021. "How price-responsive is residential retail electricity demand in the US?," Energy, Elsevier, vol. 232(C).
    3. Nuñez, Hector M. & Otero, Jesús, 2015. "Integration in Gasoline and Ethanol Markets in Brazil over Time and Space under the Flex-fuel Technology," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 204306, Agricultural and Applied Economics Association.
    4. Ca' Zorzi, Michele & Dieppe, Alistair & Chudik, Alexander, 2012. "The perils of aggregating foreign variables in panel data models," Working Paper Series 1444, European Central Bank.
    5. Paulo M.M. Rodrigues & Matei Demetrescu, 2022. "Cross-Sectional Error Dependence in Panel Quantile Regressions," Working Papers w202213, Banco de Portugal, Economics and Research Department.
    6. Demetrescu, Matei & Hosseinkouchack, Mehdi & Rodrigues, Paulo M. M., 2023. "Tests of no cross-sectional error dependence in panel quantile regressions," Ruhr Economic Papers 1041, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    7. Ambrogio Cesa-Bianchi & M. Hashem Pesaran & Alessandro Rebucci, 2018. "Uncertainty and Economic Activity: A Multi-Country Perspective," NBER Working Papers 24325, National Bureau of Economic Research, Inc.
    8. Bakas, Dimitrios & Mendieta-Muñoz, Ivan, 2020. "Financial crises and economic recovery: Cross-country heterogeneity and cross-sectional dependence," Economics Letters, Elsevier, vol. 195(C).
    9. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2018. "Exponent of cross-sectional dependence for residuals," Monash Econometrics and Business Statistics Working Papers 13/18, Monash University, Department of Econometrics and Business Statistics.
    10. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross-Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Post-Print hal-03539371, HAL.
    11. Kyriakos Drivas & Claire Economidou & Konstantinos N. Konstantakis & Panayotis G. Michaelides, 2022. "Technological Leaders, Laggards and Spillovers: A Network GVAR Analysis," Open Economies Review, Springer, vol. 33(2), pages 231-269, April.
    12. M. Hashem Pesaran & Cynthia Fan Yang, 2019. "Estimation and inference in spatial models with dominant units," CESifo Working Paper Series 7563, CESifo.
    13. Masako Ikegami & Zijian Wang, 2024. "Does energy technology R&D save energy in OECD countries?," Economic Change and Restructuring, Springer, vol. 57(2), pages 1-22, April.
    14. Candelon, Bertrand & Luisi , Angelo & Roccazzella, Francesco, 2021. "Fragmentation in the European Monetary Union: Is it really over?," LIDAM Discussion Papers LFIN 2021015, Université catholique de Louvain, Louvain Finance (LFIN).
    15. Herrera Gomez Marcos & Fernández Pablo, 2023. "Regresiones SUR Espaciales. Análisis espacio-temporal del empleo sectorial en Argentina," Asociación Argentina de Economía Política: Working Papers 4660, Asociación Argentina de Economía Política.
    16. Rama Cont & Lakshithe Wagalath, 2012. "Fire Sales Forensics: Measuring Endogenous Risk," Working Papers hal-00697224, HAL.
    17. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
    18. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2019. "Estimation and inference for spatial models with heterogeneous coefficients: an application to U.S. house prices," CESifo Working Paper Series 7542, CESifo.
    19. M. Hashem Pesaran & Qiankun Zhou, 2015. "To Pool or not to Pool: Revisited," CESifo Working Paper Series 5410, CESifo.
    20. Carlos Vladimir Rodríguez-Caballero, 2016. "Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure," CREATES Research Papers 2016-31, Department of Economics and Business Economics, Aarhus University.
    21. Saptorshee Kanto Chakraborty & Massimiliano Mazzanti, 2021. "Renewable Electricity and Economic Growth relationship in the long run: panel data econometric evidence from the OECD," SEEDS Working Papers 0421, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Apr 2021.
    22. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    23. Pesaran, M. H. & Yamagata, T., 2012. "Testing CAPM with a Large Number of Assets (Updated 28th March 2012)," Cambridge Working Papers in Economics 1210, Faculty of Economics, University of Cambridge.
    24. Cynthia Fan Yang, 2021. "Common factors and spatial dependence: an application to US house prices," Econometric Reviews, Taylor & Francis Journals, vol. 40(1), pages 14-50, January.
    25. Matthieu Bussiere & Alexander Chudik & Giulia Sestieri, 2012. "Modelling global trade flows: results from a GVAR model," Globalization Institute Working Papers 119, Federal Reserve Bank of Dallas.
    26. Pesaran, M. Hashem & Chudik, Alexander, 2011. "Aggregation in Large Dynamic Panels," IZA Discussion Papers 5478, Institute of Labor Economics (IZA).
    27. Wei Shi & Lung-fei Lee, 2018. "The effects of gun control on crimes: a spatial interactive fixed effects approach," Empirical Economics, Springer, vol. 55(1), pages 233-263, August.
    28. Floro, Danvee & van Roye, Björn, 2015. "Threshold Effects of Financial Stress on Monetary Policy Rules: A Panel Data Analysis," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112840, Verein für Socialpolitik / German Economic Association.
    29. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    30. Alexander Chudik & M. Hashem Pesaran, 2013. "Large panel data models with cross-sectional dependence: a survey," Globalization Institute Working Papers 153, Federal Reserve Bank of Dallas.
    31. Elhorst, J. Paul & Madre, Jean-Loup & Pirotte, Alain, 2020. "Car traffic, habit persistence, cross-sectional dependence, and spatial heterogeneity: New insights using French departmental data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 614-632.
    32. Capasso, Salvatore & D'Uva, Marcella & Fiorelli, Cristiana & Napolitano, Oreste, 2023. "Cross-border Italian sovereign risk transmission in EMU countries," Economic Modelling, Elsevier, vol. 126(C).
    33. Yoshimasa Uematsu & Takashi Yamagata, 2019. "Estimation of Weak Factor Models," ISER Discussion Paper 1053r, Institute of Social and Economic Research, Osaka University, revised Mar 2020.
    34. Pesaran, H. & Yang, Cynthia Fan, 2016. "Econometric Analysis of Production Networks with Dominant Units," Cambridge Working Papers in Economics 1678, Faculty of Economics, University of Cambridge.
    35. Philip Kerner & Torben Klarl & Tobias Wendler, 2021. "Green Technologies, Environmental Policy and Regional Growth," Bremen Papers on Economics & Innovation 2104, University of Bremen, Faculty of Business Studies and Economics.
    36. Chudik, A. & Pesaran, H. & Mohaddes, K., 2018. "Identifying Global and National Output and Fiscal Policy Shocks Using a GVAR," Cambridge Working Papers in Economics 1874, Faculty of Economics, University of Cambridge.
    37. Cem Ertur & Antonio Musolesi, 2014. "Dépendance individuelle forte et faible : une analyse en données de panel de la diffusion internationale de la technologie," Working Papers halshs-01015208, HAL.
    38. Manuela Fritz, 2022. "Wave after wave: determining the temporal lag in Covid-19 infections and deaths using spatial panel data from Germany," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-30, December.
    39. Musolesi, Antonio & Prete, Giada Andrea & Simioni, Michel, 2022. "Is infrastructure capital really productive? Non-parametric modeling and data-driven model selection in a cross-sectionally dependent panel framework," TSE Working Papers 22-1335, Toulouse School of Economics (TSE).
    40. Eberhardt, Markus & Vollrath, Dietrich, 2016. "The Role of Crop Type in Cross-Country Income Differences," CEPR Discussion Papers 11248, C.E.P.R. Discussion Papers.
    41. Gregori, Tullio & Tiwari, Aviral Kumar, 2020. "Do urbanization, income, and trade affect electricity consumption across Chinese provinces?," Energy Economics, Elsevier, vol. 89(C).
    42. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2014. "Multivariate variance ratio statistics," CeMMAP working papers 29/14, Institute for Fiscal Studies.
    43. Margaretic, Paula & Cifuentes, Rodrigo & Carreño, José Gabriel, 2021. "Banks’ interconnections and peer effects: Evidence from Chile," Research in International Business and Finance, Elsevier, vol. 58(C).
    44. Syed A. Basher & Josep Lluis Carrión-i-Silvestre, 2008. "Deconstructing Shocks and Persistence in OECD Real Exchange Rates," Working Papers XREAP2008-06, Xarxa de Referència en Economia Aplicada (XREAP), revised Jun 2008.
    45. Demetrescu, Matei & Leppin, Julian Sebastian & Reitz, Stefan, 2017. "Homogenous vs. heterogenous transition functions in smooth transition regressions: A LM-type test," Kiel Working Papers 2094, Kiel Institute for the World Economy (IfW Kiel).
    46. M. Hashem Pesaran & L. Vanessa Smith & Takashi Yamagata, 2008. "Panel Unit Root Tests in the Presence of a Multifactor Error Structure," CESifo Working Paper Series 2193, CESifo.
    47. Camilla Mastromarco & Laura Serlenga & Yongcheol Shin, 2023. "Regional Productivity Network in the EU," CESifo Working Paper Series 10404, CESifo.
    48. Markus Eberhardt & Andrea F. Presbitero, 2013. "This Time They’re Different: Heterogeneity and Nonlinearity in the Relationship between Debt and Growth," Discussion Papers 2013/10, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    49. Peter Backus & Thien Nguyen, 2021. "The Effect of the Sex Buyer Law on the Market for Sex, Sexual Health and Sexual Violence," Economics Discussion Paper Series 2106, Economics, The University of Manchester.
    50. Halleck Vega, Solmaria & Elhorst, J. Paul, 2016. "A regional unemployment model simultaneously accounting for serial dynamics, spatial dependence and common factors," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 85-95.
    51. Pot, Felix Johan & Koster, Sierdjan, 2022. "Small airports: Runways to regional economic growth?," Journal of Transport Geography, Elsevier, vol. 98(C).
    52. Diego-Ivan Ruge-Leiva, 2014. "International R&D spillovers and unobserved common shocks," Working Papers 08/14, Instituto Universitario de Análisis Económico y Social.
    53. Elhorst, J. Paul & Gross, Marco & Tereanu, Eugen, 2018. "Spillovers in space and time: where spatial econometrics and Global VAR models meet," Working Paper Series 2134, European Central Bank.
    54. M. Hashem Pesaran, 2015. "Testing Weak Cross-Sectional Dependence in Large Panels," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1089-1117, December.
    55. Gyimah, Daniel & Danso, Albert & Adu-Ameyaw, Emmanuel & Boateng, Agyenim, 2022. "Firm-level political risk and corporate leverage decisions," International Review of Financial Analysis, Elsevier, vol. 84(C).
    56. Kapetanios, George & Serlenga, Laura & Shin, Yongcheol, 2021. "Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 504-531.
    57. Jiti Gao & Guangming Pan & Yanrong Yang, 2016. "CEstimation of Structural Breaks in Large Panels with Cross-Sectional Dependence," Monash Econometrics and Business Statistics Working Papers 12/16, Monash University, Department of Econometrics and Business Statistics.
    58. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
    59. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2014. "Multivariate variance ratio statistics," CeMMAP working papers CWP29/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    60. Jia Chen Author-Name-First: Jia & Yongcheol Shin & Chaowen Zheng, 2023. "Dynamic Quantile Panel Data Models with Interactive Effects," Economics Discussion Papers em-dp2023-06, Department of Economics, University of Reading.
    61. George Kapetanios & M. Hashem Pesaran & Simon Reese, 2018. "A Residual-based Threshold Method for Detection of Units that are Too Big to Fail in Large Factor Models," CESifo Working Paper Series 7401, CESifo.
    62. Saman Banafti & Tae-Hwy Lee, 2023. "Inferential Theory for Granular Instrumental Variables in High Dimensions," Working Papers 202308, University of California at Riverside, Department of Economics.
    63. M Hashem Pesaran & Takashi Yamagata, 2012. "Testing CAPM with a Large Number of Assets," Discussion Papers 12/05, Department of Economics, University of York.
    64. Zhenhong Huang & Zhaoyuan Li & Jianfeng Yao, 2023. "Unified and robust Lagrange multiplier type tests for cross-sectional independence in large panel data models," Papers 2302.14387, arXiv.org.
    65. Mehdi Ben Jebli & Montassar Kahia, 2020. "The interdependence between CO2 emissions, economic growth, renewable and non-renewable energies, and service development: evidence from 65 countries," Climatic Change, Springer, vol. 162(2), pages 193-212, September.
    66. Chaisri Tarasawatpipat & Witthaya Mekhum, 2021. "Rethinking the Reasons of Greenhouse Gases Emission in ASEAN Countries: Finding Reasons in Urbanization, Industrialization and Population Growth," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 544-550.
    67. J. Paul Elhorst & Marco Gross & Eugen Tereanu, 2021. "Cross‐Sectional Dependence And Spillovers In Space And Time: Where Spatial Econometrics And Global Var Models Meet," Journal of Economic Surveys, Wiley Blackwell, vol. 35(1), pages 192-226, February.
    68. R. Golinelli & I. Mammi & A. Musolesi, 2018. "Parameter heterogeneity, persistence and cross-sectional dependence: new insights on fiscal policy reaction functions for the Euro area," Working Papers wp1120, Dipartimento Scienze Economiche, Universita' di Bologna.
    69. Deng, Qiu Shi & Alvarado, Rafael & Cuesta, Lizeth & Tillaguango, Brayan & Murshed, Muntasir & Rehman, Abdul & Işık, Cem & López-Sánchez, Michelle, 2022. "Asymmetric impacts of foreign direct investment inflows, financial development, and social globalization on environmental pollution," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 236-251.
    70. M. Hashem Pesaran & Ron P. Smith, 2021. "Factor Strengths, Pricing Errors, and Estimation of Risk Premia," CESifo Working Paper Series 8947, CESifo.
    71. Paula Margaretic & Sebastián Becerra, 2017. "Dispersed Information and Sovereign Risk Premia," Working Papers Central Bank of Chile 808, Central Bank of Chile.
    72. Bai, Jushan & Liao, Yuan, 2016. "Efficient estimation of approximate factor models via penalized maximum likelihood," Journal of Econometrics, Elsevier, vol. 191(1), pages 1-18.
    73. Michaelides, Panayotis G. & Tsionas, Efthymios G. & Konstantakis, Konstantinos N., 2018. "Debt Crisis in Europe (2001-2015): A Network General Equilibrium GVAR approach," MPRA Paper 89998, University Library of Munich, Germany.
    74. Arnoldo López-Marmolejo & Carlos Vladimir Rodríguez-Caballero & Daniel Ventosa-Santaulà ria, 2021. "Remittances at record highs in Latin America: Time to revisit the Dutch disease," Economics Bulletin, AccessEcon, vol. 41(3), pages 2133-2146.
    75. Alexander Chudik & M. Hashem Pesaran & Kamiar Mohaddes, 2020. "Identifying Global and National Output and Fiscal Policy Shocks Using a GVAR," Advances in Econometrics, in: Essays in Honor of Cheng Hsiao, volume 41, pages 143-189, Emerald Group Publishing Limited.
    76. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    77. Carlo Ciccarelli & Jean Paul Elhorst, 2016. "A Spatial Diffusion Model with Common Factors and an Application to Cigarette Consumption," CEIS Research Paper 381, Tor Vergata University, CEIS, revised 31 May 2016.
    78. Aleksy Kwilinski & Oleksii Lyulyov & Tetyana Pimonenko, 2024. "Energy Poverty and Democratic Values: A European Perspective," Energies, MDPI, vol. 17(12), pages 1-23, June.
    79. Rose Camille Vincent & Victor Osei Kwadwo, 2022. "Spatial interdependence and spillovers of fiscal grants in Benin: Static and dynamic diffusions," Post-Print hal-04055064, HAL.
    80. Romano Piras, 2017. "A long-run analysis of push and pull factors of internal migration in Italy. Estimation of a gravity model with human capital using homogeneous and heterogeneous approaches," Papers in Regional Science, Wiley Blackwell, vol. 96(3), pages 571-602, August.
    81. Chi-Young Choi & Alexander Chudik, 2017. "Geographic Inequality of Economic Well-being among U.S. Cities: Evidence from Micro Panel Data," Globalization Institute Working Papers 330, Federal Reserve Bank of Dallas.
    82. Chen, Jia & Shin, Yongcheol & Zheng, Chaowen, 2022. "Estimation and inference in heterogeneous spatial panels with a multifactor error structure," Journal of Econometrics, Elsevier, vol. 229(1), pages 55-79.
    83. Antonio Musolesi & Giada Andrea Prete & Michel Simioni, 2022. "Is infrastructure capital really productive? Non-parametric modeling and data-driven model selection in a cross-sectionally dependent panel framework," SEEDS Working Papers 0522, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Mar 2022.
    84. Jan Ditzen, 2021. "Estimating long run effects and the exponent of cross-sectional dependence: an update to xtdcce2," BEMPS - Bozen Economics & Management Paper Series BEMPS81, Faculty of Economics and Management at the Free University of Bozen.
    85. Alvarado, Rafael & Tillaguango, Brayan & Murshed, Muntasir & Ochoa-Moreno, Santiago & Rehman, Abdul & Işık, Cem & Alvarado-Espejo, Johana, 2022. "Impact of the informal economy on the ecological footprint: The role of urban concentration and globalization," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 750-767.
    86. Duran, Nicolas & Elhorst, J. Paul, 2017. "A Spatio-temporal-similarity and Common Factor Approach of Individual Housing Prices," Research Report 2018007-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    87. Schanne, Norbert, 2015. "A Global Vector Autoregression (GVAR) model for regional labour markets and its forecasting performance with leading indicators in Germany," IAB-Discussion Paper 201513, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    88. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2015. "An investigation into Multivariate Variance Ratio Statistics and their application to Stock Market Predictability," Cambridge Working Papers in Economics 1552, Faculty of Economics, University of Cambridge.
    89. Jibril, Halima & Chaudhuri, Kausik & Mohaddes, Kamiar, 2020. "Asymmetric oil prices and trade imbalances: Does the source of the oil shock matter?," Energy Policy, Elsevier, vol. 137(C).
    90. Floros Flouros & Victoria Pistikou & Vasilios Plakandaras, 2022. "Geopolitical Risk as a Determinant of Renewable Energy Investments," Energies, MDPI, vol. 15(4), pages 1-21, February.
    91. J. B. Qian, 2016. "Estimation of Panel Model with Spatial Autoregressive Error and Common Factors," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 367-399, March.
    92. Mitch Kunce, 2023. "Unemployment and Suicide in the United States: The Import of Addressing Cross-Sectional Dependence," Bulletin of Applied Economics, Risk Market Journals, vol. 10(1), pages 1-19.
    93. Alexander Chudik & Janet Koech & Mark Wynne, 2021. "The Heterogeneous Effects of Global and National Business Cycles on Employment in US States and Metropolitan Areas," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 495-517, April.
    94. Pakel, Cavit, 2019. "Bias reduction in nonlinear and dynamic panels in the presence of cross-section dependence," Journal of Econometrics, Elsevier, vol. 213(2), pages 459-492.
    95. Tullio Gregori & Marco Giansoldati, 2023. "Do current and capital account liberalizations affect economic growth in the long run?," Empirical Economics, Springer, vol. 65(1), pages 247-273, July.
    96. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2015. "An investigation into multivariate variance ratio statistics and their application to stock market predictability," CeMMAP working papers 13/15, Institute for Fiscal Studies.
    97. Chakraborty, Saptorshee Kanto & Mazzanti, Massimiliano, 2020. "Energy intensity and green energy innovation: Checking heterogeneous country effects in the OECD," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 328-343.
    98. Kapetanios, G. & Pesaran, M.H. & Reese, S., 2021. "Detection of units with pervasive effects in large panel data models," Journal of Econometrics, Elsevier, vol. 221(2), pages 510-541.

  44. Kapetanios, George & Yates, Tony, 2011. "Evolving UK and US macroeconomic dynamics through the lens of a model of deterministic structural change," Bank of England working papers 434, Bank of England.

    Cited by:

    1. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    2. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.

  45. George Kapetanios & Fotis Papailias, 2011. "Block Bootstrap and Long Memory," Working Papers 679, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Adam McCloskey, 2012. "Estimation of the Long-Memory Stochastic Volatility Model Parameters that is Robust to Level Shifts and Deterministic Trends," Working Papers 2012-17, Brown University, Department of Economics.
    2. Sizova, Natalia, 2014. "A frequency-domain alternative to long-horizon regressions with application to return predictability," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 261-272.
    3. Arteche, Josu & Orbe, Jesus, 2016. "A bootstrap approximation for the distribution of the Local Whittle estimator," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 645-660.

  46. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," CEPR Discussion Papers 7796, C.E.P.R. Discussion Papers.

    Cited by:

    1. Fuentes-Albero, Cristina & Melosi, Leonardo, 2013. "Methods for computing marginal data densities from the Gibbs output," Journal of Econometrics, Elsevier, vol. 175(2), pages 132-141.
    2. Haroon Mumtaz & Alexandra Solovyeva & Elena Vasilieva, 2012. "Asset prices, credit and the Russian economy," Joint Research Papers 1, Centre for Central Banking Studies, Bank of England.
    3. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 1-33.
    4. Kapetanios, George & Mumtaz, Haroon & Stevens, Ibrahim & Theodoridis, Konstantinos, 2012. "Assessing the economy-wide effects of quantitative easing," Bank of England working papers 443, Bank of England.
    5. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2011. "Bayesian VARs: Specification Choices and Forecast Accuracy," CEPR Discussion Papers 8273, C.E.P.R. Discussion Papers.
    6. Simon Gilchrist & Egon Zakrajsek & Cristina Fuentes Albero & Dario Caldara, 2013. "On the Identification of Financial and Uncertainty Shocks," 2013 Meeting Papers 965, Society for Economic Dynamics.
    7. Habrov, Vladimir, 2012. "Optimization of portfolio management based on vector autoregression models and multivariate volatility models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 28(4), pages 35-62.
    8. Hajer Ben Romdhane & Nahed Ben Tanfous, 2017. "Conditional FAVAR and scenario analysis for a large data: case of Tunisia," IHEID Working Papers 15-2017, Economics Section, The Graduate Institute of International Studies.

  47. Eklund, Jana & Kapetanios, George & Price, Simon, 2010. "Forecasting in the presence of recent structural change," Bank of England working papers 406, Bank of England.

    Cited by:

    1. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    2. Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen V.K. Rombouts, 2011. "The Contribution of Structural Break Models to Forecasting Macroeconomic Series," Working Paper series 38_11, Rimini Centre for Economic Analysis.
    3. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2012. "Exponent of Cross-sectional Dependence: Estimation and Inference," CESifo Working Paper Series 3722, CESifo.
    4. Liudas Giraitis & George Kapetanios & Simon Price, 2012. "Adaptive Forcasting in the Presence of Recent and Ongoing Structural Change," CAMA Working Papers 2012-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Tian, Jing & Anderson, Heather M., 2014. "Forecast combinations under structural break uncertainty," International Journal of Forecasting, Elsevier, vol. 30(1), pages 161-175.
    6. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    7. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    8. Kirdan Lees, 2009. "Overview of a recent Reserve Bank workshop: nowcasting with model combination," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 72, pages 31-33, March.
    9. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.
    10. Vipin Arora and Jozef Lieskovsky, 2014. "Natural Gas and U.S. Economic Activity," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).

  48. Marcellino, Massimiliano & Kapetanios, George, 2010. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," CEPR Discussion Papers 7726, C.E.P.R. Discussion Papers.

    Cited by:

    1. Ludovica Gambaro & Guido Neidhöfer & C. Katharina Spieß, 2019. "The Effect of Early Childhood Education and Care Services on the Social Integration of Refugee Families," Discussion Papers of DIW Berlin 1828, DIW Berlin, German Institute for Economic Research.
    2. Ng Serena & Bai Jushan, 2009. "Selecting Instrumental Variables in a Data Rich Environment," Journal of Time Series Econometrics, De Gruyter, vol. 1(1), pages 1-34, April.
    3. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.
    4. Paul Hubert, 2014. "Disentangling qualitative and quantitative central bank influence," SciencePo Working papers Main hal-01098464, HAL.
    5. Milda Norkute, 2015. "Can the sectoral New Keynesian Phillips curve explain inflation dynamics in the Euro Area?," Empirical Economics, Springer, vol. 49(4), pages 1191-1216, December.
    6. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2012. "Exponent of Cross-sectional Dependence: Estimation and Inference," CESifo Working Paper Series 3722, CESifo.
    7. Gründler, Klaus & Scheuermeyer, Philipp, 2018. "Growth effects of inequality and redistribution: What are the transmission channels?," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 293-313.
    8. Onatski, Alexei, 2015. "Asymptotic analysis of the squared estimation error in misspecified factor models," Journal of Econometrics, Elsevier, vol. 186(2), pages 388-406.
    9. Alexandre Belloni & Daniel Chen & Victor Chernozhukov & Christian Hansen, 2010. "Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain," Papers 1010.4345, arXiv.org, revised Apr 2015.
    10. Guenter W. Beck & Kirstin Hubrich & Massimiliano Marcellino, 2016. "On the Importance of Sectoral and Regional Shocks for Price‐Setting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1234-1253, November.
    11. Emna Trabelsi, 2022. "Macroprudential Transparency and Price Stability in Emerging and Developing Countries," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 11(1), pages 105-129.
    12. Wenxi Lu, 2018. "FDI, Service imports and Export development," School of Economics and Public Policy Working Papers 2018-05, University of Adelaide, School of Economics and Public Policy.
    13. Ieva Skarda, 2016. "The Political Economy of Foreign Aid Effectiveness," Discussion Papers 16/12, Department of Economics, University of York.
    14. Panagiota Papadimitri & Ansgar Wohlschlegel, 2020. "Lobbying and Enforcement: Theory and Application to Bank Regulation," Working Papers 2020-01, Swansea University, School of Management.
    15. Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
    16. Guy Tchuente, 2016. "Estimation of social interaction models using regularization," Studies in Economics 1607, School of Economics, University of Kent.
    17. Mullings, Robert & Mahabir, Aruneema, 2018. "Growth by Destination: The Role of Trade in Africa’s Recent Growth Episode," World Development, Elsevier, vol. 102(C), pages 243-261.
    18. Marcellino, Massimiliano & Kapetanios, George, 2010. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," CEPR Discussion Papers 7726, C.E.P.R. Discussion Papers.
    19. Yongfu Huang & Muhammad G. Quibria, 2015. "The global partnership for sustainable development," Natural Resources Forum, Blackwell Publishing, vol. 39(3-4), pages 157-174, August.
    20. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2009. "Weak and Strong Cross Section Dependence and Estimation of Large Panels," CESifo Working Paper Series 2689, CESifo.
    21. Mavroeidis, Sophocles & Plagborg-Moller, Mikkel & Stock, James H., 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Scholarly Articles 22795845, Harvard University Department of Economics.
    22. Pesaran, M. Hashem & Tosetti, Elisa, 2007. "Large Panels with Common Factors and Spatial Correlations," IZA Discussion Papers 3032, Institute of Labor Economics (IZA).
    23. M. E. Bontempi & I. Mammi, 2014. "pca2: implementing a strategy to reduce the instrument count in panel GMM," Working Papers wp960, Dipartimento Scienze Economiche, Universita' di Bologna.
    24. Robert Lantis & Erik Nesson, 2024. "The Hot Hand in the NBA 3-Point Contest: The Importance of Location, Location, Location," Journal of Sports Economics, , vol. 25(3), pages 283-321, April.
    25. Emna Trabelsi, 2019. "Do independence and transparency matter for bank development? A new lookup on emerging and developing countries," Post-Print hal-02162780, HAL.
    26. Carrasco, Marine & Tchuente, Guy, 2015. "Regularized LIML for many instruments," Journal of Econometrics, Elsevier, vol. 186(2), pages 427-442.
    27. Declan French, 2016. "Financial Strain in the United Kingdom," CHaRMS Working Papers 16-02, Centre for HeAlth Research at the Management School (CHaRMS).
    28. Travaglini, Guido, 2010. "Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005," MPRA Paper 22077, University Library of Munich, Germany.
    29. Fajeau, Maxime, 2021. "Too much finance or too many weak instruments?," International Economics, Elsevier, vol. 165(C), pages 14-36.
    30. Njindan Iyke, Bernard, 2016. "Does Trade Openness Matter for Economic Growth in the CEE Countries?," MPRA Paper 78869, University Library of Munich, Germany.
    31. Tong, Eric, 2017. "US monetary policy and global financial stability," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 466-485.
    32. Giorgio Canarella & Stephen M. Miller, 2017. "The Determinants of Growth in the Information and Communication Technology (ICT) Industry: A Firm-Level Analysis," Working papers 2017-12, University of Connecticut, Department of Economics.
    33. Saman Banafti & Tae-Hwy Lee, 2023. "Inferential Theory for Granular Instrumental Variables in High Dimensions," Working Papers 202308, University of California at Riverside, Department of Economics.
    34. Marcellino, Massimiliano & Kapetanios, George & Khalaf, Lynda, 2015. "Factor based identification-robust inference in IV regressions," CEPR Discussion Papers 10390, C.E.P.R. Discussion Papers.
    35. Hao-Chang Yang & Ferry Syarifuddin & Chun-Ping Chang & Hai-Jie Wang, 2022. "The Impact of Exchange Rate Futures Fluctuations on Macroeconomy: Evidence from Ten Trading Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(8), pages 2300-2313, June.
    36. Hansen, Christian & Kozbur, Damian, 2014. "Instrumental variables estimation with many weak instruments using regularized JIVE," Journal of Econometrics, Elsevier, vol. 182(2), pages 290-308.
    37. Tom Boot & Didier Nibbering, 2016. "Forecasting Using Random Subspace Methods," Tinbergen Institute Discussion Papers 16-073/III, Tinbergen Institute, revised 11 Aug 2017.
    38. Guo, Xiao & Chen, Yu & Tang, Cheng Yong, 2023. "Information criteria for latent factor models: A study on factor pervasiveness and adaptivity," Journal of Econometrics, Elsevier, vol. 233(1), pages 237-250.
    39. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    40. Gachet, Ivan & Maldonado, Diego & Pérez, Wilson, 2008. "Determinantes de la Inflación en una Economía Dolarizada: El Caso Ecuatoriano [Determinants of Inflation in a Dollarized Economy: The Case of Ecuador]," MPRA Paper 17101, University Library of Munich, Germany.
    41. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2008. "A Shrinkage Instrumental Variable Estimator for Large Datasets," Working Papers 626, Queen Mary University of London, School of Economics and Finance.
    42. Bontempi, Maria Elena & Mammi, Irene, 2012. "A strategy to reduce the count of moment conditions in panel data GMM," MPRA Paper 40720, University Library of Munich, Germany.
    43. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    44. Claudio Detotto & Sauveur Giannoni & Claire Goavec, 2017. "Does good governance attract tourists?," Working Papers 002, Laboratoire Lieux, Identités, eSpaces et Activités (LISA).
    45. Christopher Malikane, 2017. "The labour share and the dynamics of output," Applied Economics, Taylor & Francis Journals, vol. 49(37), pages 3741-3750, August.
    46. Kapetanios, George & Marcellino, Massimiliano, 2010. "Cross-sectional averaging and instrumental variable estimation with many weak instruments," Economics Letters, Elsevier, vol. 108(1), pages 36-39, July.
    47. Abeer Elshennawy & Mohammed Bouaddi, 2021. "Sources of firm-level heterogeneity in labour productivity in Egypt’s manufacturing sector," Empirical Economics, Springer, vol. 60(5), pages 2589-2612, May.
    48. Mirza, Harun & Storjohann, Lidia, 2011. "Making a Weak Instrument Set Stronger: Factor-Based Estimation of the Taylor Rule," Bonn Econ Discussion Papers 13/2011, University of Bonn, Bonn Graduate School of Economics (BGSE).
    49. Gambaro, Ludovica & Neidhöfer, Guido & Spiess, C. Katharina, 2021. "The effect of early childhood education and care services on the integration of refugee families," Labour Economics, Elsevier, vol. 72(C).
    50. Panagiota Makrychoriti & Fotios Pasiouras & Menelaos Tasiou, 2022. "Financial stress and economic growth: The moderating role of trust," Kyklos, Wiley Blackwell, vol. 75(1), pages 48-74, February.
    51. Scheffel, Eric Michael, 2012. "Political uncertainty in a data-rich environment," MPRA Paper 37318, University Library of Munich, Germany.
    52. Guy Tchuente, 2019. "Weak Identification and Estimation of Social Interaction Models," Papers 1902.06143, arXiv.org.
    53. Omer Bayar, 2022. "Reducing large datasets to improve the identification of estimated policy rules," Empirical Economics, Springer, vol. 63(1), pages 113-140, July.
    54. Kwok Tong Soo, 2015. "Innovation across cities," Working Papers 100098721, Lancaster University Management School, Economics Department.
    55. Takeshima, Hiroyuki & Liu, Yanyan, 2020. "Smallholder mechanization induced by yield-enhancing biological technologies: Evidence from Nepal and Ghana," Agricultural Systems, Elsevier, vol. 184(C).
    56. Bertille Antoine & Otilia Boldea, 2014. "Efficient Inference with Time-Varying Identification Strength," Discussion Papers dp14-03, Department of Economics, Simon Fraser University.
    57. Beck, Guenter W. & Hubrich, Kirstin & Marcellino, Massimiliano, 2009. "On the importance of sectoral shocks for price-setting," CFS Working Paper Series 2009/32, Center for Financial Studies (CFS).
    58. Dima Bogdan & Dima Ştefana Maria, 2017. "Does Corporate Tax Burden Affect Growth? Evidences from OECD Countries," Journal of Heterodox Economics, Sciendo, vol. 4(2), pages 51-80, December.

  49. Groen, Jan J J & Kapetanios, George & Price, Simon, 2009. "Multivariate methods for monitoring structural change," Bank of England working papers 369, Bank of England.

    Cited by:

    1. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 206(1), pages 187-225.
    2. S. O. Tickle & I. A. Eckley & P. Fearnhead, 2021. "A computationally efficient, high‐dimensional multiple changepoint procedure with application to global terrorism incidence," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1303-1325, October.
    3. Jana Eklund & George Kapetanios & Simon Price, 2011. "Forecasting in the presence of recent structural change," CAMA Working Papers 2011-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Pape, Katharina & Wied, Dominik & Galeano, Pedro, 2016. "Monitoring multivariate variance changes," Journal of Empirical Finance, Elsevier, vol. 39(PA), pages 54-68.
    5. Chiu, Ching-Wai (Jeremy) & Hayes, Simon & Kapetanios, George & Theodoridis, Konstantinos, 2019. "A new approach for detecting shifts in forecast accuracy," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1596-1612.
    6. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.
    7. Matteo Barigozzi & Lorenzo Trapani, 2017. "Sequential testing for structural stability in approximate factor models," Papers 1708.02786, arXiv.org, revised Mar 2020.
    8. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    9. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.
    10. Pierre Perron & Eduardo Zorita & Eiji Kurozumi, 2017. "Monitoring Parameter Constancy with Endogenous Regressors," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 791-805, September.
    11. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2020. "Change-point methods for multivariate time-series: paired vectorial observations," Statistical Papers, Springer, vol. 61(4), pages 1351-1383, August.

  50. Jan J. J. Groen & George Kapetanios, 2009. "Parsimonious estimation with many instruments," Staff Reports 386, Federal Reserve Bank of New York.

    Cited by:

    1. M. E. Bontempi & I. Mammi, 2014. "pca2: implementing a strategy to reduce the instrument count in panel GMM," Working Papers wp960, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. Travaglini, Guido, 2010. "Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005," MPRA Paper 22077, University Library of Munich, Germany.
    3. Bontempi, Maria Elena & Mammi, Irene, 2012. "A strategy to reduce the count of moment conditions in panel data GMM," MPRA Paper 40720, University Library of Munich, Germany.

  51. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.

    Cited by:

    1. Lombardi, Marco J. & Osbat, Chiara & Schnatz, Bernd, 2010. "Global commodity cycles and linkages a FAVAR approach," Working Paper Series 1170, European Central Bank.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    3. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    4. Teresa Buchen & Klaus Wohlrabe, 2013. "Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany," CESifo Working Paper Series 4148, CESifo.
    5. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    6. Pirschel, Inske & Wolters, Maik, 2014. "Forecasting German key macroeconomic variables using large dataset methods," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100587, Verein für Socialpolitik / German Economic Association.
    7. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    8. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    9. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    10. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    11. Gianluca Cubadda & Marco Mazzali, 2024. "The vector error correction index model: representation, estimation and identification," The Econometrics Journal, Royal Economic Society, vol. 27(1), pages 126-150.
    12. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    13. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    14. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    15. Inske Pirschel & Maik H. Wolters, 2018. "Forecasting with large datasets: compressing information before, during or after the estimation?," Empirical Economics, Springer, vol. 55(2), pages 573-596, September.
    16. Panagiotelis, Anastasios & Athanasopoulos, George & Hyndman, Rob J. & Jiang, Bin & Vahid, Farshid, 2019. "Macroeconomic forecasting for Australia using a large number of predictors," International Journal of Forecasting, Elsevier, vol. 35(2), pages 616-633.
    17. Christophe Croux & Peter Exterkate, 2011. "Sparse and Robust Factor Modelling," Tinbergen Institute Discussion Papers 11-122/4, Tinbergen Institute.
    18. Churm, Rohan & Joyce, Mike & Kapetanios, George & Theodoridis, Konstantinos, 2015. "Unconventional monetary policies and the macroeconomy: the impact of the United Kingdom's QE2 and Funding for Lending Scheme," Bank of England working papers 542, Bank of England.
    19. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
    20. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
    21. Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig, 2021. "Forecasting the production side of GDP," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 458-480, April.
    22. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    23. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    24. Marco Centoni & Gianluca Cubadda, 2015. "Common Feature Analysis of Economic Time Series: An Overview and Recent Developments," CEIS Research Paper 355, Tor Vergata University, CEIS, revised 05 Oct 2015.
    25. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Common drifting volatility in large Bayesian VARs," Working Papers (Old Series) 1206, Federal Reserve Bank of Cleveland.
    26. Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Adaptive hierarchical priors for high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 212(1), pages 241-271.
    27. Gustavo Fruet Dias & George Kapetanios, 2014. "Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets," CREATES Research Papers 2014-37, Department of Economics and Business Economics, Aarhus University.
    28. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    29. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," CEPR Discussion Papers 7796, C.E.P.R. Discussion Papers.
    30. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    31. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    32. Chan, Joshua C.C. & Eisenstat, Eric & Strachan, Rodney W., 2020. "Reducing the state space dimension in a large TVP-VAR," Journal of Econometrics, Elsevier, vol. 218(1), pages 105-118.
    33. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2011. "Bayesian VARs: Specification Choices and Forecast Accuracy," CEPR Discussion Papers 8273, C.E.P.R. Discussion Papers.
    34. Pablo Guerrón-Quintana & Molin Zhong, 2017. "Macroeconomic Forecasting in Times of Crises," Finance and Economics Discussion Series 2017-018, Board of Governors of the Federal Reserve System (U.S.).
    35. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    36. Gianluca Cubadda & Barbara Guardabascio, 2017. "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model," CEIS Research Paper 397, Tor Vergata University, CEIS, revised 13 Jul 2018.
    37. Bernardini, Emmanuela & Cubadda, Gianluca, 2015. "Macroeconomic forecasting and structural analysis through regularized reduced-rank regression," International Journal of Forecasting, Elsevier, vol. 31(3), pages 682-691.
    38. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss," Resources Policy, Elsevier, vol. 47(C), pages 95-107.
    39. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    40. Justyna Wróblewska & Anna Pajor, 2019. "One-period joint forecasts of Polish inflation, unemployment and interest rate using Bayesian VEC-MSF models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 11(1), pages 23-45, March.
    41. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    42. Bin Jiang & Anastasios Panagiotelis & George Athanasopoulos & Rob Hyndman & Farshid Vahid, 2016. "Bayesian Rank Selection in Multivariate Regression," Monash Econometrics and Business Statistics Working Papers 6/16, Monash University, Department of Econometrics and Business Statistics.
    43. Matilainen, M. & Croux, C. & Nordhausen, K. & Oja, H., 2017. "Supervised dimension reduction for multivariate time series," Econometrics and Statistics, Elsevier, vol. 4(C), pages 57-69.
    44. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    45. Bartkus Algirdas, 2016. "A New Model with Regime Switching Errors: Forecasting Gdp in Times of Great Recession," Ekonomika (Economics), Sciendo, vol. 95(2), pages 7-29, February.
    46. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    47. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper series 18-37, Rimini Centre for Economic Analysis.
    48. Doo Won Bang & HyuckShin Kwon, 2022. "Policy Impact Analysis of Housing Policies Using Housing Cycles," SAGE Open, , vol. 12(3), pages 21582440221, July.
    49. Adam Nowak & Patrick Smith, 2015. "Textual Analysis in Real Estate," Working Papers 15-34, Department of Economics, West Virginia University.
    50. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.
    51. Zeng, Jing, 2014. "Forecasting Aggregates with Disaggregate Variables: Does boosting help to select the most informative predictors?," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100310, Verein für Socialpolitik / German Economic Association.
    52. Prüser, Jan, 2023. "Data-based priors for vector error correction models," International Journal of Forecasting, Elsevier, vol. 39(1), pages 209-227.
    53. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    54. Jing Zeng, 2014. "Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?," Working Paper Series of the Department of Economics, University of Konstanz 2014-20, Department of Economics, University of Konstanz.
    55. Churm, Rohan & Joyce, Michael & Kapetanios, George & Theodoridis, Konstantinos, 2021. "Unconventional monetary policies and the macroeconomy: The impact of the UK's QE2 and funding for lending scheme," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 721-736.
    56. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    57. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    58. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    59. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.

  52. Jan J. J. Groen & George Kapetanios, 2009. "Model selection criteria for factor-augmented regressions," Staff Reports 363, Federal Reserve Bank of New York.

    Cited by:

    1. Pesenti, Paolo & Groen, Jan J. J., 2010. "Commodity prices, commodity currencies, and global economic developments," CEPR Discussion Papers 7689, C.E.P.R. Discussion Papers.
    2. John W. Galbraith & Victoria Zinde-Walsh, 2011. "Partially Dimension-Reduced Regressions with Potentially Infinite-Dimensional Processes," CIRANO Working Papers 2011s-57, CIRANO.
    3. Menzie D. Chinn & Kavan J. Kucko, 2010. "The Predictive Power of the Yield Curve across Countries and Time," NBER Working Papers 16398, National Bureau of Economic Research, Inc.
    4. Kapetanios, G & Price, SG & Young, G, 2017. "A UK financial conditions index using targeted data reduction: forecasting and structural identification," Essex Finance Centre Working Papers 20328, University of Essex, Essex Business School.
    5. Westerlund, Joakim & Urbain, Jean-Pierre, 2013. "On the implementation and use of factor-augmented regressions in panel data," Journal of Asian Economics, Elsevier, vol. 28(C), pages 3-11.
    6. Antoine A. Djogbenou, 2017. "Model Selection In Factor-augmented Regressions With Estimated Factors," Working Paper 1391, Economics Department, Queen's University.
    7. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.

  53. Caggiano, Giovanni & Kapetanios, George & Labhard, Vincent, 2009. "Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK," Working Paper Series 1051, European Central Bank.

    Cited by:

    1. Maurin, Laurent & Drechsel, Katja, 2008. "Flow of conjunctural information and forecast of euro area economic activity," Working Paper Series 925, European Central Bank.
    2. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    3. Evren Erdogan Cosar & Sevim Kosem & Cagri Sarikaya, 2013. "Do We Really Need Filters In Estimating Output Gap? : Evidence From Turkey," Working Papers 1333, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    4. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    5. Germán López, 2015. "Forecast Accuracy of Small and Large Scale Dynamic Factor Models in Developing Economies," Working Papers. Serie AD 2015-03, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    6. Derek Bunn & Julien Chevallier & Yannick Le Pen & Benoît Sévi, 2017. "Fundamental and Financial Influences on the Co-movement of Oil and Gas prices," Post-Print hal-01619890, HAL.
    7. Irma Hindrayanto & Siem Jan Koopman & Jasper de Winter, 2014. "Nowcasting and Forecasting Economic Growth in the Euro Area using Principal Components," Tinbergen Institute Discussion Papers 14-113/III, Tinbergen Institute.
    8. Marie Bessec, 2013. "Short‐Term Forecasts of French GDP: A Dynamic Factor Model with Targeted Predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 500-511, September.
    9. Zeyyad Mandalinci, 2015. "Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models," CReMFi Discussion Papers 3, CReMFi, School of Economics and Finance, QMUL.
    10. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
    11. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.
    12. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    13. Poncela, Pilar, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Dr. Alain Galli & Dr. Christian Hepenstrick & Dr. Rolf Scheufele, 2017. "Mixed-frequency models for tracking short-term economic developments in Switzerland," Working Papers 2017-02, Swiss National Bank.
    15. Ard Reijer, 2013. "Forecasting Dutch GDP and inflation using alternative factor model specifications based on large and small datasets," Empirical Economics, Springer, vol. 44(2), pages 435-453, April.
    16. Alvarez, Rocio & Camacho, Maximo & Perez-Quiros, Gabriel, 2016. "Aggregate versus disaggregate information in dynamic factor models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 680-694.
    17. Carstensen, Kai & Wohlrabe, Klaus & Ziegler, Christina, 2011. "Predictive ability of business cycle indicators under test: A case study for the Euro area industrial production," Munich Reprints in Economics 19953, University of Munich, Department of Economics.
    18. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434, Emerald Group Publishing Limited.
    19. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    20. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    21. Marianna Cervená & Martin Schneider, 2010. "Short-term forecasting GDP with a DSGE model augmented by monthly indicators," Working Papers 163, Oesterreichische Nationalbank (Austrian Central Bank).
    22. Bessonovs, Andrejs, 2010. "Faktoru modeļu agregēta un dezagregēta pieeja IKP prognožu precizitātes mērīšanā [Measuring GDP forecasting accuracy using factor models: aggregated vs. disaggregated approach]," MPRA Paper 30386, University Library of Munich, Germany.
    23. Müller-Kademann Christian, 2015. "Internal Validation of Temporal Disaggregation: A Cloud Chamber Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(3), pages 298-319, June.
    24. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    25. Christiana Anaxagorou & Nicoletta Pashourtidou, 2022. "Forecasting economic activity using preselected predictors: the case of Cyprus," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 16(1), pages 11-36, June.
    26. Gianluca Cubadda & Barbara Guardabascio, 2017. "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model," CEIS Research Paper 397, Tor Vergata University, CEIS, revised 13 Jul 2018.
    27. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2015. "Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 576-595, June.
    28. Siliverstovs Boriss & Kholodilin Konstantin A., 2012. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 429-444, August.
    29. Poncela, Pilar, 2015. "Small versus big-data factor extraction in Dynamic Factor Models: An empirical assessment," DES - Working Papers. Statistics and Econometrics. WS ws1502, Universidad Carlos III de Madrid. Departamento de Estadística.
    30. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    31. Pérez-Quirós, Gabriel & Camacho, Máximo & Alvarez, Rocio, 2012. "Finite sample performance of small versus large scale dynamic factor models," CEPR Discussion Papers 8867, C.E.P.R. Discussion Papers.
    32. Bellégo, C. & Ferrara, L., 2012. "Macro-financial linkages and business cycles: A factor-augmented probit approach," Economic Modelling, Elsevier, vol. 29(5), pages 1793-1797.
    33. Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting the French index of industrial production: A comparison from bridge and factor models," Economic Modelling, Elsevier, vol. 29(6), pages 2174-2182.
    34. Sauvenier, Mathieu & Van Bellegem, Sébastien, 2023. "Goodness-of-fit test in high-dimensional linear sparse models," LIDAM Discussion Papers CORE 2023008, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    35. Solikin M. Juhro & Bernard Njindan Iyke, 2019. "Forecasting Indonesian Inflation Within An Inflation-Targeting Framework: Do Large-Scale Models Pay Off?," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 22(4), pages 423-436, December.
    36. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    37. Bessonovs, Andrejs, 2011. "GDP Modelling with Factor Model: an Impact of Nested Data on Forecasting Accuracy," MPRA Paper 30211, University Library of Munich, Germany.
    38. Hauber, Philipp, 2022. "Real-time nowcasting with sparse factor models," EconStor Preprints 251551, ZBW - Leibniz Information Centre for Economics.
    39. Aysun, Uluc & Wright, Cardel, 2024. "A two-step dynamic factor modelling approach for forecasting inflation in small open economies," Emerging Markets Review, Elsevier, vol. 62(C).

  54. George Kapetanios & Massimiliano Marcellino, 2008. "Cross-sectional Averaging and Instrumental Variable Estimation with Many Weak Instruments," Working Papers 627, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Malikane, Christopher, 2013. "A New Keynesian Triangle Phillips Curve," MPRA Paper 43548, University Library of Munich, Germany.

  55. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," CEPR Discussion Papers 7008, C.E.P.R. Discussion Papers.

    Cited by:

    1. Teona Shugliashvili, 2023. "The words have power: the impact of news on exchange rates," FFA Working Papers 5.006, Prague University of Economics and Business, revised 31 Jul 2023.
    2. Igor Masten & Massimiliano Marcellino & Anindya Banerjeey, 2009. "Forecasting with Factor-augmented Error Correction Models," RSCAS Working Papers 2009/32, European University Institute.
    3. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    4. Koop, Gary & Korobilis, Dimitris, 2016. "Model uncertainty in Panel Vector Autoregressive models," European Economic Review, Elsevier, vol. 81(C), pages 115-131.
    5. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    6. Joshua C.C. Chan & Angelia L. Grant, 2016. "Reconciling output gaps: unobserved components model and Hodrick-Prescott filter," CAMA Working Papers 2016-44, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Jonas Dovern & Martin Feldkircher & Florian Huber, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR," Working Papers 200, Oesterreichische Nationalbank (Austrian Central Bank).
    8. Huber, Florian, 2016. "Density forecasting using Bayesian global vector autoregressions with stochastic volatility," International Journal of Forecasting, Elsevier, vol. 32(3), pages 818-837.
    9. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2020. "Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 934-943, September.
    10. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    11. Antonio Pacifico, 2022. "Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure," Econometrics, MDPI, vol. 10(3), pages 1-24, July.
    12. Chan, Joshua C.C., 2023. "Comparing stochastic volatility specifications for large Bayesian VARs," Journal of Econometrics, Elsevier, vol. 235(2), pages 1419-1446.
    13. Costantini, Mauro & Crespo Cuaresma, Jesus & Hlouskova, Jaroslava, 2014. "Can Macroeconomists Get Rich Forecasting Exchange Rates?," Department of Economics Working Paper Series 176, WU Vienna University of Economics and Business.
    14. Alexander Chudik & M. Hashem Pesaran, 2016. "Theory And Practice Of Gvar Modelling," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 165-197, February.
    15. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    16. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    17. Simone Auer, 2014. "Monetary policy shocks and foreign investment income: evidence from a large Bayesian VAR," Globalization Institute Working Papers 170, Federal Reserve Bank of Dallas.
    18. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    19. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    20. Florian Huber & Tamas Krisztin & Philipp Piribauer, 2014. "Forecasting Global Equity Indices using Large Bayesian VARs," Department of Economics Working Papers wuwp184, Vienna University of Economics and Business, Department of Economics.
    21. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    22. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    23. Santos, André A.P. & Nogales, Francisco J. & Ruiz, Esther & Dijk, Dick Van, 2012. "Optimal portfolios with minimum capital requirements," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1928-1942.
    24. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian vector autoregressions," LSE Research Online Documents on Economics 87393, London School of Economics and Political Science, LSE Library.
    25. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2014. "Large Bayesian VARMAs," SIRE Discussion Papers 2015-06, Scottish Institute for Research in Economics (SIRE).
    26. Koop, Gary & Korobilis, Dimitris, 2012. "Large Time-Varying Parameter VARs," SIRE Discussion Papers 2012-14, Scottish Institute for Research in Economics (SIRE).
    27. Xin Sheng & Rangan Gupta & Afees A. Salisu & Elie Bouri, 2021. "OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning," Working Papers 202101, University of Pretoria, Department of Economics.
    28. Albis, Manuel Leonard F. & Mapa, Dennis S., 2014. "Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models," MPRA Paper 55902, University Library of Munich, Germany.
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    32. Chan, Joshua C.C., 2021. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1212-1226.
    33. Dimitrios P. Louzis, 2017. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs," Empirical Economics, Springer, vol. 53(2), pages 569-598, September.
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    35. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    36. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2011. "Bayesian VARs: Specification Choices and Forecast Accuracy," CEPR Discussion Papers 8273, C.E.P.R. Discussion Papers.
    37. Crespo Cuaresma, Jesus & Fortin, Ines & Hlouskova, Jaroslava, 2017. "Exchange rate forecasting and the performance of currency portfolios," Economics Series 326, Institute for Advanced Studies.
    38. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    39. Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
    40. Garratt, Anthony & Mise, Emi, 2014. "Forecasting exchange rates using panel model and model averaging," Economic Modelling, Elsevier, vol. 37(C), pages 32-40.
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    42. Haskamp, Ulrich, 2017. "Forecasting exchange rates: The time-varying relationship between exchange rates and Taylor rule fundamentals," Ruhr Economic Papers 704, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    43. Huber Florian, 2016. "Forecasting exchange rates using multivariate threshold models," The B.E. Journal of Macroeconomics, De Gruyter, vol. 16(1), pages 193-210, January.
    44. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    45. Ahmed Ibrahim & Rasha Kashef & Menglu Li & Esteban Valencia & Eric Huang, 2020. "Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables," JRFM, MDPI, vol. 13(9), pages 1-21, August.
    46. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    47. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    48. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2018. "Directed Graphs and Variable Selection in Large Vector Autoregressive Models," Working Paper Series of the Department of Economics, University of Konstanz 2018-08, Department of Economics, University of Konstanz.
    49. Abbate, Angela & Marcellino, Massimiliano, 2016. "Point, interval and density forecasts of exchange rates with time-varying parameter models," Discussion Papers 19/2016, Deutsche Bundesbank.
    50. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
    51. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    52. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    53. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    54. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    55. Ponomareva, Natalia & Sheen, Jeffrey & Wang, Ben Zhe, 2019. "Forecasting exchange rates using principal components," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    56. Chihwa Kao & Lorenzo Trapani & Giovanni Urga, 2012. "Testing for Instability in Covariance Structures," Center for Policy Research Working Papers 131, Center for Policy Research, Maxwell School, Syracuse University.
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    61. Magnus Reif, 2018. "Macroeconomic Uncertainty and Forecasting Macroeconomic Aggregates," ifo Working Paper Series 265, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    62. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.
    63. Beckmann, J & Koop, G & Korobilis, D & Schüssler, R, 2017. "Exchange rate predictability and dynamic Bayesian learning," Essex Finance Centre Working Papers 20781, University of Essex, Essex Business School.
    64. Ms. Adina Popescu & Ms. Alina Carare, 2011. "Monetary Policy and Risk-Premium Shocks in Hungary: Results from a Large Bayesian VAR," IMF Working Papers 2011/259, International Monetary Fund.
    65. Carriero, Andrea & Clements, Michael P. & Galvão, Ana Beatriz, 2015. "Forecasting with Bayesian multivariate vintage-based VARs," International Journal of Forecasting, Elsevier, vol. 31(3), pages 757-768.
    66. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    67. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
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    71. Zhang, Bo & Nguyen, Bao H., 2020. "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers 2020-12, University of Tasmania, Tasmanian School of Business and Economics.
    72. Florian Huber & Jesus Crespo-Cuaresma & Martin Feldkircher, 2014. "Forecasting with Bayesian Global Vector Autoregressions," ERSA conference papers ersa14p25, European Regional Science Association.
    73. Florian Huber & Gary Koop & Massimiliano Marcellino & Tobias Scheckel, 2024. "Bayesian modelling of VAR precision matrices using stochastic block networks," Papers 2407.16349, arXiv.org.
    74. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    75. Nguyen, BH & Zhang, Bo, 2022. "Forecasting oil Prices: can large BVARs help?," Working Papers 2022-04, University of Tasmania, Tasmanian School of Business and Economics.
    76. Bacchetta, Philippe & van Wincoop, Eric & Beutler, Toni, 2009. "Can Parameter Instability Explain the Meese-Rogoff Puzzle?," CEPR Discussion Papers 7383, C.E.P.R. Discussion Papers.
    77. Valentina Aprigliano, 2020. "A large Bayesian VAR with a block‐specific shrinkage: A forecasting application for Italian industrial production," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1291-1304, December.
    78. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    79. Feldkircher, Martin & Huber, Florian, 2016. "The international transmission of US shocks—Evidence from Bayesian global vector autoregressions," European Economic Review, Elsevier, vol. 81(C), pages 167-188.
    80. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2023. "Directed graphs and variable selection in large vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 223-246, March.
    81. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    82. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    83. Fu, Bowen, 2023. "Measuring the trend real interest rate in a data-rich environment," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    84. Lance Kent, 2014. "Bilateral Linkages and the International Transmission of Business Cycles," Working Papers 149, Department of Economics, College of William and Mary.
    85. Kim, Young Min & Lee, Seojin, 2020. "Exchange rate predictability: A variable selection perspective," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 117-134.
    86. Cobb, Marcus P A, 2017. "Aggregate Density Forecasting from Disaggregate Components Using Large VARs," MPRA Paper 76849, University Library of Munich, Germany.
    87. Jesús Crespo Cuaresma & Martin Feldkircher & Florian Huber, 2014. "Forecasting with Bayesian Global Vector Autoregressive Models: A Comparison of Priors," Working Papers 189, Oesterreichische Nationalbank (Austrian Central Bank).
    88. Brian D. Deaton, 2018. "Effects of the Swiss Franc/Euro Exchange Rate Floor on the Calibration of Probability Forecasts," Forecasting, MDPI, vol. 1(1), pages 1-23, May.
    89. Brahim Gaies, Khaled Guesmi, Thomas Porcher, Raphael Boroumand, 2020. "Financial instability and oil price fluctuations: evidence from oil exporting developing countries," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 17(1), pages 55-71, June.
    90. Dimitris P. Louzis, 2014. "Macroeconomic and credit forecasts in a small economy during crisis: A large Bayesian VAR approach," Working Papers 184, Bank of Greece.

  56. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Teresa Buchen & Klaus Wohlrabe, 2013. "Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany," CESifo Working Paper Series 4148, CESifo.
    2. Bell, Venetia & Co, Lai Wah & Stone, Sophie & Wallis, gavin`, 2014. "Nowcasting UK GDP growth," Bank of England Quarterly Bulletin, Bank of England, vol. 54(1), pages 58-68.
    3. Alexander Chudik & M. Hashem Pesaran, 2016. "Theory And Practice Of Gvar Modelling," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 165-197, February.
    4. Focardi, Sergio M. & Fabozzi, Frank J. & Mitov, Ivan K., 2016. "A new approach to statistical arbitrage: Strategies based on dynamic factor models of prices and their performance," Journal of Banking & Finance, Elsevier, vol. 65(C), pages 134-155.
    5. Wallis, Kenneth F., 2008. "Macroeconomic modelling in central banks in Latin America," Documentos de Proyectos 3627, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    6. Liu, Philip & Matheson, Troy & Romeu, Rafael, 2012. "Real-time forecasts of economic activity for Latin American economies," Economic Modelling, Elsevier, vol. 29(4), pages 1090-1098.
    7. Houcine Senoussi, 2021. "Inflation and Inflation Uncertainty in Growth Model of Barro: An Application of Random Forest Method," International Econometric Review (IER), Econometric Research Association, vol. 13(1), pages 4-23, March.
    8. Eirini Konstantinidi & Gkaren Papazian & George Skiadopoulos, 2015. "Modeling the Dynamics of Temperature with a View to Weather Derivatives," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 17, pages 511-544, World Scientific Publishing Co. Pte. Ltd..
    9. Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization Institute Working Papers 213, Federal Reserve Bank of Dallas.
    10. Renbo Liu & Yuhui Ge & Peng Zuo, 2023. "Study on Economic Data Forecasting Based on Hybrid Intelligent Model of Artificial Neural Network Optimized by Harris Hawks Optimization," Mathematics, MDPI, vol. 11(21), pages 1-28, November.
    11. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.

  57. Richard Harrison & George Kapetanios & Alasdair Scott & Jana Eklund, 2008. "Breaks in DSGE models," 2008 Meeting Papers 657, Society for Economic Dynamics.

    Cited by:

    1. Canova, Fabio, 2014. "Bridging DSGE models and the raw data," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 1-15.

  58. Andrea Cipollini & George Kapetanios, 2008. "Forecasting Financial Crises and Contagion in Asia using Dynamic Factor Analysis," Center for Economic Research (RECent) 014, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".

    Cited by:

    1. Hyeongwoo Kim & Kyunghwan Ko, 2019. "Improving Forecast Accuracy of Financial Vulnerability: PLS Factor Model Approach," Auburn Economics Working Paper Series auwp2019-03, Department of Economics, Auburn University.
    2. Priyadarshi Dash, 2017. "Predicting Financial Crises: A Study of Asian Economies," Global Business Review, International Management Institute, vol. 18(5), pages 1262-1277, October.
    3. Mustapha Djennas & Mohamed Benbouziane & Meriem Djennas, 2011. "An Approach of Combining Empirical Mode Decomposition and Neural Network Learning for Currency Crisis Forecasting," Working Papers 627, Economic Research Forum, revised 09 Jan 2011.
    4. Hyeongwoo Kim & Wen Shi & Hyun Hak Kim, 2020. "Forecasting financial stress indices in Korea: a factor model approach," Empirical Economics, Springer, vol. 59(6), pages 2859-2898, December.
    5. Corder, Matthew & Weale, Martin, 2011. "Banking crises and recessions: what can leading indicators tell us?," Discussion Papers 33, Monetary Policy Committee Unit, Bank of England.
    6. Hyeongwoo Kim & Kyunghwan Ko, 2017. "Improving Forecast Accuracy of Financial Vulnerability: Partial Least Squares Factor Model Approach," Working Papers 2017-14, Economic Research Institute, Bank of Korea.
    7. Hyeongwoo Kim & Jisoo Son, 2023. "What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors?," Auburn Economics Working Paper Series auwp2023-06, Department of Economics, Auburn University.
    8. Kim, Hyeongwoo & Shi, Wen, 2018. "Forecasting Financial Vulnerability in the US: A Factor Model Approach," MPRA Paper 89766, University Library of Munich, Germany.
    9. Hyeongwoo Kim & Jisoo Son, 2023. "Forecasting Net Charge-Off Rates of Large U.S. Bank Holding Companies using Macroeconomic Latent Factors," Auburn Economics Working Paper Series auwp2023-02, Department of Economics, Auburn University.
    10. Briceño Avalos, Hernán Ricardo, 2003. "Tipos de Cambio Cuasi-fijo y Posibilidad de Crisis Financieras: Solarizar o Dolarizar la Economía Peruana? [Fixed Exchange Rates and Possibilities of Financial Crisis: Solarizar or Dollarization of," MPRA Paper 42029, University Library of Munich, Germany.
    11. Rodrigo César de Castro Miranda & Benjamin Miranda Tabak & Mauricio Medeiros Junior, 2012. "Contagion in CDS, Banking and Equity Markets," Working Papers Series 293, Central Bank of Brazil, Research Department.
    12. Tjeerd M. Boonman & Jan P.A.M. Jacobs & Gerard H. Kuper, 2011. "Why didn't the Global Financial Crisis hit Latin America?," CIRANO Working Papers 2011s-63, CIRANO.
    13. Boonman, Tjeerd M. & Jacobs, Jan P.A.M. & Kuper, Gerard H., 2012. "The Global Financial Crisis and currency crises in Latin America," Research Report 12005-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).

  59. Camba-Méndez, Gonzalo & Kapetanios, George, 2008. "Statistical tests and estimators of the rank of a matrix and their applications in econometric modelling," Working Paper Series 850, European Central Bank.

    Cited by:

    1. Juodis, Arturas & Sarafidis, Vasilis, 2015. "A Simple Estimator for Short Panels with Common Factors," MPRA Paper 68164, University Library of Munich, Germany.
    2. De Vos, Ignace & Everaert, Gerdie & Sarafidis, Vasilis, 2021. "A method for evaluating the rank condition for CCE estimators," MPRA Paper 112305, University Library of Munich, Germany, revised 09 Mar 2022.
    3. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    4. Anyck Dauphin & Bernard Fortin & Guy Lacroix, 2018. "Is consumption efficiency within households falsifiable?," Review of Economics of the Household, Springer, vol. 16(3), pages 737-766, September.
    5. Ruli Xiao, 2016. "Nonparametric Identification of Dynamic Games with Multiple Equilibria and Unobserved Heterogeneity," CAEPR Working Papers 2016-002, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    6. Ruli Xiao, 2015. "Identification and Estimation of Incomplete Information Games with Multiple Equilibria," CAEPR Working Papers 2015-007, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    7. Al-Sadoon, Majid M., 2017. "A unifying theory of tests of rank," Journal of Econometrics, Elsevier, vol. 199(1), pages 49-62.
    8. Xiao, Ruli, 2018. "Identification and estimation of incomplete information games with multiple equilibria," Journal of Econometrics, Elsevier, vol. 203(2), pages 328-343.
    9. Erhao Xie, 2022. "Nonparametric Identification of Incomplete Information Discrete Games with Non-equilibrium Behaviors," Staff Working Papers 22-22, Bank of Canada.
    10. Andreou, Elena & Gagliardini, Patrick & Ghysels, Eric & Rubin, Mirco, 2017. "Is Industrial Production Still the Dominant Factor for the US Economy?," CEPR Discussion Papers 12219, C.E.P.R. Discussion Papers.
    11. Majid M. Al-Sadoon, 2014. "A general theory of rank testing," Economics Working Papers 1411, Department of Economics and Business, Universitat Pompeu Fabra, revised Feb 2015.
    12. Blake LeBaron, 2013. "Heterogeneous Agents and Long Horizon Features of Asset Prices," Working Papers 63, Brandeis University, Department of Economics and International Business School, revised Sep 2013.
    13. White, Halbert & Pettenuzzo, Davide, 2014. "Granger causality, exogeneity, cointegration, and economic policy analysis," Journal of Econometrics, Elsevier, vol. 178(P2), pages 316-330.
    14. Guay, Alain, 2021. "Identification of structural vector autoregressions through higher unconditional moments," Journal of Econometrics, Elsevier, vol. 225(1), pages 27-46.
    15. Luo, Yao & Xiao, Ping & Xiao, Ruli, 2022. "Identification of dynamic games with unobserved heterogeneity and multiple equilibria," Journal of Econometrics, Elsevier, vol. 226(2), pages 343-367.

  60. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2008. "Forecasting with Dynamic Models using Shrinkage-based Estimation," Working Papers 635, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Gustavo Fruet Dias & George Kapetanios, 2014. "Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets," CREATES Research Papers 2014-37, Department of Economics and Business Economics, Aarhus University.
    2. Emrah Oral & Gazanfer Unal, 2017. "Co-movement of precious metals and forecasting using scale by scale wavelet transform," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-21, March.

  61. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York.

    Cited by:

    1. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    2. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
    3. Pesenti, Paolo & Groen, Jan J. J., 2010. "Commodity prices, commodity currencies, and global economic developments," CEPR Discussion Papers 7689, C.E.P.R. Discussion Papers.
    4. Emmanuel C. Mamatzakis & Mike G. Tsionas, 2020. "Revealing forecaster's preferences: A Bayesian multivariate loss function approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 412-437, April.
    5. Hyeongwoo Kim & Kyunghwan Ko, 2019. "Improving Forecast Accuracy of Financial Vulnerability: PLS Factor Model Approach," Auburn Economics Working Paper Series auwp2019-03, Department of Economics, Auburn University.
    6. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    7. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.
    8. Alexander Chudik & M. Hashem Pesaran, 2016. "Theory And Practice Of Gvar Modelling," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 165-197, February.
    9. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic Forecast Accuracy in data-rich environment," Post-Print hal-02435757, HAL.
    10. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    11. Oguzhan Cepni, Duc Khuong Nguyen, and Ahmet Sensoy, 2022. "News Media and Attention Spillover across Energy Markets: A Powerful Predictor of Crude Oil Futures Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    12. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    13. Eickmeier, Sandra & Ng, Tim, 2011. "How do credit supply shocks propagate internationally? A GVAR approach," Discussion Paper Series 1: Economic Studies 2011,27, Deutsche Bundesbank.
    14. Hyeongwoo Kim & Kyunghwan Ko, 2017. "Improving Forecast Accuracy of Financial Vulnerability: Partial Least Squares Factor Model Approach," Working Papers 2017-14, Economic Research Institute, Bank of Korea.
    15. Barbarino, Alessandro & Bura, Efstathia, 2024. "Forecasting Near-equivalence of Linear Dimension Reduction Methods in Large Panels of Macro-variables," Econometrics and Statistics, Elsevier, vol. 31(C), pages 1-18.
    16. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    17. Mihnea Constantinescu, 2023. "Sparse Warcasting," Working Papers 01/2023, National Bank of Ukraine.
    18. Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
    19. Falk Brauning & Siem Jan Koopman, 2012. "Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis," Tinbergen Institute Discussion Papers 12-042/4, Tinbergen Institute.
    20. Duo Qin & Sophie van Huellen & Qing Chao Wang & Thanos Moraitis, 2022. "Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data," Econometrics, MDPI, vol. 10(2), pages 1-22, April.
    21. Sarthak Behera & Hyeongwoo Kim, 2019. "Forecasting Dollar Real Exchange Rates and the Role of Real Activity Factors," Auburn Economics Working Paper Series auwp2019-04, Department of Economics, Auburn University.
    22. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    23. Gianluca Cubadda & Alain Hecq, 2011. "Testing for common autocorrelation in data‐rich environments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 325-335, April.
    24. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    25. Jan J. J. Groen & George Kapetanios, 2009. "Model selection criteria for factor-augmented regressions," Staff Reports 363, Federal Reserve Bank of New York.
    26. Hyeongwoo Kim & Jisoo Son, 2023. "Forecasting Net Charge-Off Rates of Large U.S. Bank Holding Companies using Macroeconomic Latent Factors," Auburn Economics Working Paper Series auwp2023-02, Department of Economics, Auburn University.
    27. Scott A. Brave & R. Andrew Butters & David Kelley, 2019. "A New “Big Data” Index of U.S. Economic Activity," Economic Perspectives, Federal Reserve Bank of Chicago, issue 1, pages 1-30.
    28. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    29. Jan J. J. Groen & Michael Nattinger, 2020. "Alternative Indicators for Chinese Economic Activity Using Sparse PLS Regression," Economic Policy Review, Federal Reserve Bank of New York, vol. 26(4), pages 39-68, October.
    30. Adrian, Tobias & Etula, Erkko & Groen, Jan J.J., 2011. "Financial amplification of foreign exchange risk premia," European Economic Review, Elsevier, vol. 55(3), pages 354-370, April.
    31. Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
    32. Gianluca Cubadda & Barbara Guardabascio, 2017. "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model," CEIS Research Paper 397, Tor Vergata University, CEIS, revised 13 Jul 2018.
    33. Matteo Luciani, 2011. "Forecasting with Approximate Dynamic Factor Models: the Role of Non-Pervasive Shocks," Working Papers ECARES ECARES 2011‐022, ULB -- Universite Libre de Bruxelles.
    34. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2015. "Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 576-595, June.
    35. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2020. "Markov-Switching Three-Pass Regression Filter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 285-302, April.
    36. Hutchinson, Mark C. & Kyziropoulos, Panagiotis E. & O'Brien, John & O'Reilly, Philip & Sharma, Tripti, 2022. "Are carry, momentum and value still there in currencies?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    37. Eddie Casey, 2019. "Inside the "Upside Down": Estimating Ireland's Output Gap," The Economic and Social Review, Economic and Social Studies, vol. 50(1), pages 5-34.
    38. Tom Boot & Didier Nibbering, 2016. "Forecasting Using Random Subspace Methods," Tinbergen Institute Discussion Papers 16-073/III, Tinbergen Institute, revised 11 Aug 2017.
    39. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    40. Oguzhan Cepni & Rangan Gupta & I. Ethem Guney & M. Hasan Yilmaz, 2019. "Forecasting Local Currency Bond Risk Premia of Emerging Markets: The Role of Cross-Country Macro-Financial Linkages," Working Papers 201957, University of Pretoria, Department of Economics.
    41. Dalibor Stevanovic & Rachidi Kotchoni & Maxime Leroux, 2017. "Forecasting economic activity in data-rich environment," CIRANO Working Papers 2017s-05, CIRANO.
    42. Dendramis, Y. & Tzavalis, E. & Varthalitis, P. & Athanasiou, E., 2020. "Predicting default risk under asymmetric binary link functions," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1039-1056.
    43. Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization Institute Working Papers 213, Federal Reserve Bank of Dallas.
    44. Eliana González, 2011. "Forecasting With Many Predictors. An Empirical Comparison," Borradores de Economia 7996, Banco de la Republica.
    45. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    46. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
    47. Alessandro Barbarino & Efstathia Bura, 2017. "A Unified Framework for Dimension Reduction in Forecasting," Finance and Economics Discussion Series 2017-004, Board of Governors of the Federal Reserve System (U.S.).
    48. Wang, Xiangning & Zhao, Xing, 2014. "The invoicing currency choice model of export enterprises assuming joint utility maximization and analysis of the factors influencing selection," Economic Modelling, Elsevier, vol. 42(C), pages 38-42.
    49. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    50. Erik Christian Montes Schütte, 2018. "In Search of a Job: Forecasting Employment Growth in the US using Google Trends," CREATES Research Papers 2018-25, Department of Economics and Business Economics, Aarhus University.
    51. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    52. Alessandro Barbarino & Efstathia Bura, 2015. "Forecasting with Sufficient Dimension Reductions," Finance and Economics Discussion Series 2015-74, Board of Governors of the Federal Reserve System (U.S.).
    53. Duo Qin & Qingchao Wang, 2016. "Predictive Macro-Impacts of PLS-based Financial Conditions Indices: An Application to the USA," Working Papers 201, Department of Economics, SOAS University of London, UK.
    54. Biing-Shen Kuo & Su-Ling Peng, 2011. "Price Pass-Through, Household Expenditure, and Industrial Structure: The Case of Taiwan," NBER Chapters, in: Commodity Prices and Markets, pages 237-255, National Bureau of Economic Research, Inc.
    55. Matthew Pritsker, 2017. "Choosing Stress Scenarios for Systemic Risk Through Dimension Reduction," Supervisory Research and Analysis Working Papers RPA 17-4, Federal Reserve Bank of Boston.

  62. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2007. "Forecasting Large Datasets with Reduced Rank Multivariate Models," Working Papers 617, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
    2. Branimir Jovanovic & Magdalena Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," Working Papers 2010-02, National Bank of the Republic of North Macedonia, revised Aug 2010.
    3. Branimir, Jovanovic & Magdalena, Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," MPRA Paper 43162, University Library of Munich, Germany.

  63. Alastair Cunningham & Jana Eklund & Christopher Jeffery & George Kapetanios & Vincent Labhard, 2007. "A state space approach to extracting the signal from uncertain data," Bank of England working papers 336, Bank of England.

    Cited by:

    1. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.
    2. Philip Vermeulen, 2012. "Bank dependence and investment during the financial crisis," Research Bulletin, European Central Bank, vol. 17, pages 12-14.
    3. Naoko Hara & Hibiki Ichiue, 2010. "Real-time Analysis on Japan's Labor Productivity," Bank of Japan Working Paper Series 10-E-7, Bank of Japan.
    4. Anesti, Nikoleta & Galvão, Ana & Miranda-Agrippino, Silvia, 2018. "Uncertain Kingdom: nowcasting GDP and its revisions," Bank of England working papers 764, Bank of England, revised 31 Jan 2020.
    5. Dean Croushore, 2009. "Commentary on Estimating U.S. output growth with vintage data in a state-space framework," Review, Federal Reserve Bank of St. Louis, vol. 91(Jul), pages 371-382.
    6. Richard G. Anderson & Charles S. Gascon, 2009. "Estimating U.S. output growth with vintage data in a state-space framework," Review, Federal Reserve Bank of St. Louis, vol. 91(Jul), pages 349-370.
    7. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, University of Reading.
    8. Ronald Indergand & Stefan Leist, 2014. "A Real-Time Data Set for Switzerland," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 150(IV), pages 331-352, December.
    9. Kerry Patterson & Hossein Hassani & Saeed Heravi & Anatoly Zhigljavsky, 2011. "Multivariate singular spectrum analysis for forecasting revisions to real-time data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2183-2211.
    10. Billio, Monica & Donadelli, Michael & Paradiso, Antonio & Riedel, Max, 2016. "Which market integration measure?," SAFE Working Paper Series 159, Leibniz Institute for Financial Research SAFE.
    11. Michael P. Clements & Ana Beatriz Galvão, 2011. "Improving Real-time Estimates of Output Gaps and Inflation Trends with Multiple-vintage Models," Working Papers 678, Queen Mary University of London, School of Economics and Finance.
    12. George Kapetanios & Tony Yates, 2010. "Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 869-893.
    13. Valentina Raponi & Cecilia Frale, 2014. "Revisions in official data and forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 451-472, August.
    14. Clements, Michael P. & Galvão, Ana Beatriz, 2010. "Real-time Forecasting of Inflation and Output Growth in the Presence of Data Revisions," The Warwick Economics Research Paper Series (TWERPS) 953, University of Warwick, Department of Economics.
    15. Dungey, Mardi & Jacobs, Jan & Tian, Jing & Norden, Simon van, 2012. "On trend-cycle decomposition and data revision," Research Report 12009-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    16. Chiu Adrian & Wieladek Tomasz, 2013. "Is the “Great Recession” really so different from the past?," The B.E. Journal of Macroeconomics, De Gruyter, vol. 13(1), pages 1037-1084, October.
    17. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.
    18. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    19. Simone Manganelli, 2012. "The impact of the Securities Markets Programme," Research Bulletin, European Central Bank, vol. 17, pages 2-5.
    20. Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    21. Carriero, Andrea & Clements, Michael P. & Galvão, Ana Beatriz, 2015. "Forecasting with Bayesian multivariate vintage-based VARs," International Journal of Forecasting, Elsevier, vol. 31(3), pages 757-768.
    22. Xin Zhang & Bernd Schwaab & Andre Lucas, 2011. "Conditional Probabilities and Contagion Measures for Euro Area Sovereign Default Risk," Tinbergen Institute Discussion Papers 11-176/2/DSF29, Tinbergen Institute, revised 28 Jun 2012.
    23. Michael P. Clements, 2017. "Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 420-433, July.
    24. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.
    25. Jan P.A.M. Jacobs & Samad Sarferaz & Simon van Norden & Jan-Egbert Sturm, 2013. "Modeling Multivariate Data Revisions," CIRANO Working Papers 2013s-44, CIRANO.
    26. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.

  64. George Kapetanios & Andrew P. Blake, 2007. "Testing the Martingale Difference Hypothesis Using Neural Network Approximations," Working Papers 601, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Juan Carlos Escanciano & Silvia Mayoral, 2007. "Data-Driven Smooth Tests for the Martingale Difference Hypothesis," Faculty Working Papers 01/07, School of Economics and Business Administration, University of Navarra.

  65. George Kapetanios & Andrew P. Blake, 2007. "Boosting Estimation of RBF Neural Networks for Dependent Data," Working Papers 588, Queen Mary University of London, School of Economics and Finance.

    Cited by:

  66. Alastair Cunningham & Chris Jeffery & George Kapetanios & Vincent Labhard, 2007. "A State Space Approach To The Policymaker's Data Uncertainty Problem," Money Macro and Finance (MMF) Research Group Conference 2006 168, Money Macro and Finance Research Group.

    Cited by:

    1. Garratt, Anthony & Lee, Kevin & Mise, Emi & Shields, Kalvinder, 2009. "Real time representation of the UK output gap in the presence of model uncertainty," International Journal of Forecasting, Elsevier, vol. 25(1), pages 81-102.
    2. Seth Pruitt, 2012. "Uncertainty Over Models and Data: The Rise and Fall of American Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(2‐3), pages 341-365, March.

  67. George Kapetanios, 2007. "Testing for Strict Stationarity," Working Papers 602, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Hart, Jeffrey D., 2016. "A nonparametric test of stationarity for independent data," Statistics & Probability Letters, Elsevier, vol. 108(C), pages 40-44.
    2. Lorenzo Trapani, 2018. "Testing for strict stationarity in a random coefficient autoregressive model," Discussion Papers 18/02, University of Nottingham, Granger Centre for Time Series Econometrics.
    3. Kapetanios, George, 2009. "Testing for strict stationarity in financial variables," Journal of Banking & Finance, Elsevier, vol. 33(12), pages 2346-2362, December.

  68. George Kapetanios & Vincent Labhard & Simon Price, 2007. "Forecast combination and the Bank of England’s suite of statistical forecasting models," Bank of England working papers 323, Bank of England.

    Cited by:

    1. Marie Diron & Benoit Mojon, 2008. "Are inflation targets good inflation forecasts?," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 32(Q II), pages 33-45.
    2. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    3. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    4. João Henrique Gonçalves Mazzeu & Esther Ruiz & Helena Veiga, 2018. "Uncertainty And Density Forecasts Of Arma Models: Comparison Of Asymptotic, Bayesian, And Bootstrap Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 388-419, April.
    5. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2022. "Testing big data in a big crisis: Nowcasting under COVID-19," JRC Working Papers in Economics and Finance 2022-06, Joint Research Centre, European Commission.
    6. Sigal Ribon, 2011. "The Effect of Monetary Policy on Inflation: A Factor Augmented VAR Approach using disaggregated data," Bank of Israel Working Papers 2011.12, Bank of Israel.
    7. Chris Florakis & Gianluigi Giorgioni & Alexandros Kostakis & Costas Milas, 2012. "The Impact of Stock Market Illiquidity on Real UK GDP Growth," Working Paper series 65_12, Rimini Centre for Economic Analysis.
    8. Bell, Venetia & Co, Lai Wah & Stone, Sophie & Wallis, gavin`, 2014. "Nowcasting UK GDP growth," Bank of England Quarterly Bulletin, Bank of England, vol. 54(1), pages 58-68.
    9. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    10. Öğünç, Fethi & Akdoğan, Kurmaş & Başer, Selen & Chadwick, Meltem Gülenay & Ertuğ, Dilara & Hülagü, Timur & Kösem, Sevim & Özmen, Mustafa Utku & Tekatlı, Necati, 2013. "Short-term inflation forecasting models for Turkey and a forecast combination analysis," Economic Modelling, Elsevier, vol. 33(C), pages 312-325.
    11. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    12. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    13. Shahzad Ahmad & Farooq Pasha, 2015. "A Pragmatic Model for Monetary Policy Analysis I: The Case of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 1-42.
    14. Anthoulla Phella, 2020. "Forecasting With Factor-Augmented Quantile Autoregressions: A Model Averaging Approach," Papers 2010.12263, arXiv.org.
    15. Tony Chernis & Taylor Webley, 2022. "Nowcasting Canadian GDP with Density Combinations," Discussion Papers 2022-12, Bank of Canada.
    16. Selen Baser Andic & Fethi Ogunc, 2015. "Variable Selection for Inflation : A Pseudo Out-of-sample Approach," Working Papers 1506, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    17. Hilde C. Bjørnland & Karsten Gerdrup & Anne Sofie Jore & Christie Smith & Leif Anders Thorsrud, 2012. "Does Forecast Combination Improve Norges Bank Inflation Forecasts?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 163-179, April.
    18. Meri Papavangjeli, 2019. "Forecasting the Albanian short-term inflation through a Bayesian VAR model," IHEID Working Papers 16-2019, Economics Section, The Graduate Institute of International Studies, revised 09 Oct 2019.
    19. Eliana González & . Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model for the Colombian Inflation," Borradores de Economia 549, Banco de la Republica de Colombia.
    20. Michael K. Andersson & Sune Karlsson, 2008. "Bayesian forecast combination for VAR models," Advances in Econometrics, in: Bayesian Econometrics, pages 501-524, Emerald Group Publishing Limited.
    21. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    22. Fabio Bacchini & Cristina Brandimarte & Piero Crivelli & Roberta De Santis & Marco Fioramanti & Alessandro Girardi & Roberto Golinelli & Cecilia Jona-Lasinio & Massimo Mancini & Carmine Pappalardo & D, 2013. "Building the core of the Istat system of models for forecasting the Italian economy: MeMo-It," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(1), pages 17-45.
    23. Diron, Marie & Mojon, Benoît, 2005. "Forecasting the central bank's inflation objective is a good rule of thumb," Working Paper Series 564, European Central Bank.
    24. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    25. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    26. Juan Acosta & Beatrice Cherrier & François Claveau & Clément Fontan & Aurélien Goutsmedt & Francesco Sergi, 2023. "Six Decades of Economic Research at the Bank of England," Post-Print hal-03919394, HAL.
    27. Muhammad Nadim Hanif & Muhammad Jahanzeb Malik, 2015. "Evaluating the Performance of Inflation Forecasting Models of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 43-78.
    28. Goutsmedt, Aurélien & Sergi, Francesco & Cherrier, Beatrice & Claveau, François & Fontan, Clément & Acosta, Juan, 2023. "To change or not to change The evolution of forecasting models at the Bank of England," SocArXiv m2cet, Center for Open Science.
    29. Andrejs Bessonovs, 2015. "Suite of Latvia's GDP forecasting models," Working Papers 2015/01, Latvijas Banka.
    30. Katerina Arnostova & David Havrlant & Luboš Rùžièka & Peter Tóth, 2011. "Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(6), pages 566-583, December.
    31. Koop, Gary & Korobilis, Dimitris, 2009. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," SIRE Discussion Papers 2009-40, Scottish Institute for Research in Economics (SIRE).
    32. Adam Jêdrzejczyk, 2012. "Inflation forecasting using dynamic factor analysis. SAS 4GL programming approach," Working Papers 63, Department of Applied Econometrics, Warsaw School of Economics.
    33. Andersson, Michael K & Karlsson, Sune, 2007. "Bayesian forecast combination for VAR models," Working Paper Series 216, Sveriges Riksbank (Central Bank of Sweden).
    34. Boriss Siliverstovs, 2015. "Dissecting Models' Forecasting Performance," KOF Working papers 15-397, KOF Swiss Economic Institute, ETH Zurich.
    35. Anh Dinh Minh Nguyen, 2017. "U.K. Monetary Policy under Inflation Targeting," Bank of Lithuania Working Paper Series 41, Bank of Lithuania.
    36. Georgios Papadopoulos & Dionysios Chionis & Nikolaos P. Rachaniotis, 2018. "Macro-financial linkages during tranquil and crisis periods: evidence from stressed economies," Risk Management, Palgrave Macmillan, vol. 20(2), pages 142-166, May.
    37. Zhang, Xinyu & Wan, Alan T.K. & Zou, Guohua, 2013. "Model averaging by jackknife criterion in models with dependent data," Journal of Econometrics, Elsevier, vol. 174(2), pages 82-94.
    38. Reason Lesego Machete, 2011. "Early Warning with Calibrated and Sharper Probabilistic Forecasts," Papers 1112.6390, arXiv.org, revised Jan 2012.
    39. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
    40. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    41. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
    42. Li, Z. & Hurn, A.S. & Clements, A.E., 2017. "Forecasting quantiles of day-ahead electricity load," Energy Economics, Elsevier, vol. 67(C), pages 60-71.
    43. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.
    44. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    45. Christian Glocker & Serguei Kaniovski, 2020. "Macroeconometric Forecasting Using a Cluster of Dynamic Factor Models," WIFO Working Papers 614, WIFO.
    46. Fawcett, Nicholas & Koerber, Lena & Masolo, Riccardo & Waldron, Matthew, 2015. "Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis," Bank of England working papers 538, Bank of England.
    47. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.
    48. Kurmaş Akdoğan, 2015. "Asymmetric Behaviour of Inflation around the Target in Inflation-Targeting Countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 62(5), pages 486-504, November.
    49. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.
    50. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
    51. Domit, Sílvia & Monti, Francesca & Sokol, Andrej, 2019. "Forecasting the UK economy with a medium-scale Bayesian VAR," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1669-1678.
    52. Altavilla, Carlo & Ciccarelli, Matteo, 2010. "Evaluating the effect of monetary policy on unemployment with alternative inflation forecasts," Economic Modelling, Elsevier, vol. 27(1), pages 237-253, January.
    53. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute.
    54. Phella, Anthoulla & Gabriel, Vasco J. & Martins, Luis F., 2024. "Predicting tail risks and the evolution of temperatures," Energy Economics, Elsevier, vol. 131(C).
    55. Florackis, Chris & Giorgioni, Gianluigi & Kostakis, Alexandros & Milas, Costas, 2014. "On stock market illiquidity and real-time GDP growth," Journal of International Money and Finance, Elsevier, vol. 44(C), pages 210-229.
    56. Davor Kunovac, 2007. "Factor Model Forecasting of Inflation in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 31(4), pages 371-393.
    57. Arvydas Jadevicius & Brian Sloan & Andrew Brown, 2012. "Examination of property forecasting models - accuracy and its improvement through combination forecasting," ERES eres2012_082, European Real Estate Society (ERES).
    58. Fayyaz Hussain & Zafar Hayat, 2016. "Do Inflation Expectations Matter for Inflation Forecastability: Evidence from Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 55(3), pages 211-225.
    59. Chris Bloor, 2009. "The use of statistical forecasting models at the Reserve Bank of New Zealand," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 72, pages 21-26, June.
    60. Arvydas Jadevicius & Brian Sloan & Andrew Brown, 2013. "Property Market Modelling and Forecasting: A Case for Simplicity," ERES eres2013_10, European Real Estate Society (ERES).
    61. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.

  69. Chris Tsoukis & George Kapetanios & Joseph Pearlman, 2007. "The Elusive Persistence: Wage and Price Rigidities, the Phillips Curve, and Inflation Dynamics," Working Papers 619, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Thomas Hasenzagl & Filippo Pellegrino & Lucrezia Reichlin & Giovanni Ricco, 2022. "A Model of the Fed's View on Inflation," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 686-704, October.
    2. Christopher Tsoukis & Frederic Tournemaine, 2011. "Social Conflict, Growth And Factor Shares," Metroeconomica, Wiley Blackwell, vol. 62(2), pages 283-304, May.

  70. George Kapetanios & Vincent Labhard & Simon Price, 2006. "Forecasting Using Predictive Likelihood Model Averaging," Working Papers 567, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Aman Ullah & Huansha Wang, 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging," Econometrics, MDPI, vol. 1(2), pages 1-23, September.
    2. Binder, Michael & Lieberknecht, Philipp & Quintana, Jorge & Wieland, Volker, 2017. "Model uncertainty in macroeconomics: On the implications of financial frictions," IMFS Working Paper Series 114, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    3. Öğünç, Fethi & Akdoğan, Kurmaş & Başer, Selen & Chadwick, Meltem Gülenay & Ertuğ, Dilara & Hülagü, Timur & Kösem, Sevim & Özmen, Mustafa Utku & Tekatlı, Necati, 2013. "Short-term inflation forecasting models for Turkey and a forecast combination analysis," Economic Modelling, Elsevier, vol. 33(C), pages 312-325.
    4. Eliana González, 2010. "Bayesian Model Averaging. An Application to Forecast Inflation in Colombia," Borradores de Economia 7015, Banco de la Republica.
    5. Liudas Giraitis & George Kapetanios & Simon Price, 2012. "Adaptive Forcasting in the Presence of Recent and Ongoing Structural Change," CAMA Working Papers 2012-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Burgess, Stephen & Fernandez-Corugedo, Emilio & Groth, Charlotta & Harrison, Richard & Monti, Francesca & Theodoridis, Konstantinos & Waldron, Matt, 2013. "The Bank of England's forecasting platform: COMPASS, MAPS, EASE and the suite of models," Bank of England working papers 471, Bank of England.
    7. Florian Martin & Jesús Crespo Cuaresma, 2017. "Weighting schemes in global VAR modelling: a forecasting exercise," Letters in Spatial and Resource Sciences, Springer, vol. 10(1), pages 45-56, March.
    8. Leandro Maciel, 2012. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(3), pages 337-367.
    9. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
    10. Binder, Michael & Lieberknecht, Philipp & Quintana, Jorge & Wieland, Volker, 2018. "Robust Macroprudential Policy Rules under Model Uncertainty," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181503, Verein für Socialpolitik / German Economic Association.
    11. Luis F. Melo Velandia & Rubén A. Loaiza Maya & Mauricio Villamizar-Villegas, 2014. "Bayesian Combination for Inflation Forecasts: The Effects of a Prior Based on Central Banks’ Estimates," Borradores de Economia 853, Banco de la Republica de Colombia.
    12. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    13. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
    14. Goutsmedt, Aurélien & Sergi, Francesco & Cherrier, Beatrice & Claveau, François & Fontan, Clément & Acosta, Juan, 2023. "To change or not to change The evolution of forecasting models at the Bank of England," SocArXiv m2cet, Center for Open Science.
    15. Marie Bessec & Julien Fouquau & Sophie Méritet, 2014. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Post-Print hal-01502835, HAL.
    16. David Bolder & Yuliya Romanyuk, 2008. "Combining Canadian Interest-Rate Forecasts," Staff Working Papers 08-34, Bank of Canada.
    17. Ando, Tomohiro & Tsay, Ruey, 2010. "Predictive likelihood for Bayesian model selection and averaging," International Journal of Forecasting, Elsevier, vol. 26(4), pages 744-763, October.

  71. Georgios Chortareas & George Kapetanios, 2006. "The yen real exchange rate may be stationary after all: evidence from non-linear unit root tests," Bank of England working papers 311, Bank of England.

    Cited by:

    1. Dilem Yıldırım, 2016. "Empirical Investigation of Purchasing Power Parity for Turkey: Evidence from Recent Nonlinear Unit Root Tests," ERC Working Papers 1604, ERC - Economic Research Center, Middle East Technical University, revised Apr 2016.
    2. Mohsen Bahmani-Oskooee & Ali M. Kutan & Su Zhou, 2009. "A century of PPP: supportive results from nonlinear unit root tests," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 11(1), pages 19-27.
    3. McMillan, David G., 2009. "The confusing time-series behaviour of real exchange rates: Are asymmetries important?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(4), pages 692-711, October.
    4. Kuswanto, Heri & Sibbertsen, Philipp, 2009. "Testing for Long Memory Against ESTAR Nonlinearities," Hannover Economic Papers (HEP) dp-427, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    5. Mohsen Bahmani-Oskooee & Abera Gelan, 2006. "Testing the PPP in the non-linear STAR Framework: Evidence from Africa," Economics Bulletin, AccessEcon, vol. 6(17), pages 1-15.
    6. Kurmas Akdogan & Yunus Aksoy, 2007. "Exchange Rates and Fundamentals : Is there a Role for Nonlinearities in Real Time?," Working Papers 0703, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    7. Wu, Jyh-Lin & Lee, Hsiu-Yun, 2009. "A revisit to the non-linear mean reversion of real exchange rates: Evidence from a series-specific non-linear panel unit-root test," Journal of Macroeconomics, Elsevier, vol. 31(4), pages 591-601, December.
    8. Rod Tyers & Ying Zhang, 2014. "Real Exchange Rate Determination and the China Puzzle," Economics Discussion / Working Papers 14-19, The University of Western Australia, Department of Economics.
    9. Jean-François Hoarau, 2010. "Does long-run purchasing power parity hold in Eastern and Southern African countries? Evidence from panel data stationary tests with multiple structural breaks," Post-Print hal-01243461, HAL.
    10. Arize, Augustine C., 2011. "Purchasing power parity in LDCs: An empirical investigation," Global Finance Journal, Elsevier, vol. 22(1), pages 56-71.
    11. Bahmani-Oskooee, Mohsen & Tankui, Altin, 2008. "The black market exchange rate vs. the official rate in testing PPP: Which rate fosters the adjustment process?," Economics Letters, Elsevier, vol. 99(1), pages 40-43, April.
    12. Chortareas, Georgios & Kapetanios, George, 2009. "Getting PPP right: Identifying mean-reverting real exchange rates in panels," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 390-404, February.
    13. Mario Cerrato & Nick Sarantis, 2006. "Nonlinear Mean Reversion in Real Exchange Rates: Evidence from Developing and Emerging Market Economies," Economics Bulletin, AccessEcon, vol. 6(7), pages 1-14.
    14. Su Zhou & Mohsen Bahmani-Oskooee & Ali M. Kutan, 2008. "Purchasing Power Parity before and after the Adoption of the Euro," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 144(1), pages 134-150, April.
    15. Saadet Kasman & Adnan Kasman & Duygu Ayhan, 2010. "Testing the Purchasing Power Parity Hypothesis for the New Member and Candidate Countries of the European Union: Evidence from Lagrange Multiplier Unit Root Tests with Structural Breaks," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 46(2), pages 53-65, March.
    16. Su Zhou, 2012. "Is the stationarity of the yen real exchange rates a puzzle? This paper attempts to shed light on the puzzling finding that the results of applying the conventional or nonlinear unit root tests to the," Working Papers 0005, College of Business, University of Texas at San Antonio.
    17. Liew, Venus Khim-Sen & Baharumshah, Ahmad Zubaidi & Habibullah, Muzafar Shah & Midi, Habshah, 2008. "Monetary exchange rate model: supportive evidence from nonlinear testing procedures," MPRA Paper 7293, University Library of Munich, Germany.
    18. Aviral Tiwari & Muhammad Shahbaz, 2014. "Revisiting Purchasing Power Parity for India using threshold cointegration and nonlinear unit root test," Economic Change and Restructuring, Springer, vol. 47(2), pages 117-133, May.
    19. Paresh Kumar Narayan, 2005. "New evidence on purchasing power parity from 17 OECD countries," Applied Economics, Taylor & Francis Journals, vol. 37(9), pages 1063-1071.
    20. Bahmani-Oskooee, Mohsen & Kutan, Ali M. & Zhou, Su, 2007. "Testing PPP in the non-linear STAR framework," Economics Letters, Elsevier, vol. 94(1), pages 104-110, January.
    21. Arghyrou, Michael G. & Gregoriou, Andros, 2008. "Non-linearity versus non-normality in real exchange rate dynamics," Economics Letters, Elsevier, vol. 100(2), pages 200-203, August.
    22. Hunter, John & Menla Ali, Faek, 2014. "Money demand instability and real exchange rate persistence in the monetary model of USD–JPY exchange rate," Economic Modelling, Elsevier, vol. 40(C), pages 42-51.
    23. Paresh Kumar Narayan & Biman Chand Prasad, 2008. "Are shocks to real effective exchange rates permanent or transitory? Evidence from Pacific Island countries," Applied Economics, Taylor & Francis Journals, vol. 40(8), pages 1053-1060.
    24. Hyeongwoo Kim & Young-Kyu Moh, 2012. "The Yen Real Exchange Rate May Not Be Stationary After All: New Evidence from Non-linear Unit-Root Tests," Auburn Economics Working Paper Series auwp2012-02, Department of Economics, Auburn University.
    25. Mohsen Bahmani-Oskooee & Omid Ranjbar, 2016. "Quantile unit root test and PPP: evidence from 23 OECD countries," Applied Economics, Taylor & Francis Journals, vol. 48(31), pages 2899-2911, July.
    26. Yunus Aksoy & Miguel A. Leon-Ledesma, 2007. "Non-linearities and Unit Roots in G7 Macroeconomic Variables," Birkbeck Working Papers in Economics and Finance 0710, Birkbeck, Department of Economics, Mathematics & Statistics.
    27. Dimitris, Christopoulos & Miguel, Leon-Ledesma, 2009. "Smooth Breaks and Nonlinear Mean Reversion: Post-Bretton Woods Real Exchange Rates," MPRA Paper 22553, University Library of Munich, Germany.
    28. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    29. Narayan Paresh K & Prasad Biman Chand, 2005. "The Validity of Purchasing Power Parity Hypothesis for Eleven Middle Eastern Countries," Review of Middle East Economics and Finance, De Gruyter, vol. 3(2), pages 44-58, August.
    30. Mohsen Bahmani‐Oskooee & Ali M. Kutan & Su Zhou, 2008. "Do Real Exchange Rates Follow a Nonlinear Mean Reverting Process in Developing Countries?," Southern Economic Journal, John Wiley & Sons, vol. 74(4), pages 1049-1062, April.
    31. Mark J. Holmes, 2010. "Are Asia‐Pacific Real Exchange Rates Stationary? A Regime‐Switching Perspective," Pacific Economic Review, Wiley Blackwell, vol. 15(2), pages 189-203, May.
    32. Kapetanios, George, 2006. "Cluster analysis of panel data sets using non-standard optimisation of information criteria," Journal of Economic Dynamics and Control, Elsevier, vol. 30(8), pages 1389-1408, August.
    33. Daiki Maki, 2006. "Variance ratio tests for a unit root in the presence of a mean shift: small sample properties and an application to purchasing power parity," Applied Financial Economics, Taylor & Francis Journals, vol. 16(8), pages 607-615.
    34. Zhou, Su & Kutan, Ali M., 2011. "Is the evidence for PPP reliable? A sustainability examination of the stationarity of real exchange rates," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2479-2490, September.
    35. M.Abimbola OYINLOLA & Luwatosin ADENIYI & Nd Festus O.EGWAIKHIDE*, 2011. "Purchasing Power Parity Hypothesis in the Selected African Countries," Pakistan Journal of Applied Economics, Applied Economics Research Centre, vol. 21, pages 93-110.
    36. Grossmann, Axel & McMillan, David G., 2010. "Forecasting exchange rates: Non-linear adjustment and time-varying equilibrium," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(4), pages 436-450, October.
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    39. Jeff Hamrick & Murad Taqqu, 2009. "Testing diffusion processes for non-stationarity," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 509-551, July.
    40. Hoarau, Jean-François, 2009. "L’approche microéconomique du taux de change réel d’équilibre : une revue de la littérature théorique," L'Actualité Economique, Société Canadienne de Science Economique, vol. 85(4), pages 403-436, décembre.
    41. Mohsen Bahmani‐Oskooee & Scott W. Hegerty, 2009. "Purchasing Power Parity In Less‐Developed And Transition Economies: A Review Paper," Journal of Economic Surveys, Wiley Blackwell, vol. 23(4), pages 617-658, September.

  72. George Kapetanios & Zacharias Psaradakis, 2006. "Sieve Bootstrap for Strongly Dependent Stationary Processes," Working Papers 552, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    2. George Kapetanios & Fotis Papailias, 2011. "Block Bootstrap and Long Memory," Working Papers 679, Queen Mary University of London, School of Economics and Finance.
    3. Margherita Gerolimetto & Stefano Magrini, 2020. "Testing for boundary conditions in case of fractionally integrated processes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 357-371, June.
    4. Psaradakis, Zacharias & Vávra, Marián, 2017. "A distance test of normality for a wide class of stationary processes," Econometrics and Statistics, Elsevier, vol. 2(C), pages 50-60.
    5. Kim, Young Min & Nordman, Daniel J., 2013. "A frequency domain bootstrap for Whittle estimation under long-range dependence," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 405-420.

  73. Kapetanios, G. & Pesaran, M.H. & Yamagata, T., 2006. "Panels with Nonstationary Multifactor Error Structures," Cambridge Working Papers in Economics 0651, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Michele Manna & Stefano Nobili, 2018. "Banks' holdings of and trading in government bonds," Temi di discussione (Economic working papers) 1166, Bank of Italy, Economic Research and International Relations Area.
    2. Mastromarco, Camilla & Simar, Leopold, 2014. "Global Dependence and Productivity: A Robust Nonparametric World Frontier Analysis," LIDAM Discussion Papers ISBA 2014049, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Eberhardt, Markus & Helmers, Christian & Strauss, Hubert, 2010. "Do spillovers matter when estimating private returns to R&D?," Economic and Financial Reports 2010/1, European Investment Bank, Economics Department.
    4. Valerio Della Corte & Claire Giordano, 2021. "Methodological issues in the estimation of current account imbalances," Questioni di Economia e Finanza (Occasional Papers) 617, Bank of Italy, Economic Research and International Relations Area.
    5. Christos Bouras & Christina Christou & Rangan Gupta & Keagile Lesame, 2020. "Forecasting State- and MSA-Level Housing Returns of the US: The Role of Mortgage Default Risks," Working Papers 202037, University of Pretoria, Department of Economics.
    6. Fuertes, Ana-Maria, 2008. "Sieve bootstrap t-tests on long-run average parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3354-3370, March.
    7. Jia Chen & Yongcheol Shin & Chaowen Zheng, 2020. "Estimation and Inference in Heterogeneous Spatial Panel Data Models with a Multifactor Error Structure," Discussion Papers 20/03, Department of Economics, University of York.
    8. Matteo Barigozzi & Lorenzo Trapani, 2018. "Determining the dimension of factor structures in non-stationary large datasets," Papers 1806.03647, arXiv.org.
    9. Christou, Christina & Gupta, Rangan, 2020. "Forecasting equity premium in a panel of OECD countries: The role of economic policy uncertainty," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 243-248.
    10. Dreger, Christian & Herzer, Dierk, 2011. "A further examination of the export-led growth hypothesis," Discussion Papers 305, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    11. Ioannis Bournakis & Dimitris Christopoulos & Sushanta Mallick, 2018. "Knowledge Spillovers And Output Per Worker: An Industry‐Level Analysis For Oecd Countries," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 1028-1046, April.
    12. Mastromarco, Camilla & Simar, Leopold, 2018. "Globalization and productivity: A robust nonparametric world frontier analysis," LIDAM Reprints ISBA 2018008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    13. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross-Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Post-Print hal-03539371, HAL.
    14. Mantobaye Moundigbaye & William Rea & W. Robert Reed, 2016. "More Evidence On “Which Panel Data Estimator Should I Use?”," Working Papers in Economics 16/18, University of Canterbury, Department of Economics and Finance.
    15. Anran Xiao & Zeshui Xu & Marinko Skare & Yong Qin & Xinxin Wang, 2024. "Bridging the digital divide: the impact of technological innovation on income inequality and human interactions," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    16. Dong-Hyeon Kim & Shu-Chin Lin, 2017. "Natural Resources and Economic Development: New Panel Evidence," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 66(2), pages 363-391, February.
    17. Namahoro, J.P. & Nzabanita, J. & Wu, Q., 2021. "The impact of total and renewable energy consumption on economic growth in lower and middle- and upper-middle-income groups: Evidence from CS-DL and CCEMG analysis," Energy, Elsevier, vol. 237(C).
    18. Abrams M.E. Tagem, 2017. "The economics and politics of foreign aid and domestic revenue," WIDER Working Paper Series wp-2017-180, World Institute for Development Economic Research (UNU-WIDER).
    19. Eberhardt, Markus & Presbitero, Andrea F., 2015. "Public debt and growth: Heterogeneity and non-linearity," Journal of International Economics, Elsevier, vol. 97(1), pages 45-58.
    20. Rafaty, R. & Dolphin, G. & Pretis, F., 2020. "Carbon pricing and the elasticity of CO2 emissions," Cambridge Working Papers in Economics 20116, Faculty of Economics, University of Cambridge.
    21. Gioldasis, Georgios & Musolesi, Antonio & Simioni, Michel, 2023. "Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection," International Journal of Forecasting, Elsevier, vol. 39(1), pages 144-169.
    22. Akihiko Kawaura & Sumner La Croix, 2016. "Integration Of North And South American Players In Japan'S Professional Baseball Leagues," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57(3), pages 1107-1130, August.
    23. Saldías, Martín, 2013. "A market-based approach to sector risk determinants and transmission in the euro area," Working Paper Series 1574, European Central Bank.
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    25. Olufemi Adewale Aluko & George S. Chen & Eric Evans Osei Opoku, 2023. "Is foreign direct investment globalization‐induced or a myth? A tale of Africa," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 2651-2663, July.
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    27. Kamiar Mohaddes & Mehdi Raissi, 2017. "Do sovereign wealth funds dampen the negative effects of commodity price volatility?," CAMA Working Papers 2017-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
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    30. Usman, Muhammad & Makhdum, Muhammad Sohail Amjad, 2021. "What abates ecological footprint in BRICS-T region? Exploring the influence of renewable energy, non-renewable energy, agriculture, forest area and financial development," Renewable Energy, Elsevier, vol. 179(C), pages 12-28.
    31. Herrera Gomez Marcos & Fernández Pablo, 2023. "Regresiones SUR Espaciales. Análisis espacio-temporal del empleo sectorial en Argentina," Asociación Argentina de Economía Política: Working Papers 4660, Asociación Argentina de Economía Política.
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    35. Selien De Schryder and Gert Peersman, 2015. "The U.S. Dollar Exchange Rate and the Demand for Oil," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
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    37. Bertoli, Simone & Fernández-Huertas Moraga, Jesús, 2013. "Multilateral resistance to migration," Journal of Development Economics, Elsevier, vol. 102(C), pages 79-100.
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    42. Yunus Emre Ergemen, 2016. "System Estimation of Panel Data Models under Long-Range Dependence," CREATES Research Papers 2016-02, Department of Economics and Business Economics, Aarhus University.
    43. Kapetanios, George & Price, Simon & Tasiou, Menelaos & Ventouri, Alexia, 2021. "State-level wage Phillips curves," Econometrics and Statistics, Elsevier, vol. 18(C), pages 1-11.
    44. Shahbaz, Muhammad & Destek, Mehmet Akif & Okumus, Ilyas & Sinha, Avik, 2019. "An empirical note on comparison between resource abundance and resource dependence in resource abundant countries," Resources Policy, Elsevier, vol. 60(C), pages 47-55.
    45. Nin Pratt, Alejandro & Falconi, César & Ludeña, Carlos E. & Martel, Pedro, 2015. "Productivity and the Performance of Agriculture in Latin America and the Caribbean: From the Lost Decade to the Commodity Boom," IDB Publications (Working Papers) 7306, Inter-American Development Bank.
    46. Bai, Jushan & Carrion-i-Silvestre, Josep Lluis, 2009. "Testing Panel Cointegration with Unobservable Dynamic Common Factors," MPRA Paper 35243, University Library of Munich, Germany.
    47. Olivier Damette & Mathilde Maurel & Michael A. Stemmer, 2016. "What does it take to grow out of recession? An error-correction approach towards growth convergence of European and transition countries," Documents de travail du Centre d'Economie de la Sorbonne 16041, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    48. Jamel Saadaoui, 2013. "Global Imbalances: Should We Use Fundamental Equilibrium Exchange Rates," Working Papers of BETA 2013-14, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    49. Olympia Frogoudaki & Eleni Sardianou & Konstadinos Abeliotis & Ioannis Kostakis, 2024. "Assessing Determinants of E-waste in Eurozone Countries: An Empirical Study," Circular Economy and Sustainability, Springer, vol. 4(3), pages 2221-2240, September.
    50. Alexander Chudik & Roland Straub, 2017. "Size, Openness, And Macroeconomic Interdependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 58(1), pages 33-55, February.
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    53. Peter Fuleky & L Ventura & Qianxue Zhao, 2013. "Common correlated effects and international risk sharing," Working Papers 201304, University of Hawaii at Manoa, Department of Economics.
    54. Gerdie Everaert & Hauke Vierke, 2016. "Demographics and Business Cycle Volatility: A Spurious Relationship?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1467-1477, November.
    55. Ciccarelli, Carlo & Elhorst, J.Paul, 2018. "A dynamic spatial econometric diffusion model with common factors: The rise and spread of cigarette consumption in Italy," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 131-142.
    56. Gerdie Everaert & Freddy Heylen & Ruben Schoonackers, 2014. "Fiscal policy and TFP in the OECD : Measuring direct and indirect effects," Working Paper Research 274, National Bank of Belgium.
    57. Posso, Alberto & Zhang, Quanda, 2023. "Social R&D: Does academic freedom contribute to improved societal outcomes?," Information Economics and Policy, Elsevier, vol. 63(C).
    58. Esteban Lafuente & Zoltan J. Acs & Mark Sanders & László Szerb, 2020. "The global technology frontier: productivity growth and the relevance of Kirznerian and Schumpeterian entrepreneurship," Small Business Economics, Springer, vol. 55(1), pages 153-178, June.
    59. George Kapetanios & Laura Serlenga & Yongcheol Shin, 2019. "Testing for Correlated Factor Loadings in Cross Sectionally Dependent Panels," SERIES 02-2019, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Jun 2019.
    60. Sofien Tiba & Fateh Belaid, 2020. "The pollution concern in the era of globalization: Do the contribution of foreign direct investment and trade openness matter?," Post-Print hal-03271491, HAL.
    61. Apergis, Nicholas, 2019. "The impact of fracking activities on Oklahoma's housing prices: A panel cointegration analysis," Energy Policy, Elsevier, vol. 128(C), pages 94-101.
    62. Carlos Vladimir Rodríguez-Caballero, 2016. "Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure," CREATES Research Papers 2016-31, Department of Economics and Business Economics, Aarhus University.
    63. Bright A. Gyamfi & Asiedu B. Ampomah & Festus V. Bekun & Simplice A. Asongu, 2022. "Can Information and Communication Technology and Institutional Quality help mitigate climate change in E7 economies? An Environmental Kuznets Curve extension," Working Papers 22/052, European Xtramile Centre of African Studies (EXCAS).
    64. Dong, Kangyin & Hochman, Gal & Zhang, Yaqing & Sun, Renjin & Li, Hui & Liao, Hua, 2018. "CO2 emissions, economic and population growth, and renewable energy: Empirical evidence across regions," Energy Economics, Elsevier, vol. 75(C), pages 180-192.
    65. Alper Aslan & Ebru Topcu, 2018. "The Relationship between Export and Growth: Panel Data Evidence from Turkish Sectors," Economies, MDPI, vol. 6(2), pages 1-15, April.
    66. Sarafidis, Vasilis & Wansbeek, Tom, 2010. "Cross-sectional Dependence in Panel Data Analysis," MPRA Paper 20367, University Library of Munich, Germany.
    67. Ricardo Bebczuk & Tamara Burdisso & Máximo Sangiácomo, 2013. "Credit vs. Payment Services: Financial Development and Economic Activity Revisited," Department of Economics, Working Papers 097, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata.
    68. Chudik, Alexander & Pesaran, M. Hashem, 2015. "Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors," Journal of Econometrics, Elsevier, vol. 188(2), pages 393-420.
    69. Kostakis, Ioannis & Arauzo-Carod, Josep-Maria, 2023. "The key roles of renewable energy and economic growth in disaggregated environmental degradation: Evidence from highly developed, heterogeneous and cross-correlated countries," Renewable Energy, Elsevier, vol. 206(C), pages 1315-1325.
    70. Chan, Mark K. & Kwok, Simon, 2016. "Policy Evaluation with Interactive Fixed Effects," Working Papers 2016-11, University of Sydney, School of Economics.
    71. Markus Eberhardt & Francis Teal, 2010. "Productivity Analysis in Global Manufacturing Production," DEGIT Conference Papers c015_019, DEGIT, Dynamics, Economic Growth, and International Trade.
    72. Miao, Nana & Sharif, Arshian & Ozturk, Ilhan & Razzaq, Asif, 2023. "How do the exploitation of natural resources and fiscal policy affect green growth? Moderating role of ecological governance in G7 countries," Resources Policy, Elsevier, vol. 85(PA).
    73. Shingal, ANIRUDH, 2010. "Services growth and convergence: Getting India’s states together," MPRA Paper 32813, University Library of Munich, Germany.
    74. Binder, Michael & Offermanns, Christian J., 2014. "International investment positions and exchange rate dynamics," Discussion Papers 2014/23, Free University Berlin, School of Business & Economics.
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    77. John Lewis & Jumana Saleheen, 2018. "Tailwinds from the East: how has the rising share of imports from emerging markets affected import prices?," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 42(5), pages 1343-1365.
    78. Baumann, Ursel & Albuquerque, Bruno & Krustev, Georgi, 2014. "Has US household deleveraging ended? a model-based estimate of equilibrium debt," Working Paper Series 1643, European Central Bank.
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  74. George Kapetanios & Elias Tzavalis, 2006. "Stochastic Volatility Driven by Large Shocks," Working Papers 568, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Kapetanios, George, 2009. "Testing for strict stationarity in financial variables," Journal of Banking & Finance, Elsevier, vol. 33(12), pages 2346-2362, December.

  75. Richard T. Baillie & George Kapetanios, 2006. "Nonlinear Models with Strongly Dependent Processes and Applications to Forward Premia and Real Exchange Rates," Working Papers 570, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Thabo M. Mokoena & Rangan Gupta & Reneé Van Eyden, 2009. "Testing For Fractional Integration In Southern African Development Community Real Exchange Rates," South African Journal of Economics, Economic Society of South Africa, vol. 77(4), pages 531-537, December.

  76. Marcellino, Massimiliano & Kapetanios, George, 2006. "A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions," CEPR Discussion Papers 5620, C.E.P.R. Discussion Papers.

    Cited by:

    1. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    2. In Choi & Jorg Breitung, 2011. "Factor models," Working Papers 1121, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Dec 2011.
    3. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.
    4. Eickmeier, Sandra, 2006. "Comovements and heterogeneity in the Comovements and heterogeneity in the dynamic factor model," Discussion Paper Series 1: Economic Studies 2006,31, Deutsche Bundesbank.
    5. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    6. Dominique Guégan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Economics Bulletin, AccessEcon, vol. 30(1), pages 508-518.
    7. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    8. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate Pass-Through in the Major European Economies - The Role of Expectations," Discussion Papers 10/03, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    9. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2009. "GDP nowcasting with ragged-edge data : A semi-parametric modelling," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00344839, HAL.
    10. Shinya Tanaka & Eiji Kurozumi, 2010. "Investigating Finite Sample Properties of Estimators for Approximate Factor Models When N Is Small," Global COE Hi-Stat Discussion Paper Series gd10-156, Institute of Economic Research, Hitotsubashi University.
    11. Alain Kabundi & Francisco Nadal De Simone, 2011. "France in the global economy: a structural approximate dynamic factor model analysis," Empirical Economics, Springer, vol. 41(2), pages 311-342, October.
    12. Mario Forni & Alessandro Giovannelli & Marco Lippi & Stefano Soccorsi, 2016. "Dynamic Factor Model with Infinite Dimensional Factor Space: Forecasting," Working Papers ECARES ECARES 2016-16, ULB -- Universite Libre de Bruxelles.
    13. Hofmann, Boris, 2009. "Do monetary indicators lead euro area inflation?," Journal of International Money and Finance, Elsevier, vol. 28(7), pages 1165-1181, November.
    14. Rangan Gupta & Alan Kabundi & Stephen M. Miller, 2010. "Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals," Working Papers 1001, University of Nevada, Las Vegas , Department of Economics.
    15. Alain Kabundi & Rangan Gupta, 2009. "A Large Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 137, Economic Research Southern Africa.
    16. Falk Brauning & Siem Jan Koopman, 2012. "Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis," Tinbergen Institute Discussion Papers 12-042/4, Tinbergen Institute.
    17. Forni, Mario & Di Bonaventura, Luca & Pattarin, Francesco, 2018. "The Forcasting Performance of Dynamic Factor Models with Vintage Data," CEPR Discussion Papers 13034, C.E.P.R. Discussion Papers.
    18. Mario Forni & Filippo Altissimo & Riccardo Cristadoro & Marco Lippi & Giovanni Veronese., 2008. "New Eurocoin: Tracking Economic Growth in Real Time," Center for Economic Research (RECent) 020, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    19. Marcellino, Massimiliano & Kapetanios, George, 2010. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," CEPR Discussion Papers 7726, C.E.P.R. Discussion Papers.
    20. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2013. "How Do Anticipated Changes to Short-Term Market Rates Influence Banks' Retail Interest Rates? Evidence from the Four Major Euro Area Economies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(7), pages 1375-1414, October.
    21. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    22. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    23. Boriss Siliverstovs & Konstantin A. Kholodilin, 2006. "On Selection of Components for a Diffusion Index Model: It's not the Size, It's How You Use It," Discussion Papers of DIW Berlin 598, DIW Berlin, German Institute for Economic Research.
    24. Oliver Hülsewig & Johannes Mayr & Timo Wollmershäuser, 2008. "Forecasting Euro Area Real GDP: Optimal Pooling of Information," CESifo Working Paper Series 2371, CESifo.
    25. Camba-Méndez, Gonzalo & Kapetanios, George, 2004. "Forecasting euro area inflation using dynamic factor measures of underlying inflation," Working Paper Series 402, European Central Bank.
    26. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    27. B. Jungbacker & S.J. Koopman & M. van Der Wel, 2011. "Maximum likelihood estimation for dynamic factor models with missing data," Post-Print hal-00828980, HAL.
    28. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2009. "GDP nowcasting with ragged-edge data : A semi-parametric modelling," Post-Print halshs-00344839, HAL.
    29. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Post-Print halshs-00460461, HAL.
    30. Sandra Eickmeier & Joerg Breitung, 2006. "Business cycle transmission from the euro area to CEECs," Computing in Economics and Finance 2006 229, Society for Computational Economics.
    31. Ali Babikir & Henry Mwambi, 2016. "Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods," Empirical Economics, Springer, vol. 51(4), pages 1541-1556, December.
    32. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
    33. Dimitar EFTIMOSKI, 2019. "Improving Short-Term Forecasting of Macedonian GDP: Comparing the Factor Model with the Macroeconomic Structural Equation Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 32-53, June.
    34. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
    35. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    36. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    37. In Choi, 2007. "Efficient Estimation of Factor Models," Working Papers 0701, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Dec 2010.
    38. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    39. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    40. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00511979, HAL.
    41. Heather Anderson & Fashid Vahid, 2005. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," ANU Working Papers in Economics and Econometrics 2005-451, Australian National University, College of Business and Economics, School of Economics.
    42. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," PSE-Ecole d'économie de Paris (Postprint) halshs-00460472, HAL.
    43. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    44. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute.
    45. Eliana González, 2011. "Forecasting With Many Predictors. An Empirical Comparison," Borradores de Economia 7996, Banco de la Republica.
    46. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Post-Print halshs-00460472, HAL.
    47. Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
    48. Tjeerd M. Boonman & Jan P.A.M. Jacobs & Gerard H. Kuper, 2011. "Why didn't the Global Financial Crisis hit Latin America?," CIRANO Working Papers 2011s-63, CIRANO.
    49. Tjeerd M. Boonman & Andrea E. Sanchez Urbina, 2020. "Extreme Bounds Analysis in Early Warning Systems for Currency Crises," Open Economies Review, Springer, vol. 31(2), pages 431-470, April.
    50. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.
    51. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A short note on the nowcasting and the forecasting of Euro-area GDP using non-parametric techniques," Post-Print halshs-00461711, HAL.
    52. Bulligan, Guido & Marcellino, Massimiliano & Venditti, Fabrizio, 2015. "Forecasting economic activity with targeted predictors," International Journal of Forecasting, Elsevier, vol. 31(1), pages 188-206.
    53. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," PSE-Ecole d'économie de Paris (Postprint) halshs-00511979, HAL.
    54. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00505165, HAL.
    55. Dominique Guegan & Patrick Rakotomarolahy, 2009. "The Multivariate k-Nearest Neighbor Model for Dependent Variables : One-Sided Estimation and Forecasting," Post-Print halshs-00423871, HAL.
    56. B. Jungbacker & S.J. Koopman & M. van der Wel, 2009. "Dynamic Factor Analysis in The Presence of Missing Data," Tinbergen Institute Discussion Papers 09-010/4, Tinbergen Institute, revised 11 Mar 2011.

  77. Massimiliano Marcellino & George Kapetanios, 2006. "The Role of Search Frictions and Bargaining for Inflation Dynamics," Working Papers 305, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Burkhard Heer & Alfred Maussner, 2010. "Inflation and Output Dynamics in a Model with Labor Market Search and Capital Accumulation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 13(3), pages 654-686, July.
    2. Kai Christoffel & James Costain & Keith Kuester & Tobias Linzert & Stephen Millard & Olivier Pierrard, 2009. "Inflation dynamics with labour market matching: assessing alternative specifications," Working Papers 09-6, Federal Reserve Bank of Philadelphia.
    3. Richard W P Holt, 2007. "Job Reallocation, Unemployment and Hours in a New Keynesian Model," Edinburgh School of Economics Discussion Paper Series 172, Edinburgh School of Economics, University of Edinburgh.
    4. Abo-Zaid, Salem, 2013. "Optimal monetary policy and downward nominal wage rigidity in frictional labor markets," Journal of Economic Dynamics and Control, Elsevier, vol. 37(1), pages 345-364.

  78. Marcellino, Massimiliano & Kapetanios, George, 2006. "Impulse Response Functions from Structural Dynamic Factor Models: A Monte Carlo Evaluation," CEPR Discussion Papers 5621, C.E.P.R. Discussion Papers.

    Cited by:

    1. Christian Schulz, 2007. "Forecasting economic growth for Estonia : application of common factor methodologies," Bank of Estonia Working Papers 2007-09, Bank of Estonia, revised 04 Sep 2007.
    2. Marcellino, Massimiliano & Kapetanios, George, 2010. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," CEPR Discussion Papers 7726, C.E.P.R. Discussion Papers.
    3. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.

  79. George Kapetanios, 2005. "Cluster Analysis of Panel Datasets using Non-Standard Optimisation of Information Criteria," Working Papers 535, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Johan Blomquist & Joakim Westerlund, 2016. "Panel bootstrap tests of slope homogeneity," Empirical Economics, Springer, vol. 50(4), pages 1359-1381, June.
    2. Bottasso, Anna & Conti, Maurizio & Piacenz, Massimiliano & Vannoni, Davide, 2011. "The appropriateness of the poolability assumption for multiproduct technologies: Evidence from the English water and sewerage utilities," International Journal of Production Economics, Elsevier, vol. 130(1), pages 112-117, March.

  80. Georgios Chortareas & George Kapetanios, 2005. "How Puzzling is the PPP Puzzle? An Alternative Half-Life Measure of convergence to PPP," Money Macro and Finance (MMF) Research Group Conference 2005 36, Money Macro and Finance Research Group.

    Cited by:

    1. Qian Chen & David E. Giles, 2007. "A Saddlepoint Approximation to the Distribution of the Half-Life Estimator in an Autoregressive Model: New Insights Into the PPP Puzzle," Econometrics Working Papers 0703, Department of Economics, University of Victoria.
    2. Thabo M. Mokoena & Gupta, R. & Van Eyden, R., 2009. "Half-Life Deviations from PPP in the South African Development Community (SADC)," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 9(1).
    3. Marcel Aloy & Mohamed Boutahar & Karine Gente & Anne Peguin-Feissolle, 2011. "Purchasing power parity and the long memory properties of real exchange rates: does one size fit all?," Working Papers halshs-00559170, HAL.
    4. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2022. "Globalization, long memory, and real interest rate convergence: a historical perspective," Empirical Economics, Springer, vol. 63(5), pages 2331-2355, November.
    5. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas F. Quising, 2006. "Measuring Regional Market Integration by Dynamic Factor Error Correction Model (DF-ECM) Approach - The Case of Developing Asia," Working Papers 565, Queen Mary University of London, School of Economics and Finance.
    6. Venetis, Ioannis A. & Paya, Ivan & Peel, David A., 2007. "Deterministic impulse response in a nonlinear model. An analytical expression," Economics Letters, Elsevier, vol. 95(3), pages 315-319, June.
    7. Firouz Fallahi, 2020. "Persistence and unit root in $$\text {CO}_{2}$$CO2 emissions: evidence from disaggregated global and regional data," Empirical Economics, Springer, vol. 58(5), pages 2155-2179, May.
    8. Adler, Gustavo & Lisack, Noëmie & Mano, Rui C., 2019. "Unveiling the effects of foreign exchange intervention: A panel approach," Emerging Markets Review, Elsevier, vol. 40(C), pages 1-1.
    9. Hwa-Taek Lee & Gawon Yoon, 2013. "Does purchasing power parity hold sometimes? Regime switching in real exchange rates," Applied Economics, Taylor & Francis Journals, vol. 45(16), pages 2279-2294, June.
    10. Zhang, Zhibai, 2015. "Convergence of absolute purchasing power parity," MPRA Paper 64486, University Library of Munich, Germany.
    11. Rachel Male, 2010. "Business Cycle Persistence in Developing Countries: How Successful is a DSGE Model with a Vertical Production Chain and Sticky Prices?," Working Papers 672, Queen Mary University of London, School of Economics and Finance.
    12. Baltas, Konstantinos N. & Kapetanios, George & Tsionas, Efthymios & Izzeldin, Marwan, 2017. "Liquidity creation through efficient M&As: A viable solution for vulnerable banking systems? Evidence from a stress test under a panel VAR methodology," Journal of Banking & Finance, Elsevier, vol. 83(C), pages 36-56.
    13. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas F., 2007. "Measuring Regional Market Integration in Developing Asia: a Dynamic Factor Error Correction Model (DF-ECM) Approach," Working Papers on Regional Economic Integration 8, Asian Development Bank.
    14. Tm Mokoena, 2007. "Taking The Puzzle Out Of The Purchasing Power Parity Puzzle: An Application In Respect Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 75(1), pages 22-34, March.

  81. George Kapetanios, 2005. "Estimating Deterministically Time-Varying Variances in Regression Models," Working Papers 540, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Liudas Giraitis & George Kapetanios & Simon Price, 2012. "Adaptive Forcasting in the Presence of Recent and Ongoing Structural Change," CAMA Working Papers 2012-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. George Kapetanios & Tony Yates, 2014. "Evolving UK and US macroeconomic dynamics through the lens of a model of deterministic structural change," Empirical Economics, Springer, vol. 47(1), pages 305-345, August.
    3. Henrik Jensen & Ivan Petrella & Søren Hove Ravn & Emiliano Santoro, 2020. "Leverage and Deepening Business-Cycle Skewness," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(1), pages 245-281, January.
    4. Ferreira García, María Eva & Gil Bazo, Javier & Orbe Mandaluniz, Susan, 2010. "Conditional beta pricing models: A nonparametric approach," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    5. Jana Eklund & George Kapetanios & Simon Price, 2011. "Forecasting in the presence of recent structural change," CAMA Working Papers 2011-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Ferreira, Eva & Gil-Bazo, Javier & Orbe, Susan, 2008. "Nonparametric estimation of conditional beta pricing models," DEE - Working Papers. Business Economics. WB wb082403, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    7. George Kapetanios, 2005. "Tests for Deterministic Parametric Structural Change in Regression Models," Working Papers 539, Queen Mary University of London, School of Economics and Finance.
    8. Ferreira, Eva & Gil-Bazo, Javier & Orbe, Susan, 2011. "Conditional beta pricing models: A nonparametric approach," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3362-3382.

  82. G. Kapetanios & A. Pagan & A. Scott, 2005. "Making a match: Combining theory and evidence in policy-oriented macroeconomic modelling," CAMA Working Papers 2005-01, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Mala Raghavan & George Athanasopoulos & Param Silvapulle, 2016. "Canadian monetary policy analysis using a structural VARMA model," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(1), pages 347-373, February.
    2. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2011. "Oil prices, exchange rates and emerging stock markets," MPRA Paper 30140, University Library of Munich, Germany.
    3. Adrian Pagan & Tim Robinson, 2019. "Implications of Partial Information for Applied Macroeconomic Modelling," Melbourne Institute Working Paper Series wp2019n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    4. Martin Fukac & Adrian Pagan, 2009. "Structural Macro-Econometric Modelling in a Policy Environment," NCER Working Paper Series 50, National Centre for Econometric Research.
    5. Raghavan, Mala & Athanasopoulos, George, 2018. "Analysis of shock transmissions to a small open emerging economy using a SVARMA model," Working Papers 2018-02, University of Tasmania, Tasmanian School of Business and Economics.
    6. Martin Fukač & Adrian Pagan, 2010. "Limited information estimation and evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 55-70, January.
    7. Dufour, Jean-Marie & Taamouti, Abderrahim, 2010. "Short and long run causality measures: Theory and inference," Journal of Econometrics, Elsevier, vol. 154(1), pages 42-58, January.
    8. Philippe Jeanfils & Koen Burggraeve, 2005. "Noname - A new quarterly model for Belgium," Working Paper Research 68, National Bank of Belgium.
    9. Raghavan, Mala, 2019. "An analysis of the global oil market using SVARMA models," Working Papers 2019-01, University of Tasmania, Tasmanian School of Business and Economics.
    10. Emanuele Bacchiocchi & Catalin Dragomirescu-Gaina, 2022. "Uncertainty spill-overs: when policy and financial realms overlap," Working Papers wp1174, Dipartimento Scienze Economiche, Universita' di Bologna.
    11. Martin Fukac & Adrian Pagan, 2006. "Issues in Adopting DSGE Models for Use in the Policy Process," Working Papers 2006/6, Czech National Bank.
    12. Leung, Charles Ka Yui & Shi, Song & Ho Tang, Edward Chi, 2013. "Commodity house prices," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 875-887.
    13. D.S. Poskitt & Wenying Yao, 2012. "VAR Modeling and Business Cycle Analysis: A Taxonomy of Errors," Monash Econometrics and Business Statistics Working Papers 11/12, Monash University, Department of Econometrics and Business Statistics.
    14. Eric M. Leeper & Todd B. Walker & Shu-Chun Susan Yang, 2008. "Fiscal Foresight: Analytics and Econometrics," NBER Working Papers 14028, National Bureau of Economic Research, Inc.
    15. Jarkko Jääskelä & David Jennings, 2010. "Monetary Policy and the Exchange Rate: Evaluation of VAR Models," RBA Research Discussion Papers rdp2010-07, Reserve Bank of Australia.
    16. Yao, Wenying & Kam, Timothy & Vahid, Farshid, 2014. "VAR(MA), what is it good for? more bad news for reduced-form estimation and inference," Working Papers 2014-14, University of Tasmania, Tasmanian School of Business and Economics.
    17. Franchi, Massimo & Vidotto, Anna, 2013. "A check for finite order VAR representations of DSGE models," Economics Letters, Elsevier, vol. 120(1), pages 100-103.
    18. Liu, Philip & Theodoridis, Konstantinos, 2010. "DSGE model restrictions for structural VAR identification," Bank of England working papers 402, Bank of England.
    19. Cheuk Yin Ho, 2007. "Illegal migration and economic growth: simulation analysis in an international context," Economics Bulletin, AccessEcon, vol. 6(41), pages 1-13.
    20. K. Azim Ozdemir, 2015. "Interest Rate Surprises and Transmission Mechanism in Turkey: Evidence from Impulse Response Analysis," Working Papers 1504, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    21. Jääskelä, Jarkko P. & Jennings, David, 2011. "Monetary policy and the exchange rate: Evaluation of VAR models," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1358-1374.
    22. Poskitt, D.S., 2016. "Vector autoregressive moving average identification for macroeconomic modeling: A new methodology," Journal of Econometrics, Elsevier, vol. 192(2), pages 468-484.
    23. Massimo Franchi & Anna Vidotto, 2012. "A simple check for VAR representations of DSGE models," DSS Empirical Economics and Econometrics Working Papers Series 2012/5, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    24. Wang, Lipeng & Zhang, Mengyu & Verousis, Thanos, 2021. "The road to economic recovery: Pandemics and innovation," International Review of Financial Analysis, Elsevier, vol. 75(C).

  83. Richard T. Baillie & George Kapetanios, 2005. "Testing for Neglected Nonlinearity in Long Memory Models," Working Papers 528, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Kyongwook Choi & Wei-Choun Yu & Eric Zivot, 2008. "Long Memory versus Structural Breaks in Modeling and Forecasting Realized Volatility," Working Papers UWEC-2008-20-FC, University of Washington, Department of Economics.
    2. Kuswanto, Heri & Sibbertsen, Philipp, 2009. "Testing for Long Memory Against ESTAR Nonlinearities," Hannover Economic Papers (HEP) dp-427, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    3. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Muhammed TIRAŞOĞLU, 2019. "Unemployment hysteresis analysis for OECD countries," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(621), W), pages 53-62, Winter.
    5. Hassler, Uwe & Meller, Barbara, 2011. "Detecting multiple breaks in long memory: The case of US inflation," Discussion Paper Series 1: Economic Studies 2011,26, Deutsche Bundesbank.
    6. Uwe Hassler & Paulo M.M. Rodrigues & Antonio Rubia, 2016. "Quantile Regression for Long Memory Testing: A Case of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 693-724.
    7. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    8. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    9. Eric Hillebrand & Marcelo C. Medeiros, 2016. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 23-41, January.
    10. Manabu Asai & Michael McAleer, 2013. "A Fractionally Integrated Wishart Stochastic Volatility Model," Tinbergen Institute Discussion Papers 13-025/III, Tinbergen Institute.
    11. Patrick Krieger & Carsten Lausberg & Kristin Wellner, 2018. "Einblicke in die Gründe für nicht-normalverteilte Immobilienrenditen: eine explorative Untersuchung deutscher Wohnimmobilienportfolios [Insights into the reasons for non-normal real estate returns:," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 4(1), pages 49-79, November.
    12. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
    13. Muhammed TIRAŞOĞLU, 2018. "Fisher Hipotezinin MINT Ülkeleri İçin İncelenmesi: Eşik Değerli Adl Eşbütünleşme Testi Yaklaşımı," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 14(28), pages 31-43, December.
    14. Richard T. Baille & Claudio Morana, 2009. "Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach," ICER Working Papers - Applied Mathematics Series 06-2009, ICER - International Centre for Economic Research.
    15. Busch, Ulrike & Nautz, Dieter, 2009. "Controllability and persistence of money Market rates along the yield curve: Evidence from the Euro area," SFB 649 Discussion Papers 2009-029, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Modified information criteria and selection of long memory time series models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 116-131.
    17. John Goddard & Enrico Onali, 2014. "Self-affinity in financial asset returns," Papers 1401.7170, arXiv.org.
    18. Christensen, Bent Jesper & Varneskov, Rasmus Tangsgaard, 2017. "Medium band least squares estimation of fractional cointegration in the presence of low-frequency contamination," Journal of Econometrics, Elsevier, vol. 197(2), pages 218-244.
    19. Quinton Morris & Gary Van Vuuren & Paul Styger, 2009. "Further Evidence Of Long Memory In The South African Stock Market," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 81-101, March.
    20. Baillie, Richard T. & Kapetanios, George, 2008. "Nonlinear models for strongly dependent processes with financial applications," Journal of Econometrics, Elsevier, vol. 147(1), pages 60-71, November.
    21. Baillie, Richard T. & Morana, Claudio, 2012. "Adaptive ARFIMA models with applications to inflation," Economic Modelling, Elsevier, vol. 29(6), pages 2451-2459.
    22. Chatzikonstanti, Vasiliki & Venetis, Ioannis A., 2015. "Long memory in log-range series: Do structural breaks matter?," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 104-113.
    23. Rinke, Saskia & Busch, Marie & Leschinski, Christian, 2017. "Long Memory, Breaks, and Trends: On the Sources of Persistence in Inflation Rates," Hannover Economic Papers (HEP) dp-584, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    24. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
    25. Giampiero M. Gallo & Edoardo Otranto, 2014. "Forecasting Realized Volatility with Changes of Regimes," Econometrics Working Papers Archive 2014_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.
    26. Arturo Leccadito & Omar Rachedi & Giovanni Urga, 2015. "True Versus Spurious Long Memory: Some Theoretical Results and a Monte Carlo Comparison," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 452-479, April.
    27. Thabo M. Mokoena & Rangan Gupta & Reneé Van Eyden, 2009. "Testing For Ppp Using Sadc Real Exchange Rates," South African Journal of Economics, Economic Society of South Africa, vol. 77(3), pages 351-362, September.
    28. Smallwood, Aaron D., 2008. "Measuring the persistence of deviations from purchasing power parity with a fractionally integrated STAR model," Journal of International Money and Finance, Elsevier, vol. 27(7), pages 1161-1176, November.
    29. Eric Hillebrand & Marcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).
    30. Karanasos, M. & Koutroumpis, P. & Karavias, Y. & Kartsaklas, A. & Arakelian, V., 2016. "Inflation convergence in the EMU," Journal of Empirical Finance, Elsevier, vol. 39(PB), pages 241-253.
    31. KIlIç, Rehim, 2011. "Long memory and nonlinearity in conditional variances: A smooth transition FIGARCH model," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 368-378, March.
    32. Tm Mokoena, 2007. "Taking The Puzzle Out Of The Purchasing Power Parity Puzzle: An Application In Respect Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 75(1), pages 22-34, March.
    33. Luis A. Gil-Alana & Yadollah Dadgar & Rouhollah Nazari, 2019. "Iranian inflation: peristence and structural breaks," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(2), pages 398-408, April.
    34. John Elder & Sriram Villupuram, 2012. "Persistence in the return and volatility of home price indices," Applied Financial Economics, Taylor & Francis Journals, vol. 22(22), pages 1855-1868, November.

  84. George Kapetanios, 2005. "A Testing Procedure for Determining the Number of Factors in Approximate Factor Models with Large Datasets," Working Papers 551, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Di Iorio, Francesca & Fachin, Stefano, 2021. "Evaluating restricted common factor models for non-stationary data," Econometrics and Statistics, Elsevier, vol. 17(C), pages 64-75.
    2. Xun Lu & Su Liangjun, 2015. "Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects," Working Papers 02-2015, Singapore Management University, School of Economics.
    3. Tae-Hwy Lee & Ekaterina Seregina, 2023. "Optimal Portfolio Using Factor Graphical Lasso," Working Papers 202302, University of California at Riverside, Department of Economics.
    4. Shi Yafeng & Ai Chunrong & Yanlong Shi & Ying Tingting & Xu Qunfang, 2023. "Large covariance estimation using a factor model with common and group‐specific factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2217-2248, December.
    5. Noureddine Kouaissah & Amin Hocine, 2021. "Forecasting systemic risk in portfolio selection: The role of technical trading rules," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 708-729, July.
    6. Derek Bunn & Julien Chevallier & Yannick Le Pen & Benoît Sévi, 2017. "Fundamental and Financial Influences on the Co-movement of Oil and Gas prices," Post-Print hal-01619890, HAL.
    7. Yannick Le Pen & Benoît Sévi, 2013. "Futures trading and the excess comovement of commodity prices," Working Papers 2013-19, Department of Research, Ipag Business School.
    8. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2009. "A Robust Criterion for Determining the Number of Factors in Approximate Factor Models," Working Papers ECARES 2009_023, ULB -- Universite Libre de Bruxelles.
    9. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.
    10. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2012. "Exponent of Cross-sectional Dependence: Estimation and Inference," CESifo Working Paper Series 3722, CESifo.
    11. De Vos, Ignace & Everaert, Gerdie & Sarafidis, Vasilis, 2021. "A method for evaluating the rank condition for CCE estimators," MPRA Paper 112305, University Library of Munich, Germany, revised 09 Mar 2022.
    12. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    13. Gao, Quansheng & Hu, Chengjun, 2009. "Dynamic mortality factor model with conditional heteroskedasticity," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 410-423, December.
    14. Dr. James Mitchell, 2010. "A Nonlinear Panel Data Model of Cross-sectional Dependence," National Institute of Economic and Social Research (NIESR) Discussion Papers 370, National Institute of Economic and Social Research.
    15. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    16. Mattia Guerini & Duc Thi Luu & Mauro Napoletano, 2019. "Synchronization Patterns in the European Union," GREDEG Working Papers 2019-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    17. Guowei Cui & Milda Norkuté & Vasilis Sarafidis & Takashi Yamagata, 2020. "Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects," ISER Discussion Paper 1101, Institute of Social and Economic Research, Osaka University.
    18. Francisco Corona & Pilar Poncela & Esther Ruiz, 2017. "Determining the number of factors after stationary univariate transformations," Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
    19. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    20. Cynthia Fan Yang, 2021. "Common factors and spatial dependence: an application to US house prices," Econometric Reviews, Taylor & Francis Journals, vol. 40(1), pages 14-50, January.
    21. Otter, Pieter W. & Jacobs, Jan P.A.M. & Reijer, Ard H.J. de, 2014. "A criterion for the number of factors in a data-rich environment," Research Report 14008-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    22. Alexander Chudik & M. Hashem Pesaran, 2013. "Large panel data models with cross-sectional dependence: a survey," Globalization Institute Working Papers 153, Federal Reserve Bank of Dallas.
    23. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    24. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434, Emerald Group Publishing Limited.
    25. Yuefeng Han & Rong Chen & Cun-Hui Zhang, 2020. "Rank Determination in Tensor Factor Model," Papers 2011.07131, arXiv.org, revised May 2022.
    26. Barigozzi, Matteo & Conti, Antonio & Luciani, Matteo, 2012. "Do Euro area countries respond asymmetrically to the common monetary policy?," LSE Research Online Documents on Economics 43344, London School of Economics and Political Science, LSE Library.
    27. In Choi & Rui Lin & Yongcheol Shin, 2020. "Canonical Correlation-based Model Selection for the Multilevel Factors," Working Papers 2008, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    28. Yongfu Huang & Muhammad G. Quibria, 2015. "The global partnership for sustainable development," Natural Resources Forum, Blackwell Publishing, vol. 39(3-4), pages 157-174, August.
    29. Mattia Guerini & Mauro Napoletano & Claudio Barbieri, 2024. "The anatomy of government bond yields synchronization in the Eurozone," Post-Print hal-04530954, HAL.
    30. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    31. Simon Freyaldenhoven, 2019. "A Generalized Factor Model with Local Factors," Working Papers 19-23, Federal Reserve Bank of Philadelphia.
    32. Bai, Jushan & Liao, Yuan, 2012. "Efficient Estimation of Approximate Factor Models," MPRA Paper 41558, University Library of Munich, Germany.
    33. Xu Cheng & Zhipeng Liao & Frank Schorfheide, 2016. "Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1511-1543.
    34. Wang, Cindy S.H. & Chen, Yi-Chi & Lo, Hsin-Yu, 2021. "A fresh look at the risk-return tradeoff," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    35. Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.
    36. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    37. Wei, Jie & Chen, Hui, 2020. "Determining the number of factors in approximate factor models by twice K-fold cross validation," Economics Letters, Elsevier, vol. 191(C).
    38. Focardi, Sergio M. & Fabozzi, Frank J. & Mitov, Ivan K., 2016. "A new approach to statistical arbitrage: Strategies based on dynamic factor models of prices and their performance," Journal of Banking & Finance, Elsevier, vol. 65(C), pages 134-155.
    39. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
    40. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "A diagnostic criterion for approximate factor structure," Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.
    41. Luca Margaritella & Joakim Westerlund, 2023. "Using information criteria to select averages in CCE," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 405-421.
    42. Vogt, M. & Walsh, C. & Linton, O., 2022. "CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects," Janeway Institute Working Papers 2218, Faculty of Economics, University of Cambridge.
    43. Atak, Alev & Kapetanios, George, 2013. "A factor approach to realized volatility forecasting in the presence of finite jumps and cross-sectional correlation in pricing errors," Economics Letters, Elsevier, vol. 120(2), pages 224-228.
    44. Poncela, Pilar, 2015. "Small versus big-data factor extraction in Dynamic Factor Models: An empirical assessment," DES - Working Papers. Statistics and Econometrics. WS ws1502, Universidad Carlos III de Madrid. Departamento de Estadística.
    45. Kemal Bagzibagli, 2014. "Monetary transmission mechanism and time variation in the Euro area," Empirical Economics, Springer, vol. 47(3), pages 781-823, November.
    46. Rodríguez Caballero, Carlos Vladimir, 2017. "Estimation of a Dynamic Multilevel Factor Model with possible long-range dependence," DES - Working Papers. Statistics and Econometrics. WS 24614, Universidad Carlos III de Madrid. Departamento de Estadística.
    47. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.
    48. Jianqing Fan & Yuan Liao & Martina Mincheva, 2013. "Large covariance estimation by thresholding principal orthogonal complements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
    49. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2008. "A robust criterion for determining the number of static factors in approximate factor models," Working Paper Series 903, European Central Bank.
    50. Bodnar, Taras & Reiß, Markus, 2016. "Exact and asymptotic tests on a factor model in low and large dimensions with applications," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 125-151.
    51. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2023. "Latent Factor Analysis in Short Panels," Papers 2306.14004, arXiv.org, revised May 2024.
    52. Corona, Francisco & Poncela, Pilar, 2017. "Estimating non-stationary common factors : Implications for risk sharing," DES - Working Papers. Statistics and Econometrics. WS 24585, Universidad Carlos III de Madrid. Departamento de Estadística.
    53. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Dynamic Factor Models, Cointegration, and Error Correction Mechanisms," Finance and Economics Discussion Series 2016-018, Board of Governors of the Federal Reserve System (U.S.).
    54. Bai, Jushan & Liao, Yuan, 2016. "Efficient estimation of approximate factor models via penalized maximum likelihood," Journal of Econometrics, Elsevier, vol. 191(1), pages 1-18.
    55. Claudio Morana, 2013. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks: New Insights on the US OIS SPreads Term Structure," Working Papers 233, University of Milano-Bicocca, Department of Economics, revised Feb 2013.
    56. Vogt, M. & Walsh, C. & Linton, O., 2022. "CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects," Cambridge Working Papers in Economics 2242, Faculty of Economics, University of Cambridge.
    57. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    58. Otter, Pieter W. & Jacobs, Jan P.A.M., 2006. "On information in static and dynamic factor models," CCSO Working Papers 200605, University of Groningen, CCSO Centre for Economic Research.
    59. Andres Sagner, 2020. "High Dimensional Quantile Factor Analysis," Working Papers Central Bank of Chile 886, Central Bank of Chile.
    60. Noureddine Kouaissah & Sergio Ortobelli Lozza & Ikram Jebabli, 2022. "Portfolio Selection Using Multivariate Semiparametric Estimators and a Copula PCA-Based Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 833-859, October.
    61. E. Raffinetti & I. Romeo, 2015. "Dealing with the biased effects issue when handling huge datasets: the case of INVALSI data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2554-2570, December.
    62. Bada, Oualid & Kneip, Alois, 2014. "Parameter cascading for panel models with unknown number of unobserved factors: An application to the credit spread puzzle," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 95-115.
    63. Tjeerd M. Boonman & Jan P.A.M. Jacobs & Gerard H. Kuper, 2011. "Why didn't the Global Financial Crisis hit Latin America?," CIRANO Working Papers 2011s-63, CIRANO.
    64. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    65. Markus Pelger & Jiacheng Zou, 2022. "Inference for Large Panel Data with Many Covariates," Papers 2301.00292, arXiv.org, revised Mar 2023.
    66. Rachida Ouysse, 2017. "Constrained principal components estimation of large approximate factor models," Discussion Papers 2017-12, School of Economics, The University of New South Wales.
    67. Eric Hillebrand & Jakob Guldbæk Mikkelsen & Lars Spreng & Giovanni Urga, 2023. "Exchange rates and macroeconomic fundamentals: Evidence of instabilities from time‐varying factor loadings," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 857-877, September.
    68. Oliver Linton & Maximilian Ruecker & Michael Vogt & Christopher Walsh, 2022. "Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects," Papers 2206.12152, arXiv.org, revised Nov 2024.
    69. Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
    70. Heaton, Chris & Solo, Victor, 2012. "Estimation of high-dimensional linear factor models with grouped variables," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 348-367.
    71. Pelger, Markus, 2019. "Large-dimensional factor modeling based on high-frequency observations," Journal of Econometrics, Elsevier, vol. 208(1), pages 23-42.
    72. Mehmet Caner & Xu Han, 2014. "Selecting the Correct Number of Factors in Approximate Factor Models: The Large Panel Case With Group Bridge Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 359-374, July.
    73. Matthew Harding & Carlos Lamarche & Chris Muris, 2022. "Estimation of a Factor-Augmented Linear Model with Applications Using Student Achievement Data," Papers 2203.03051, arXiv.org.
    74. Xin Shi & Robert Qiu, 2019. "Estimation of high-dimensional factor models and its application in power data analysis," Papers 1905.02061, arXiv.org, revised Oct 2019.

  85. George Kapetanios, 2005. "Variable Selection using Non-Standard Optimisation of Information Criteria," Working Papers 533, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
    2. Kapetanios, G. & Labhard, V. & Price, S., 2007. "Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation," Working Papers 07/15, Department of Economics, City University London.
    3. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.

  86. Kapetanios, G. & Pesaran, M.H., 2005. "Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling of Asset Returns," Cambridge Working Papers in Economics 0520, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Luisa Corrado & Bernard Fingleton, 2012. "Where Is The Economics In Spatial Econometrics?," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 210-239, May.
    2. Westerlund, Joakim & Reese, Simon, 2014. "Estimation of Factor-Augmented Panel Regressions with Weakly Influential Factors," Working Papers 2014:8, Lund University, Department of Economics, revised 27 Jan 2014.
    3. George Kapetanios & Laura Serlenga & Yongcheol Shin, 2019. "Testing for Correlated Factor Loadings in Cross Sectionally Dependent Panels," SERIES 02-2019, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Jun 2019.
    4. Guowei Cui & Milda Norkuté & Vasilis Sarafidis & Takashi Yamagata, 2020. "Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects," ISER Discussion Paper 1101, Institute of Social and Economic Research, Osaka University.
    5. Sirin, Selahattin Murat & Uz, Dilek & Sevindik, Irem, 2022. "How do variable renewable energy technologies affect firm-level day-ahead output decisions: Evidence from the Turkish wholesale electricity market," Energy Economics, Elsevier, vol. 112(C).
    6. Pauwels Laurent L. & Chan Felix & Mancini Griffoli Tommaso, 2012. "Testing for Structural Change in Heterogeneous Panels with an Application to the Euro's Trade Effect," Journal of Time Series Econometrics, De Gruyter, vol. 4(2), pages 1-35, November.
    7. M. Hashem Pesaran & Ron P. Smith, 2006. "Macroeconometric Modelling with a Global Perspective," CESifo Working Paper Series 1659, CESifo.
    8. Filippo di Mauro & L. Vanessa Smith & Stephane Dees & M. Hashem Pesaran, 2007. "Exploring the international linkages of the euro area: a global VAR analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 1-38.
    9. Luisa Corrado & Bernard Fingleton, 2011. "Multilevel Modelling with Spatial Effects," Working Papers 1105, University of Strathclyde Business School, Department of Economics.
    10. Westerlund, Joakim & Urbain, Jean-Pierre, 2015. "Cross-sectional averages versus principal components," Journal of Econometrics, Elsevier, vol. 185(2), pages 372-377.
    11. Castagnetti, Carolina & Rossi, Eduardo, 2008. "Estimation methods in panel data models with observed and unobserved components: a Monte Carlo study," MPRA Paper 26196, University Library of Munich, Germany.
    12. M. Hashem Pesaran & Til Schuermann & Bjorn-Jakob Treutler, 2007. "Global Business Cycles and Credit Risk," NBER Chapters, in: The Risks of Financial Institutions, pages 419-469, National Bureau of Economic Research, Inc.
    13. Su, Liangjun & Jin, Sainan, 2012. "Sieve estimation of panel data models with cross section dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 34-47.
    14. Chen, Yan & Murshed, Muntasir & Sinha, Avik & Alam, Mohammad Mahtab & Khudoykulov, Khurshid, 2024. "Revisiting the resource curse hypothesis from the viewpoint of green growth: The role of Fintech as the de-cursing agent," Resources Policy, Elsevier, vol. 95(C).
    15. Westerlund, Joakim & Urbain, Jean-Pierre, 2013. "On the estimation and inference in factor-augmented panel regressions with correlated loadings," Economics Letters, Elsevier, vol. 119(3), pages 247-250.
    16. Camilla Mastromarco & Laura Serlenga & Yongcheol Shin, 2012. "Is Globalization Driving Efficiency? A Threshold Stochastic Frontier Panel Data Modeling Approach," Review of International Economics, Wiley Blackwell, vol. 20(3), pages 563-579, August.
    17. Reese, Simon & Westerlund, Joakim, 2014. "PANICCA - PANIC on Cross-Section Averages," Working Papers 2015:3, Lund University, Department of Economics, revised 24 Mar 2015.
    18. Westerlund, Joakim & Urbain, Jean-Pierre, 2013. "On the implementation and use of factor-augmented regressions in panel data," Journal of Asian Economics, Elsevier, vol. 28(C), pages 3-11.
    19. Beyhum, Jad & Gautier, Eric, 2021. "Factor and factor loading augmented estimators for panel regression," TSE Working Papers 21-1219, Toulouse School of Economics (TSE).
    20. Hou, Lei & Li, Kunpeng & Li, Qi & Ouyang, Min, 2021. "Revisiting the location of FDI in China: A panel data approach with heterogeneous shocks," Journal of Econometrics, Elsevier, vol. 221(2), pages 483-509.
    21. Sainan Jin & Liangjun Su, 2013. "A Nonparametric Poolability Test for Panel Data Models with Cross Section Dependence," Econometric Reviews, Taylor & Francis Journals, vol. 32(4), pages 469-512, December.
    22. Guowei Cui & Kazuhiko Hayakawa & Shuichi Nagata & Takashi Yamagata, 2018. "A robust approach to heteroskedasticity, error serial correlation and slope heterogeneity for large linear panel data models with interactive effects," ISER Discussion Paper 1037r, Institute of Social and Economic Research, Osaka University, revised Jun 2019.
    23. Sirin, Selahattin Murat & Erten, Ibrahim, 2022. "Price spikes, temporary price caps, and welfare effects of regulatory interventions on wholesale electricity markets," Energy Policy, Elsevier, vol. 163(C).

  87. George Kapetanios & Elias Tzavalis, 2005. "Nonlinear Modelling of Autoregressive Structural Breaks in a US Diffusion Index Dataset," Working Papers 537, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.

  88. George Kapetanios & Vincent Labhard & Simon Price, 2005. "Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation," Bank of England working papers 268, Bank of England.

    Cited by:

    1. Maurin, Laurent & Drechsel, Katja, 2008. "Flow of conjunctural information and forecast of euro area economic activity," Working Paper Series 925, European Central Bank.
    2. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
    3. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    4. Bouckaert, Nicolas & Van den Heede, Koen & Van de Voorde, Carine, 2018. "Improving the forecasting of hospital services: A comparison between projections and actual utilization of hospital services," Health Policy, Elsevier, vol. 122(7), pages 728-736.
    5. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    6. Scharnagl, Michael & Schumacher, Christian, 2007. "Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities," Discussion Paper Series 1: Economic Studies 2007,09, Deutsche Bundesbank.
    7. Winkelried, Diego, 2012. "Predicting quarterly aggregates with monthly indicators," Working Papers 2012-023, Banco Central de Reserva del Perú.
    8. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    9. Eliana González, 2010. "Bayesian Model Averaging. An Application to Forecast Inflation in Colombia," Borradores de Economia 7015, Banco de la Republica.
    10. Hilde C. Bjørnland & Karsten Gerdrup & Anne Sofie Jore & Christie Smith & Leif Anders Thorsrud, 2012. "Does Forecast Combination Improve Norges Bank Inflation Forecasts?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 163-179, April.
    11. Todd E. Clark & Michael W. McCracken, 2008. "Averaging forecasts from VARs with uncertain instabilities," Working Papers 2008-030, Federal Reserve Bank of St. Louis.
    12. Wei, Yu & Cao, Yang, 2017. "Forecasting house prices using dynamic model averaging approach: Evidence from China," Economic Modelling, Elsevier, vol. 61(C), pages 147-155.
    13. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
    14. Hugo Gerard & Kristoffer Nimark, 2008. "Combining Multivariate Density Forecasts Using Predictive Criteria," RBA Research Discussion Papers rdp2008-02, Reserve Bank of Australia.
    15. Cogley, Timothy & de Paoli, Bianca & Matthes, Christian & Nikolov, Kalin & Yates, Tony, 2011. "A Bayesian approach to optimal monetary policy with parameter and model uncertainty," Bank of England working papers 414, Bank of England.
    16. Ivan Savin, 2010. "A comparative study of the Lasso-type and heuristic model selection methods," Working Papers 042, COMISEF.
    17. Luis F. Melo Velandia & Rubén A. Loaiza Maya & Mauricio Villamizar-Villegas, 2014. "Bayesian Combination for Inflation Forecasts: The Effects of a Prior Based on Central Banks’ Estimates," Borradores de Economia 853, Banco de la Republica de Colombia.
    18. Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023. "Анализ Возможностей Улучшения Качества Прогнозов Цен На Природные Ресурсы Методами Комбинирования На Основе Регрессионных Оценок Весов," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
    19. George Bagdatoglou & Alexandros Kontonikas & Mark E. Wohar, 2016. "Forecasting Us Inflation Using Dynamic General-To-Specific Model Selection," Bulletin of Economic Research, Wiley Blackwell, vol. 68(2), pages 151-167, April.
    20. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
    21. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
    22. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    23. Chanont Banternghansa & Michael W. McCracken, 2011. "Real-time forecast averaging with ALFRED," Review, Federal Reserve Bank of St. Louis, vol. 93(Jan), pages 49-66.
    24. Drechsel, Katja & Scheufele, Rolf, 2011. "The Financial Crisis from a Forecaster’s Perspective," IWH Discussion Papers 5/2011, Halle Institute for Economic Research (IWH).
    25. Paul Downward & Andrew Mearman, 2008. "Decision-making at the Bank of England: a critical appraisal," Oxford Economic Papers, Oxford University Press, vol. 60(3), pages 385-409, July.
    26. Zhang, Xinyu & Wan, Alan T.K. & Zou, Guohua, 2013. "Model averaging by jackknife criterion in models with dependent data," Journal of Econometrics, Elsevier, vol. 174(2), pages 82-94.
    27. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    28. Ekaterina V. Astafyeva & Maria Yu. Turuntseva, 2023. "Analysis of Opportunities to Improve the Quality of Natural Resource Price by Combining Forecasts Resulting from Methods Based on Regression Estimates of Weights [Анализ Возможностей Улучшения Каче," Russian Economic Development, Gaidar Institute for Economic Policy, issue 12, pages 24-33, December.
    29. Yundong Tu & Siwei Wang, 2023. "Variable Screening and Model Averaging for Expectile Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 574-598, June.
    30. David Bolder & Yuliya Romanyuk, 2008. "Combining Canadian Interest-Rate Forecasts," Staff Working Papers 08-34, Bank of Canada.
    31. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.
    32. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    33. Chen Yu-Chin & Rogoff Kenneth, 2012. "Are The Commodity Currencies An Exception To The Rule?," Global Journal of Economics (GJE), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 1-28.
    34. Vasyl Golosnoy & Yarema Okhrin, 2015. "Using information quality for volatility model combinations," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 1055-1073, June.
    35. George Kapetanios & Vincent Labhard & Simon Price, 2006. "Forecasting Using Predictive Likelihood Model Averaging," Working Papers 567, Queen Mary University of London, School of Economics and Finance.
    36. Lu, Xun & Su, Liangjun, 2015. "Jackknife model averaging for quantile regressions," Journal of Econometrics, Elsevier, vol. 188(1), pages 40-58.
    37. Matthias Pelster & Johannes Vilsmeier, 2018. "The determinants of CDS spreads: evidence from the model space," Review of Derivatives Research, Springer, vol. 21(1), pages 63-118, April.
    38. Pelster, Matthias & Vilsmeier, Johannes, 2016. "The determinants of CDS spreads: Evidence from the model space," Discussion Papers 43/2016, Deutsche Bundesbank.
    39. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    40. Vanina Forget, 2012. "Doing well and doing good: a multi-dimensional puzzle," Working Papers hal-00672037, HAL.
    41. Alena Skolkova, 2023. "Model Averaging with Ridge Regularization," CERGE-EI Working Papers wp758, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    42. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    43. Ferreira, Diego & Palma, Andreza Aparecida, 2015. "Forecasting Inflation with the Phillips Curve: A Dynamic Model Averaging Approach for Brazil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 69(4), December.
    44. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.
    45. Kira Alhorn & Holger Dette & Kirsten Schorning, 2021. "Optimal Designs for Model Averaging in non-nested Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 745-778, August.
    46. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    47. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    48. Mariola Pilatowska, 2009. "The Combined Forecasts Using the Akaike Weights," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 5-16.

  89. George Kapetanios, 2004. "A New Method for Determining the Number of Factors in Factor Models with Large Datasets," Working Papers 525, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.
    2. Andrés García-Medina & Graciela González Farías, 2020. "Transfer entropy as a variable selection methodology of cryptocurrencies in the framework of a high dimensional predictive model," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-31, January.
    3. Kapetanios, George, 2010. "A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 397-409.
    4. Alexander Chudik & M. Hashem Pesaran, 2013. "Large panel data models with cross-sectional dependence: a survey," Globalization Institute Working Papers 153, Federal Reserve Bank of Dallas.
    5. Simon Freyaldenhoven, 2019. "A Generalized Factor Model with Local Factors," Working Papers 19-23, Federal Reserve Bank of Philadelphia.
    6. Jushan Bai & Shuzhong Shi, 2011. "Estimating High Dimensional Covariance Matrices and its Applications," Annals of Economics and Finance, Society for AEF, vol. 12(2), pages 199-215, November.
    7. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
    8. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2006. "Forecasting Inflation and GDP growth: Comparison of Automatic Leading Indicator (ALI) Method with Macro Econometric Structural Models (MESMs)," Working Papers 554, Queen Mary University of London, School of Economics and Finance.
    9. Silvia S.W. Lui, 2006. "An Empirical Study of Asian Stock Volatility Using Stochastic Volatility Factor Model: Factor Analysis and Forecasting," Working Papers 581, Queen Mary University of London, School of Economics and Finance.
    10. Jean-Philippe Bouchaud & Laurent Laloux & M. Augusta Miceli & Marc Potters, 2005. "Large dimension forecasting models and random singular value spectra," Science & Finance (CFM) working paper archive 500066, Science & Finance, Capital Fund Management.
    11. J.-P. Bouchaud & L. Laloux & M. A. Miceli & M. Potters, 2007. "Large dimension forecasting models and random singular value spectra," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 201-207, January.
    12. Chetan Ghate & Stephen Wright, 2008. "The "V-Factor": Distribution, Timing and Correlates of the Great Indian Growth Turnaround," Discussion Papers of DIW Berlin 783, DIW Berlin, German Institute for Economic Research.
    13. Joel Bun & Jean-Philippe Bouchaud & Marc Potters, 2016. "Cleaning large correlation matrices: tools from random matrix theory," Papers 1610.08104, arXiv.org.
    14. Xu Han & Mehmet Caner, 2017. "Determining the number of factors with potentially strong within-block correlations in error terms," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 946-969, October.
    15. Otter, Pieter W. & Jacobs, Jan P.A.M., 2006. "On information in static and dynamic factor models," CCSO Working Papers 200605, University of Groningen, CCSO Centre for Economic Research.
    16. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2007. "Automatic Leading Indicators (ALIs) versus Macro Econometric Structural Models (MESMs): Comparison of Inflation and GDP growth Forecasting," EcoMod2007 23900072, EcoMod.
    17. Xin Shi & Robert Qiu, 2019. "Estimation of high-dimensional factor models and its application in power data analysis," Papers 1905.02061, arXiv.org, revised Oct 2019.
    18. Francesco Moscone & Elisa Tosetti, 2009. "A Review And Comparison Of Tests Of Cross‐Section Independence In Panels," Journal of Economic Surveys, Wiley Blackwell, vol. 23(3), pages 528-561, July.

  90. Camba-Méndez, Gonzalo & Kapetanios, George, 2004. "Forecasting euro area inflation using dynamic factor measures of underlying inflation," Working Paper Series 402, European Central Bank.

    Cited by:

    1. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    2. Stracca, Livio & Bilke, Laurent, 2008. "A persistence-weighted measure of core inflation in the euro area," Working Paper Series 905, European Central Bank.
    3. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic Factor Models," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 3, pages 25-40, Springer.
    4. Christopher L. Gilbert & Duo Qin, 2007. "Representation in Econometrics: A Historical Perspective," Working Papers 583, Queen Mary University of London, School of Economics and Finance.
    5. Milena Lipovina-Božović, 2013. "A Comparison Of The Var Model And The Pc Factor Model In Forecasting Inflation In Montenegro," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 58(198), pages 115-136, July - Se.
    6. Alonso Gomez & John M Maheu & Alex Maynard, 2008. "Improving Forecasts of Inflation using the Term Structure of Interest Rates," Working Papers tecipa-319, University of Toronto, Department of Economics.
    7. Juraj Hucek & Alexander Karsay & Marian Vavra, 2015. "Short-term Forecasting of Real GDP Using Monthly Data," Working and Discussion Papers OP 1/2015, Research Department, National Bank of Slovakia.
    8. In Choi & Dukpa Kim & Yun Jung Kim & Noh‐Sun Kwark, 2018. "A multilevel factor model: Identification, asymptotic theory and applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 355-377, April.
    9. Petar Sorić & Ivana Lolić, 2015. "A note on forecasting euro area inflation: leave- $$h$$ h -out cross validation combination as an alternative to model selection," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(1), pages 205-214, March.
    10. Barakchian , Seyed Mahdi & Bayat , Saeed & Karami , Hooman, 2013. "Common Factors of CPI Sub-aggregates and Forecast of Inflation," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 8(4), pages 1-17, October.
    11. Liam J. A. Lenten, 2010. "Bananas and petrol: further evidence on the forecasting accuracy of the ABS 'headline' and 'underlying' rates of inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(6), pages 556-572.
    12. In Choi & Hanbat Jeong, 2019. "Model selection for factor analysis: Some new criteria and performance comparisons," Econometric Reviews, Taylor & Francis Journals, vol. 38(6), pages 577-596, July.
    13. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.
    14. Bhattacharya, Rudrani & Kapoor, Mrigankshi, 2020. "Forecasting Consumer Price Index Inflation in India: Vector Error Correction Mechanism Vs. Dynamic Factor Model Approach for Non-Stationary Time Series," Working Papers 20/323, National Institute of Public Finance and Policy.
    15. Luetkepohl Helmut & Xu Fang, 2011. "Forecasting Annual Inflation with Seasonal Monthly Data: Using Levels versus Logs of the Underlying Price Index," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-23, February.
    16. Franz Ruch & Mehmet Balcilar & Mampho P. Modise & Rangan Gupta, 2015. "Forecasting Core Inflation: The Case of South Africa," Working Papers 201543, University of Pretoria, Department of Economics.
    17. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    18. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    19. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    20. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.
    21. Davor Kunovac, 2007. "Factor Model Forecasting of Inflation in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 31(4), pages 371-393.
    22. António Rua, 2016. "A wavelet-based multivariate multiscale approach for forecasting," Working Papers w201612, Banco de Portugal, Economics and Research Department.

  91. Richard Harrison & George Kapetanios & Tony Yates, 2004. "Forecasting with measurement errors in dynamic models," Bank of England working papers 237, Bank of England.

    Cited by:

    1. Alastair Cunningham & Jana Eklund & Christopher Jeffery & George Kapetanios & Vincent Labhard, 2007. "A state space approach to extracting the signal from uncertain data," Bank of England working papers 336, Bank of England.
    2. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    3. Juan Manuel Julio, 2011. "Modeling Data Revisions," Borradores de Economia 641, Banco de la Republica de Colombia.
    4. SILVESTRINI, Andrea & SALTo, Matteo & MOULIN, Laurent & VEREDAS, David, 2009. "Monitoring and forecasting annual public deficit every month: the case of France," LIDAM Reprints CORE 2019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Fabio Busetti, 2006. "Preliminary data and econometric forecasting: an application with the Bank of Italy Quarterly Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 1-23.
    6. Emilia Tomczyk, 2013. "End of sample vs. real time data: perspectives for analysis of expectations," Working Papers 68, Department of Applied Econometrics, Warsaw School of Economics.
    7. George Kapetanios & Tony Yates, 2010. "Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 869-893.
    8. Valentina Raponi & Cecilia Frale, 2014. "Revisions in official data and forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 451-472, August.
    9. Clements, Michael P. & Galvão, Ana Beatriz, 2010. "Real-time Forecasting of Inflation and Output Growth in the Presence of Data Revisions," The Warwick Economics Research Paper Series (TWERPS) 953, University of Warwick, Department of Economics.
    10. Paul Downward & Andrew Mearman, 2008. "Decision-making at the Bank of England: a critical appraisal," Oxford Economic Papers, Oxford University Press, vol. 60(3), pages 385-409, July.
    11. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.
    12. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    13. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    14. Alastair Cunningham & Chris Jeffery & George Kapetanios & Vincent Labhard, 2007. "A State Space Approach To The Policymaker's Data Uncertainty Problem," Money Macro and Finance (MMF) Research Group Conference 2006 168, Money Macro and Finance Research Group.
    15. George Kapetanios & Tony Yates, 2004. "Estimating time-variation in measurement error from data revisions; an application to forecasting in dynamic models," Bank of England working papers 238, Bank of England.

  92. George Kapetanios, 2004. "Testing for Exogeneity in Nonlinear Threshold Models," Working Papers 515, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Psaradakis Zacharias & Sola Martin & Spagnolo Fabio, 2006. "Instrumental-Variables Estimation in Markov Switching Models with Endogenous Explanatory Variables: An Application to the Term Structure of Interest Rates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(2), pages 1-31, May.

  93. George Kapetanios, 2004. "A Bootstrap Invariance Principle for Highly Nonstationary Long Memory Processes," Working Papers 507, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. George Kapetanios & Fotis Papailias, 2011. "Block Bootstrap and Long Memory," Working Papers 679, Queen Mary University of London, School of Economics and Finance.
    2. Margherita Gerolimetto & Isabella Procidano, 2008. "A test for fractional cointegration using the sieve bootstrap," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(3), pages 373-391, July.

  94. George Kapetanios, 2004. "The Impact of Large Structural Shocks on Economic Relationships: Evidence from Oil Price Shocks," Working Papers 524, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. George Kapetanios & Elias Tzavalis, 2005. "Nonlinear Modelling of Autoregressive Structural Breaks in a US Diffusion Index Dataset," Working Papers 537, Queen Mary University of London, School of Economics and Finance.
    2. Richard Harrison & George Kapetanios & Alasdair Scott & Jana Eklund, 2008. "Breaks in DSGE models," 2008 Meeting Papers 657, Society for Economic Dynamics.
    3. Kihaule, Arnold Mathias, 2012. "The impact of economic policy shocks on the outcomes of the fiscal adjustment policies in Tanzania," MPRA Paper 46151, University Library of Munich, Germany, revised Feb 2013.
    4. George Kapetanios, 2007. "Testing for Strict Stationarity," Working Papers 602, Queen Mary University of London, School of Economics and Finance.
    5. Kapetanios, George, 2008. "Bootstrap-based tests for deterministic time-varying coefficients in regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 534-545, December.
    6. George Kapetanios, 2005. "Tests for Deterministic Parametric Structural Change in Regression Models," Working Papers 539, Queen Mary University of London, School of Economics and Finance.

  95. George Kapetanios, 2004. "Dynamic Factor Extraction of Cross-Sectional Dependence in Panel Unit Root Tests," Working Papers 509, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Jan J. J. Groen & George Kapetanios & Simon Price, 2013. "Multivariate Methods For Monitoring Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 250-274, March.
    2. Chortareas, Georgios & Kapetanios, George, 2009. "Getting PPP right: Identifying mean-reverting real exchange rates in panels," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 390-404, February.
    3. Pesaran, M.H., 2003. "A Simple Panel Unit Root Test in the Presence of Cross Section Dependence," Cambridge Working Papers in Economics 0346, Faculty of Economics, University of Cambridge.
    4. Loukia Meligkotsidou & Elias Tzavalis & Ioannis D. Vrontos, 2012. "A Bayesian panel data framework for examining the economic growth convergence hypothesis: do the G7 countries converge?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 1975-1990, May.
    5. Hwa-Taek Lee & Gawon Yoon, 2013. "Does purchasing power parity hold sometimes? Regime switching in real exchange rates," Applied Economics, Taylor & Francis Journals, vol. 45(16), pages 2279-2294, June.
    6. Grigoriev, A. & Sviridenko, M. & Uetz, M.J., 2005. "Machine scheduling with resource dependent processing times," Research Memorandum 050, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    7. Giuseppe Arbia, 2011. "A Lustrum of SEA: Recent Research Trends Following the Creation of the Spatial Econometrics Association (2007--2011)," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(4), pages 377-395, July.

  96. George Kapetanios, 2004. "On Testing for Diagonality of Large Dimensional Covariance Matrices," Working Papers 526, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2012. "A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model," Journal of Econometrics, Elsevier, vol. 170(1), pages 164-177.
    2. Badi H. Baltagi & Qu Feng & Chihwa Kao, 2009. "Testing for Sphericity in a Fixed Effects Panel Data Model (Revised July 2009)," Center for Policy Research Working Papers 112, Center for Policy Research, Maxwell School, Syracuse University.

  97. Camba-Méndez, Gonzalo & Kapetanios, George, 2004. "Estimating the rank of the spectral density matrix," Working Paper Series 349, European Central Bank.

    Cited by:

    1. Gonzalo Camba-Mendez & George Kapetanios, 2009. "Statistical Tests and Estimators of the Rank of a Matrix and Their Applications in Econometric Modelling," Econometric Reviews, Taylor & Francis Journals, vol. 28(6), pages 581-611.
    2. Majid M. Al-Sadoon, 2014. "A general theory of rank testing," Economics Working Papers 1411, Department of Economics and Business, Universitat Pompeu Fabra, revised Feb 2015.

  98. Georgios Chortareas & George Kapetanios, 2004. "Getting PPP Right: Identifying Mean Reverting Real Exchange Rates in Panels," Money Macro and Finance (MMF) Research Group Conference 2004 32, Money Macro and Finance Research Group.

    Cited by:

    1. Pei-Long Shen & Chih-Wei Su & Hsu-Ling Chang, 2013. "Are real GDP levels nonstationary across Central and Eastern European countries?," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 13(1), pages 99-108, July.
    2. Tsangyao Chang & Tsung-pao Wu & Rangan Gupta, 2013. "Are House Prices in South Africa Really Non-Stationary? Evidence from SPSM-Based Panel KSS Test with a Fourier Function," Working Papers 201324, University of Pretoria, Department of Economics.
    3. Emirmahmutoglu, Furkan & Omay, Tolga, 2014. "Reexamining the PPP hypothesis: A nonlinear asymmetric heterogeneous panel unit root test," Economic Modelling, Elsevier, vol. 40(C), pages 184-190.
    4. Cerqueti, Roy & Costantini, Mauro, 2011. "Testing for rational bubbles in the presence of structural breaks: Evidence from nonstationary panels," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2598-2605, October.
    5. Nektarios Aslanidis & Stilianos Fountas, 2014. "Is real GDP stationary? Evidence from a panel unit root test with cross-sectional dependence and historical data," Empirical Economics, Springer, vol. 46(1), pages 101-108, February.
    6. Mohsen Bahmani-Oskooee & Tsangyao Chang & Kuei-Chiu Lee, 2016. "Panel asymmetric nonlinear unit root test and PPP in Africa," Applied Economics Letters, Taylor & Francis Journals, vol. 23(8), pages 554-558, May.
    7. Roy Cerqueti & Mauro Costantini & Luciano Gutierrez, 2009. "New panel tests to assess inflation persistence," Working Papers 54-2009, Macerata University, Department of Finance and Economic Sciences, revised Oct 2009.
    8. MOON, H.R. & PERRON, Benoit, 2010. "Beyond Panel Unit Root Tests : Using Multiple Testing to Determine the Non-Stationarity Properties of Individual Series in a Panel," Cahiers de recherche 10-2010, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    9. Hanck, Christoph & Czudaj, Robert, 2013. "Nonstationary-Volatility Robust Panel Unit Root Tests and the Great Moderation," Ruhr Economic Papers 434, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    10. Chih-kai Chang & Tsangyao Chang, 2012. "Revisiting the sustainability of current account deficit: SPSM using the panel KSS Test with a Fourier Function," Economics Bulletin, AccessEcon, vol. 32(1), pages 538-550.
    11. Giorgio Canarella & Stephen Miller & Stephen Pollard, 2014. "Purchasing Power Parity Between the UK and Germany: The Euro Era," Open Economies Review, Springer, vol. 25(4), pages 677-699, September.
    12. Sinan Esen & Feyyaz Zeren & Halil Şimdi, 2015. "CDS and Stock Market: Panel Evidence Under Cross-Section Dependency," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 13(1), pages 31-46.
    13. Thomas Goda & Chris Stewart & Alejandro Torres García, 2016. "Absolute Income Inequality and Rising House Prices," Documentos de Trabajo de Valor Público 15247, Universidad EAFIT.
    14. Hayat, Aziz & Ganiev, Bahodir & Tang, Xueli, 2012. "Expectations of future income and real exchange rate movements," Working Papers fe_2012_05, Deakin University, Department of Economics.
    15. Aydemir, Resul & Guloglu, Bulent, 2017. "How do banks determine their spreads under credit and liquidity risks during business cycles?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 147-157.
    16. Nicholas Apergis & Tsangyao Chang & Christina Christou & Rangan Gupta, 2017. "Convergence of Health Care Expenditures Across the US States: A Reconsideration," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 303-316, August.
    17. Václav Žďárek, 2012. "An Empirical Investigation of the Purchasing Power Parity Hypothesis in European Transition Countries," Prague Economic Papers, Prague University of Economics and Business, vol. 2012(3), pages 257-276.
    18. Anyikwa, Izunna & Hamman, Nicolene & Phiri, Andrew, 2018. "Persistence of suicides in G20 countries: SPSM approach to three generations of unit root tests," MPRA Paper 87790, University Library of Munich, Germany.
    19. Akarim, Yasemin Deniz & Sevim, Serafettin, 2013. "The impact of mean reversion model on portfolio investment strategies: Empirical evidence from emerging markets," Economic Modelling, Elsevier, vol. 31(C), pages 453-459.
    20. Pedroni, Peter L. & Vogelsang, Timothy J. & Wagner, Martin & Westerlund, Joakim, 2015. "Nonparametric rank tests for non-stationary panels," Journal of Econometrics, Elsevier, vol. 185(2), pages 378-391.
    21. Furkan Emirmahmutoglu & Rangan Gupta & Stephen M. Miller & Tolga Omay, 2016. "Is Real Per Capita State Personal Income Stationary? New Nonlinear, Asymmetric Panel-Data Evidence," Working papers 2016-20, University of Connecticut, Department of Economics.
    22. Mohamed El Hedi Arouri & Christophe Rault & Robert Sova & Anamaria Sova, 2013. "Market Structure and the Cost of Capital," CESifo Working Paper Series 4097, CESifo.
    23. Raihan, Selim & Abdullah, S M & Barkat, Aroni & Siddiqua, Salina, 2017. "Mean Reversion of the Real Exchange Rate and the validity of PPP Hypothesis in the context of Bangladesh: A Holistic Approach," MPRA Paper 77172, University Library of Munich, Germany.
    24. Saeid Mahdavi & Joakim Westerlund, 2017. "Are state–local government expenditures converging? New evidence based on sequential unit root tests," Empirical Economics, Springer, vol. 53(2), pages 373-403, September.
    25. I‐Chun Tsai & Cheng‐Feng Lee, 2012. "The convergent behavior in REIT markets," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 30(1), pages 42-57, February.
    26. Costantini, Mauro & Lupi, Claudio, 2014. "Identifying I(0) Series in Macro-panels: Are Sequential Panel Selection Methods Useful?," Economics & Statistics Discussion Papers esdp14073, University of Molise, Department of Economics.
    27. Oluwasola E Omoju & Jinkai Li & Jin Zhang & Abdul Rauf & Victor Edem Sosoo, 2020. "Implications of shocks in energy consumption for energy policy in sub-Saharan Africa," Energy & Environment, , vol. 31(6), pages 1077-1097, September.
    28. Mücahit Aydın, 2019. "Investigation of the Validity of Purchasing Power Parity Hypothesis with Fourier Unit Root Tests: The Case of Turkey," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 30(0), pages 35-48, June.
    29. Le, Thai-Ha & Chang, Youngho & Park, Donghyun, 2017. "Energy demand convergence in APEC: An empirical analysis," Energy Economics, Elsevier, vol. 65(C), pages 32-41.
    30. Vasudeva N. R. Murthy & Emmanuel Anoruo, 2009. "Are Per Capita Real GDP Series in African Countries Non-stationary or Non-linear? What does Empirical Evidence Reveal?," Economics Bulletin, AccessEcon, vol. 29(4), pages 2492-2504.
    31. Chortareas, Georgios & Kapetanios, George, 2009. "Getting PPP right: Identifying mean-reverting real exchange rates in panels," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 390-404, February.
    32. Tsangyao Chang & Wen-Chi Liu & Goodness C. Aye & Rangan Gupta, 2016. "Are there housing bubbles in South Africa? Evidence from SPSM-based panel KSS test with a Fourier function," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 18(5), pages 517-532.
    33. Giorgio Canarella & Stephen M. Miller & Stephen K. Pollard, 2012. "Purchasing Power Parity between the UK and the Euro Area," Working Papers 1208, University of Nevada, Las Vegas , Department of Economics.
    34. Matteo Lanzafame, 2014. "Current account sustainability in advanced economies," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 23(7), pages 1000-1017, October.
    35. Cushman, David O. & Michael, Nils, 2011. "Nonlinear trends in real exchange rates: A panel unit root test approach," Journal of International Money and Finance, Elsevier, vol. 30(8), pages 1619-1637.
    36. Chang, Ming-Jen & Su, Che-Yi, 2014. "Hysteresis versus natural rate in Taiwan's unemployment: Evidence from the educational attainment categories," Economic Modelling, Elsevier, vol. 43(C), pages 293-304.
    37. Saadet Kasman & Adnan Kasman & Duygu Ayhan, 2010. "Testing the Purchasing Power Parity Hypothesis for the New Member and Candidate Countries of the European Union: Evidence from Lagrange Multiplier Unit Root Tests with Structural Breaks," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 46(2), pages 53-65, March.
    38. David De Villiers & Andrew Phiri, 2022. "Towards resolving the purchasing power parity (PPP) ‘Puzzle’ in newly industrialized countries (NIC’s)," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 31(2), pages 161-180, February.
    39. Mohsen Bahmani-Oskooee & Tsangyao Chang & Kuei-Chiu Lee, 2014. "Purchasing Power Parity in the BRICS and the MIST Countries: Sequential Panel Selection Method," Review of Economics & Finance, Better Advances Press, Canada, vol. 4, pages 1-12, Feburary.
    40. Meher Manzur, 2018. "Exchange rate economics is always and everywhere controversial," Applied Economics, Taylor & Francis Journals, vol. 50(3), pages 216-232, January.
    41. Feng-Li Lin, 2020. "Do DJIA Firms Reflect Stationary Debt Ratios?," Economies, MDPI, vol. 8(4), pages 1-19, September.
    42. Kutan, Ali M. & Zhou, Su, 2015. "PPP may hold better than you think: Smooth breaks and non-linear mean reversion in real effective exchange rates," Economic Systems, Elsevier, vol. 39(2), pages 358-366.
    43. Chuluun, Tuugi & Eun, Cheol S. & Kiliç, Rehim, 2011. "Investment intensity of currencies and the random walk hypothesis: Cross-currency evidence," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 372-387, February.
    44. Mork, Knut Anton & Trønnes, Haakon Andreas, 2023. "Expected long-term rates of return when short-term returns are serially correlated," International Review of Financial Analysis, Elsevier, vol. 88(C).
    45. Tolga Omay & Mübariz Hasanov & Yongcheol Shin, 2018. "Testing for Unit Roots in Dynamic Panels with Smooth Breaks and Cross-Sectionally Dependent Errors," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 167-193, June.
    46. László KÓNYA, 2023. "Per Capita Income Convergence and Divergence of Selected OECD Countries to and from the US: A Reappraisal for the period 1900-2018," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 23(1), pages 33-56.
    47. Jean-Christian Lambelet & Alexander Mihailov, 2006. "The Triple-Parity Law," Computing in Economics and Finance 2006 33, Society for Computational Economics.
    48. Liu, Tie-Ying & Lee, Chien-Chiang, 2020. "Convergence of the world’s energy use," Resource and Energy Economics, Elsevier, vol. 62(C).
    49. James E Payne & Junsoo Lee, 2024. "Global perspective on the permanent or transitory nature of shocks to tourist arrivals: Evidence from new unit root tests with structural breaks and factors," Tourism Economics, , vol. 30(1), pages 67-103, February.
    50. Lee, Kuei-Chiu, 2014. "Is per capita real GDP stationary in China? Sequential panel selection method," Economic Modelling, Elsevier, vol. 37(C), pages 507-517.
    51. Lee, Cheng-Feng, 2010. "Testing for unemployment hysteresis in nonlinear heterogeneous panels: International evidence," Economic Modelling, Elsevier, vol. 27(5), pages 1097-1102, September.
    52. Takashi Matsuki, 2016. "Linear and nonlinear comovement in Southeast Asian local currency bond markets: a stepwise multiple testing approach," Empirical Economics, Springer, vol. 51(2), pages 591-619, September.
    53. Pei-Chien Lin & Ho-Chuan Huang, 2012. "Convergence in income inequality? evidence from panel unit root tests with structural breaks," Empirical Economics, Springer, vol. 43(1), pages 153-174, August.
    54. Matsuki, Takashi & Sugimoto, Kimiko, 2013. "Stationarity of Asian real exchange rates: An empirical application of multiple testing to nonstationary panels with a structural break," Economic Modelling, Elsevier, vol. 34(C), pages 52-58.
    55. Ogrokhina, Olena, 2019. "Persistence of prices in the Eurozone capital cities: Evidence from the Economist Intelligence Unit City Data," Economic Modelling, Elsevier, vol. 76(C), pages 330-338.
    56. Westerlund, Joakim & Thuraisamy, Kannan & Sharma, Susan, 2015. "On the use of panel cointegration tests in energy economics," Energy Economics, Elsevier, vol. 50(C), pages 359-363.
    57. Georgios Chortareas & George Kapetanios, 2004. "How Puzzling is the PPP Puzzle? An Alternative Half-Life Measure of Convergence to PPP," Working Papers 522, Queen Mary University of London, School of Economics and Finance.
    58. Tolga Omay & Muhammad Shahbaz & Chris Stewart, 2021. "Is there really hysteresis in the OECD unemployment rates? New evidence using a Fourier panel unit root test," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(4), pages 875-901, November.
    59. Baharumshah & Siew-Voon Soon & Wohar, 2015. "Parity reversion in the Asian real exchange rates: new evidence from the local-persistent model," Applied Economics, Taylor & Francis Journals, vol. 47(59), pages 6395-6408, December.
    60. Bernard Njindan Iyke, 2019. "A Test Of The Efficiency Of The Foreign Exchange Market In Indonesia," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 21(12th BMEB), pages 439-464, January.
    61. Adiguzel, Ugur & Sahbaz, Ahmet & Ozcan, Ceyhun Can & Nazlioglu, Saban, 2014. "The behavior of Turkish exchange rates: A panel data perspective," Economic Modelling, Elsevier, vol. 42(C), pages 177-185.
    62. Corakci, Aysegul & Omay, Tolga, 2023. "Is there convergence in renewable energy deployment? Evidence from a new panel unit root test with smooth and sharp structural breaks," Renewable Energy, Elsevier, vol. 205(C), pages 648-662.
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    1. Manabu Asai & Michael McAleer, 2014. "Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance," Documentos de Trabajo del ICAE 2014-05, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    2. Jinghui Chen & Masahito Kobayashi & Michael McAleer, 2017. "Testing for Volatility Co-movement in Bivariate Stochastic Volatility Models," Tinbergen Institute Discussion Papers 17-022/III, Tinbergen Institute.
    3. Andrea Cipollini & George Kapetanios, 2004. "A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data," Working Papers 506, Queen Mary University of London, School of Economics and Finance.
    4. Rodríguez Caballero, Carlos Vladimir, 2017. "Estimation of a Dynamic Multilevel Factor Model with possible long-range dependence," DES - Working Papers. Statistics and Econometrics. WS 24614, Universidad Carlos III de Madrid. Departamento de Estadística.
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    7. Chen, J. & Kobayashi, M. & McAleer, M.J., 2016. "Testing for a Common Volatility Process and Information Spillovers in Bivariate Financial Time Series Models," Econometric Institute Research Papers EI2016-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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  100. George Kapetanios, 2004. "A Bootstrap Procedure for Panel Datasets with Many Cross-Sectional Units," Working Papers 523, Queen Mary University of London, School of Economics and Finance.

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    25. Yang, Haisheng & He, Jie & Chen, Shaoling, 2015. "The fragility of the Environmental Kuznets Curve: Revisiting the hypothesis with Chinese data via an “Extreme Bound Analysis”," Ecological Economics, Elsevier, vol. 109(C), pages 41-58.
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    36. Benjamin Williams, 2018. "Identification of a Nonseparable Model under Endogeneity using Binary Proxies for Unobserved Heterogeneity," Working Papers 2018-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
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    52. Claudiu Tiberiu Albulescu & Camélia Turcu, 2022. "Productivity, financial performance, and corporate governance: evidence from Romanian R&D firms," Applied Economics, Taylor & Francis Journals, vol. 54(51), pages 5956-5975, November.
    53. Daniel Czarnowske, 2022. "A Classifier-Lasso Approach for Estimating Production Functions with Latent Group Structures," Papers 2203.02220, arXiv.org.
    54. Günster, N.K. & Kole, H.J.W.G. & Jacobsen, B., 2009. "Riding Bubbles," ERIM Report Series Research in Management ERS-2009-058-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    55. Everaert, Gerdie & Pozzi, Lorenzo, 2007. "Bootstrap-based bias correction for dynamic panels," Journal of Economic Dynamics and Control, Elsevier, vol. 31(4), pages 1160-1184, April.
    56. Westerlund, Joakim & Sharma, Susan Sunila, 2019. "Panel evidence on the ability of oil returns to predict stock returns in the G7 area," Energy Economics, Elsevier, vol. 77(C), pages 3-12.
    57. Fernández-Val, Iván & Gao, Wayne Yuan & Liao, Yuan & Vella, Francis, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," IZA Discussion Papers 15236, Institute of Labor Economics (IZA).
    58. Giovanni Forchini & Raoul Theler, 2023. "Semi-parametric modelling of inefficiencies in stochastic frontier analysis," Journal of Productivity Analysis, Springer, vol. 59(2), pages 135-152, April.
    59. Giuseppe Arbia, 2011. "A Lustrum of SEA: Recent Research Trends Following the Creation of the Spatial Econometrics Association (2007--2011)," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(4), pages 377-395, July.
    60. George Batta & Ricardo Sucre Heredia & Marc Weidenmier, 2014. "Political Connections and Accounting Quality under High Expropriation Risk," European Accounting Review, Taylor & Francis Journals, vol. 23(4), pages 485-517, December.
    61. Sigmund, Michael & Ferstl, Robert, 2021. "Panel vector autoregression in R with the package panelvar," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 693-720.
    62. Ignace De Vos & Gerdie Everaert, 2016. "Bias-Corrected Common Correlated Effects Pooled Estimation In Homogeneous Dynamic Panels," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/920, Ghent University, Faculty of Economics and Business Administration.
    63. Miller, Steve, 2020. "Causal forest estimation of heterogeneous and time-varying environmental policy effects," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    64. Antonio F. Galvao & Gabriel Montes-Rojas, 2015. "On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study," Econometrics, MDPI, vol. 3(3), pages 1-13, September.
    65. Ozcan, Burcu & Tzeremes, Panayiotis G. & Tzeremes, Nickolaos G., 2020. "Energy consumption, economic growth and environmental degradation in OECD countries," Economic Modelling, Elsevier, vol. 84(C), pages 203-213.
    66. Max Vilgalys, 2023. "A Machine Learning Approach to Measuring Climate Adaptation," Papers 2302.01236, arXiv.org.
    67. Gerdie Everaert & Lorenzo Pozzi, 2022. "Encompassing measures of international consumption risk sharing and their link with trade and financial globalization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 433-449, March.
    68. Sanjoy Sinha & Abdus Sattar, 2015. "Inference in semi-parametric spline mixed models for longitudinal data," METRON, Springer;Sapienza Università di Roma, vol. 73(3), pages 377-395, December.
    69. De Rossi, Giuliano & Steliaros, Michael, 2022. "The Shift from Active to Passive and its Effect on Intraday Stock Dynamics," Journal of Banking & Finance, Elsevier, vol. 143(C).
    70. Trapani, Lorenzo, 2013. "On bootstrapping panel factor series," Journal of Econometrics, Elsevier, vol. 172(1), pages 127-141.

  101. Andrew P. Blake & George Kapetanios, 2004. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Working Papers 508, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. KANAZAWA, Nobuyuki & 金澤, 伸幸, 2018. "Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks," Discussion paper series HIAS-E-64, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    2. Lee Jinu, 2019. "A Neural Network Method for Nonlinear Time Series Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-18, January.

  102. George Kapetanios & Tony Yates, 2004. "Estimating time-variation in measurement error from data revisions; an application to forecasting in dynamic models," Bank of England working papers 238, Bank of England.

    Cited by:

    1. Alastair Cunningham & Jana Eklund & Christopher Jeffery & George Kapetanios & Vincent Labhard, 2007. "A state space approach to extracting the signal from uncertain data," Bank of England working papers 336, Bank of England.
    2. Lavan Mahadeva & Alex Muscatelli, 2005. "National Accounts Revisions and Output Gap Estimates in a Model of Monetary Policy with Data Uncertainty," Discussion Papers 14, Monetary Policy Committee Unit, Bank of England.
    3. Paul Downward & Andrew Mearman, 2005. "Methodological Triangulation at the Bank of England:An Investigation," Working Papers 0505, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    4. Paul Downward & Andrew Mearman, 2008. "Decision-making at the Bank of England: a critical appraisal," Oxford Economic Papers, Oxford University Press, vol. 60(3), pages 385-409, July.
    5. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    6. Alastair Cunningham & Chris Jeffery & George Kapetanios & Vincent Labhard, 2007. "A State Space Approach To The Policymaker's Data Uncertainty Problem," Money Macro and Finance (MMF) Research Group Conference 2006 168, Money Macro and Finance Research Group.

  103. George Kapetanios & Yongcheol Shin, 2003. "Testing for Nonstationary Long Memory against Nonlinear Ergodic Models," Working Papers 500, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Kuswanto, Heri & Sibbertsen, Philipp, 2009. "Testing for Long Memory Against ESTAR Nonlinearities," Hannover Economic Papers (HEP) dp-427, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Gilles Dufrénot & Dominique Guegan & Anne Peguin-Feissolle, 2005. "Long-memory dynamics in a SETAR model - Applications to stock markets," Post-Print halshs-00179339, HAL.
    3. George Kapetanios & Yongcheol Shin, 2011. "Testing the Null Hypothesis of Nonstationary Long Memory Against the Alternative Hypothesis of a Nonlinear Ergodic Model," Econometric Reviews, Taylor & Francis Journals, vol. 30(6), pages 620-645.
    4. Gil-Alana, Luis A. & Cunado, Juncal & Gupta, Rangan, 2017. "Evidence of persistence in U.S. short and long-term interest rates," Journal of Policy Modeling, Elsevier, vol. 39(5), pages 775-789.
    5. Luis Alberiko Gil-Alaña & Borja Balprad & Guglielmo Maria Caporale, 2015. "African Growth, Non-Linearities and Strong Dependence: An Empirical Study," NCID Working Papers 12/2015, Navarra Center for International Development, University of Navarra.
    6. Thabo M. Mokoena & Rangan Gupta & Reneé Van Eyden, 2009. "Testing For Fractional Integration In Southern African Development Community Real Exchange Rates," South African Journal of Economics, Economic Society of South Africa, vol. 77(4), pages 531-537, December.

  104. George Kapetanios & Yongcheol Shin, 2003. "GLS Detrending-Based Unit Root Tests in Nonlinear STAR and SETAR Frameworks," Edinburgh School of Economics Discussion Paper Series 108, Edinburgh School of Economics, University of Edinburgh.

    Cited by:

    1. Rehim Kılıc, 2011. "Testing for a unit root in a stationary ESTAR process," Econometric Reviews, Taylor & Francis Journals, vol. 30(3), pages 274-302.
    2. McAdam, Peter & Muck, Jakub & Growiec, Jakub, 2015. "Will the true labor share stand up?," Working Paper Series 1806, European Central Bank.

  105. George Kapetanios, 2003. "Determining the Stationarity Properties of Individual Series in Panel Datasets," Working Papers 495, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. MOON, H.R. & PERRON, Benoit, 2010. "Beyond Panel Unit Root Tests : Using Multiple Testing to Determine the Non-Stationarity Properties of Individual Series in a Panel," Cahiers de recherche 10-2010, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    2. Luisa Corrado & Thanasis Stengos & Melvyn Weeks & M. Ege Yazgan, 2019. "Robust Tests for Convergence Clubs," CEIS Research Paper 451, Tor Vergata University, CEIS, revised 14 Feb 2019.
    3. Chortareas, Georgios & Kapetanios, George, 2009. "Getting PPP right: Identifying mean-reverting real exchange rates in panels," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 390-404, February.
    4. Beylunioglu Fuat C. & Stengos Thanasis & Yazgan M. Ege, 2017. "Detecting capital market convergence clubs," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(3), pages 1-14, June.
    5. Perevyshin, Yu. & Skrobotov, A., 2017. "The Price Convergence of Individual Goods in the Russian Regions," Journal of the New Economic Association, New Economic Association, vol. 35(3), pages 71-102.
    6. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Stephan Smeekes, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 398-415, May.
    7. Johan Blomquist & Joakim Westerlund, 2016. "Panel bootstrap tests of slope homogeneity," Empirical Economics, Springer, vol. 50(4), pages 1359-1381, June.
    8. Pesaran, M. Hashem, 2012. "On the interpretation of panel unit root tests," Economics Letters, Elsevier, vol. 116(3), pages 545-546.
    9. Smeekes, S., 2011. "Bootstrap sequential tests to determine the stationary units in a panel," Research Memorandum 003, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    10. Kapetanios, George, 2006. "Cluster analysis of panel data sets using non-standard optimisation of information criteria," Journal of Economic Dynamics and Control, Elsevier, vol. 30(8), pages 1389-1408, August.
    11. Chen, Zhongfei & Matousek, Roman & Stewart, Chris & Webb, Rob, 2019. "Do rating agencies exhibit herding behaviour? Evidence from sovereign ratings," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 57-70.

  106. George Kapetanios & Yongcheol Shin & Andy Snell, 2003. "Testing for Cointegration in Nonlinear STAR Error Correction Models," Working Papers 497, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Easaw Joshy & Golinelli Roberto, 2010. "Households Forming Inflation Expectations: Active and Passive Absorption Rates," The B.E. Journal of Macroeconomics, De Gruyter, vol. 10(1), pages 1-32, November.
    2. Tsangyao Chang & Tsung-pao Wu & Rangan Gupta, 2013. "Are House Prices in South Africa Really Non-Stationary? Evidence from SPSM-Based Panel KSS Test with a Fourier Function," Working Papers 201324, University of Pretoria, Department of Economics.
    3. Tsangyao Chang & Yang-Cheng Lu, 2006. "Equity Diversification in Two Chinese Share Markets: Old Wine and New Bottle," Economics Bulletin, AccessEcon, vol. 7(4), pages 1-7.
    4. Tsangyao Chang & Wen-Chi Liu & Goodness C. Aye & Rangan Gupta, 2016. "Are there housing bubbles in South Africa? Evidence from SPSM-based panel KSS test with a Fourier function," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 18(5), pages 517-532.
    5. Liew, Venus Khim-Sen & Baharumshah, Ahmad Zubaidi & Habibullah, Muzafar Shah & Midi, Habshah, 2008. "Monetary exchange rate model: supportive evidence from nonlinear testing procedures," MPRA Paper 7293, University Library of Munich, Germany.
    6. J. Easaw J. & R. Golinelli, 2009. "Households Forming Inflation Expectations: Who Are the 'Active' and 'Passive' Learners?," Working Papers 675, Dipartimento Scienze Economiche, Universita' di Bologna.
    7. Gabriella Deborah Legrenzi & Costas Milas, 2012. "Fiscal Policy Sustainability, Economic Cycle and Financial Crises: The Case of the GIPS," CESifo Working Paper Series 4001, CESifo.
    8. Giorgio Canarella & Stephen Miller & Stephen Pollard, 2012. "Unit Roots and Structural Change," Urban Studies, Urban Studies Journal Limited, vol. 49(4), pages 757-776, March.
    9. Tsangyao Chang & Chien-Chung Nieh & Ching-Chun Wei, 2006. "Analysis of long-run benefits from international equity diversification between Taiwan and its major European trading partners: an empirical note," Applied Economics, Taylor & Francis Journals, vol. 38(19), pages 2277-2283.
    10. Tsangyao CHANG & Yifei CAI & Wen-Yi CHEN, 2017. "Are Suicide Rate Fluctuations Transitory or Permanent? Panel KSS Unit Root Test with a Fourier Function through the Sequential Panel Selection Method," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-17, September.

  107. Georgios Chortareas & George Kapetanios & Merih Uctum, 2003. "An Investigation of Current Account Solvency in Latin America Using Non Linear Stationarity Tests," Working Papers 485, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Rihab Bousnina & Srdjan Redzepagic & Foued Badr Gabsi, 2021. "Sustainability of current account balances in MENA countries: threshold cointegration approach," Economic Change and Restructuring, Springer, vol. 54(1), pages 241-264, February.

  108. Luca Benati & George Kapetanios, 2003. "Structural Breaks in Inflation Dynamics," Computing in Economics and Finance 2003 169, Society for Computational Economics.

    Cited by:

    1. Gillman, Max & Nakov, Anton, 2005. "Granger Causality of the Inflation-Growth Mirror in Accession Countries," CEPR Discussion Papers 4845, C.E.P.R. Discussion Papers.
    2. Romero-Ávila, Diego & Usabiaga, Carlos, 2009. "The hypothesis of a unit root in OECD inflation revisited," Journal of Economics and Business, Elsevier, vol. 61(2), pages 153-161.
    3. Carlos Santos & Maria Alberta Oliveira, 2010. "Assessing French inflation persistence with impulse saturation break tests and automatic general-to-specific modelling," Applied Economics, Taylor & Francis Journals, vol. 42(12), pages 1577-1589.
    4. Mojon, Benoît, 2005. "When did unsystematic monetary policy have an effect on inflation?," Working Paper Series 559, European Central Bank.
    5. Todd E. Clark, 2003. "Disaggregate evidence on the persistence of consumer price inflation," Research Working Paper RWP 03-11, Federal Reserve Bank of Kansas City.
    6. Bilke, Laurent, 2005. "Break in the mean and persistence of inflation: a sectoral analysis of French CPI," Working Paper Series 463, European Central Bank.
    7. Corvoisier, Sandrine & Mojon, Benoît, 2005. "Breaks in the mean of inflation: how they happen and what to do with them," Working Paper Series 451, European Central Bank.
    8. Tianfeng Li & June Wei, 2015. "Multiple Structural Breaks and Inflation Persistence: Evidence from China," Asian Economic Journal, East Asian Economic Association, vol. 29(1), pages 1-20, March.
    9. Denise Osborn & Marianne Sensier, 2007. "UK inflation: persistance, seasonality and monetary policy," Economics Discussion Paper Series 0716, Economics, The University of Manchester.
    10. Logan Rangasamy, 2009. "Inflation Persistence And Core Inflation: The Case Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 77(3), pages 430-444, September.
    11. Jamie Armour, 2006. "An Evaluation of Core Inflation Measures," Staff Working Papers 06-10, Bank of Canada.
    12. Sandra Eickmeier, 2009. "Comovements and heterogeneity in the euro area analyzed in a non-stationary dynamic factor model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 933-959.
    13. Eickmeier, Sandra & Moll, Katharina, 2008. "The global dimension of inflation: evidence from factor-augmented Phillips curves," Discussion Paper Series 1: Economic Studies 2008,16, Deutsche Bundesbank.
    14. Dirk Ulbricht, 2016. "It is not structural breaks that earn average forecasts their fame," Economics Bulletin, AccessEcon, vol. 36(2), pages 1250-1259.
    15. Bilke, L., 2005. "Break in the Mean and Persistence of Inflation: a Sectoral Analysis of French CPI," Working papers 122, Banque de France.

  109. George Kapetanios, 2003. "Determining the Poolability of Individual Series in Panel Datasets," Working Papers 499, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Jean-Baptiste Hasse & Quentin Lajaunie, 2020. "Does the Yield Curve Signal Recessions? New Evidence from an International Panel Data Analysis," AMSE Working Papers 2013, Aix-Marseille School of Economics, France.
    2. Castagnetti, Carolina & Rossi, Eduardo & Trapani, Lorenzo, 2015. "Inference on factor structures in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 184(1), pages 145-157.
    3. Bertrand Candelon & Elena Ivona Dumitrescu & Christophe Hurlin, 2014. "Currency Crises Early Warning Systems: Why They Should Be Dynamic," Post-Print hal-01385975, HAL.
    4. Chortareas, Georgios & Kapetanios, George, 2009. "Getting PPP right: Identifying mean-reverting real exchange rates in panels," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 390-404, February.
    5. Bertrand Candelon & Elena Ivona Dumitrescu & Christophe Hurlin, 2012. "How to Evaluate an Early Warning System? Towards a Unified Statistical Framework for Assessing Financial Crises Forecasting Methods," Post-Print hal-01385900, HAL.
    6. van den Berg, Jeroen & Candelon, Bertrand & Urbain, Jean-Pierre, 2008. "A cautious note on the use of panel models to predict financial crises," Economics Letters, Elsevier, vol. 101(1), pages 80-83, October.
    7. Trapani, Lorenzo, 2021. "Inferential theory for heterogeneity and cointegration in large panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 474-503.
    8. Perevyshin, Yu. & Skrobotov, A., 2017. "The Price Convergence of Individual Goods in the Russian Regions," Journal of the New Economic Association, New Economic Association, vol. 35(3), pages 71-102.
    9. Johan Blomquist & Joakim Westerlund, 2016. "Panel bootstrap tests of slope homogeneity," Empirical Economics, Springer, vol. 50(4), pages 1359-1381, June.
    10. Pesaran, M. Hashem, 2012. "On the interpretation of panel unit root tests," Economics Letters, Elsevier, vol. 116(3), pages 545-546.
    11. Kapetanios, George, 2006. "Cluster analysis of panel data sets using non-standard optimisation of information criteria," Journal of Economic Dynamics and Control, Elsevier, vol. 30(8), pages 1389-1408, August.
    12. Chen, Zhongfei & Matousek, Roman & Stewart, Chris & Webb, Rob, 2019. "Do rating agencies exhibit herding behaviour? Evidence from sovereign ratings," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 57-70.
    13. Zigraiova, Diana & Jakubik, Petr, 2015. "Systemic event prediction by an aggregate early warning system: An application to the Czech Republic," Economic Systems, Elsevier, vol. 39(4), pages 553-576.

  110. Valerie Herzberg & George Kapetanios & Simon Price, 2003. "Import prices and exchange rate pass-through: theory and evidence from the United Kingdom," Bank of England working papers 182, Bank of England.

    Cited by:

    1. Toshitaka Sekine, 2006. "Time-varying exchange rate pass-through: experiences of some industrial countries," BIS Working Papers 202, Bank for International Settlements.
    2. Musti, Babagana Mala & Siddiki, Jalal Uddin, 2018. "Nonlinear and Asymmetric Exchange Rate Pass-Through to Consumer Prices In Nigeria: Evidence from a Smooth Transition Autoregressive Model," Economics Discussion Papers 2018-3, School of Economics, Kingston University London.
    3. Ben Cheikh, Nidhaleddine, 2012. "Non-linearities in exchange rate pass-through: Evidence from smooth transition models," MPRA Paper 39258, University Library of Munich, Germany.
    4. Ben Cheikh, Nidhaleddine, 2012. "Asymmetric exchange rate pass-through in the Euro area: New evidence from smooth transition models," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 6, pages 1-28.
    5. Ben Cheikh, Nidhaleddine, 2013. "The Pass-Through of Exchange Rate in the Context of the European Sovereign Debt Crisis," MPRA Paper 47308, University Library of Munich, Germany.
    6. Martins Bitans, 2004. "Pass-Through of Exchange Rates to Domestic Prices in East European Countries and the Role of Economic Enviroment," Working Papers 2004/04, Latvijas Banka.
    7. Raphael Brun-Aguerre & Ana-Maria Fuertes & Matthew Greenwood-Nimmo, 2017. "Heads I win; tails you lose: asymmetry in exchange rate pass-through into import prices," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 587-612, February.
    8. Nidhaleddine Ben Cheikh & Christophe Rault, 2016. "The Pass-Through of Exchange Rate in the Context of the European Sovereign Debt Crisis," Post-Print hal-03533323, HAL.
    9. Jinbin Wang & Nan Li, 2010. "Exchange rate pass-through: The case of China," Frontiers of Economics in China, Springer;Higher Education Press, vol. 5(3), pages 356-374, September.
    10. Mr. Hamid Faruqee, 2004. "Exchange Rate Pass-Through in the Euro Area: The Role of Asymmetric Pricing Behavior," IMF Working Papers 2004/014, International Monetary Fund.
    11. Bussière, Matthieu, 2007. "Exchange rate pass-through to trade prices: the role of non-linearities and asymmetries," Working Paper Series 822, European Central Bank.
    12. Ida Wolden Bache & Bjørn E. Naug, 2008. "Estimating New Keynesian import price models," Working Paper 2007/15, Norges Bank.
    13. Brun-Aguerre, Raphael & Fuertes, Ana-Maria & Phylaktis, Kate, 2012. "Exchange rate pass-through into import prices revisited: What drives it?," Journal of International Money and Finance, Elsevier, vol. 31(4), pages 818-844.
    14. Mototsugu Shintani & Akiko Terada-Hagiwara & Tomoyoshi Yabu, 2009. "Exchange Rate Pass-Through and Inflation: A Nonlinear Time Series Analysis," Vanderbilt University Department of Economics Working Papers 0920, Vanderbilt University Department of Economics.
    15. Kiliç, Rehim, 2016. "Regime-dependent exchange-rate pass-through to import prices," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 295-308.
    16. Kellermann, Kersten & Schlag, Carsten-Henning, 2011. "Frankenstärke und Importpreisreagibilität: Kurz-, mittel- und langfristige Effekte," KOFL Working Papers 10, Konjunkturforschungsstelle Liechtenstein (KOFL), Vaduz.
    17. Nidhaleddine Ben Cheikh, 2012. "Nonlinear Mechanism of the Exchange Rate Pass-Through: Does Business Cycle Matter?," Working Papers halshs-00731502, HAL.
    18. Julio-Román, Juan Manuel, 2019. "Estimating the Exchange Rate Pass-Through: A Time-Varying Vector Auto-Regression with Residual Stochastic Volatility Approach," Working papers 21, Red Investigadores de Economía.
    19. Jovic, Srdjan & Smigic Miladinovic, Jasmina & Micic, Radmila & Markovic, Sanja & Rakic, Goran, 2019. "Analysing of exchange rate and gross domestic product (GDP) by adaptive neuro fuzzy inference system (ANFIS)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 333-338.
    20. Reginaldo P. Nogueira Junior & Miguel Leon-Ledesma, 2008. "Exchange Rate Pass-Through Into Inflation: The Role of Asymmetries and NonLinearities," Studies in Economics 0801, School of Economics, University of Kent.
    21. Balcilar, Mehmet & Usman, Ojonugwa, 2021. "Exchange rate and oil price pass-through in the BRICS countries: Evidence from the spillover index and rolling-sample analysis," Energy, Elsevier, vol. 229(C).
    22. John Lewis, 2020. "Exchange rate passthrough at the micro and macro levels in a small open economy: Evidence from several million unit values," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(3), pages 1274-1291, August.
    23. Torben M. Andersen & Allan Sørensen, 2008. "Product Market Integration and Heterogeneity—Rent Sharing and Pricing to Market," Review of International Economics, Wiley Blackwell, vol. 16(2), pages 268-284, May.
    24. Ben Cheikh, Nidhaleddine & Ben Zaied, Younes, 2020. "Revisiting the pass-through of exchange rate in the transition economies: New evidence from new EU member states," Journal of International Money and Finance, Elsevier, vol. 100(C).
    25. Suleyman Hilmi Kal & Ferhat Arslaner & Nuran Arslaner, 2015. "Sources of Asymmetry and Non-linearity in Pass-Through of Exchange Rate and Import Price to Consumer Price Inflation for the Turkish Economy during Inflation Targeting Regime," Working Papers 1530, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    26. Páll, Zsombor, 2015. "Three essays on the Russian wheat export," Studies on the Agricultural and Food Sector in Transition Economies, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), volume 80, number 80, September.
    27. Andreas Benedictow & Pål Boug, 2013. "Trade liberalisation and exchange rate pass-through: the case of textiles and wearing apparels," Empirical Economics, Springer, vol. 45(2), pages 757-788, October.
    28. Ozkan, Ibrahim & Erden, Lutfi, 2015. "Time-varying nature and macroeconomic determinants of exchange rate pass-through," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 56-66.
    29. Mehmet Balcilar & Usman Ojonugwa, 2018. "Exchange rate and oil price pass-through to inflation in BRICS countries: Evidence from the spillover index and rolling-sample analysis," Working Papers 15-45, Eastern Mediterranean University, Department of Economics.
    30. Przystupa, Jan & Wróbel, Ewa, 2009. "Asymmetry of the exchange rate pass-through: An exercise on the Polish data," MPRA Paper 17660, University Library of Munich, Germany.
    31. Aaron Drew & Rishab Sethi, 2007. "The transmission mechanism of New Zealand monetary policy," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 70, June.

  111. Andrew P. Blake & George Kapetanios, 2003. "Testing for ARCH in the Presence of Nonlinearity of Unknown Form in the Conditional Mean," Working Papers 496, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Mohamed El Hedi Arouri & Shawkat Hammoudeh & Duc Khuong Nguyen & Amine Lahiani, 2013. "On the short- and long-run efficiency of energy and precious metal markets," Working Papers hal-00798036, HAL.
    2. Daiki Maki & Yasushi Ota, 2019. "Robust tests for ARCH in the presence of the misspecified conditional mean: A comparison of nonparametric approches," Papers 1907.12752, arXiv.org, revised Sep 2019.
    3. Richard Ashley, 2012. "On the Origins of Conditional Heteroscedasticity in Time Series," Korean Economic Review, Korean Economic Association, vol. 28, pages 5-25.
    4. Carlos Escanciano, J., 2008. "Joint and marginal specification tests for conditional mean and variance models," Journal of Econometrics, Elsevier, vol. 143(1), pages 74-87, March.
    5. Erdenebat Bataa & Andrew Vivian & Mark Wohar, 2019. "Changes in the relationship between short‐term interest rate, inflation and growth: evidence from the UK, 1820–2014," Bulletin of Economic Research, Wiley Blackwell, vol. 71(4), pages 616-640, October.
    6. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "Some thoughts on accurate characterization of stock market indexes trends in conditions of nonlinear capital flows during electronic trading at stock exchanges in global capital markets," MPRA Paper 49921, University Library of Munich, Germany.
    7. KANAZAWA, Nobuyuki & 金澤, 伸幸, 2018. "Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks," Discussion paper series HIAS-E-64, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    8. Meher Manzur, 2018. "Exchange rate economics is always and everywhere controversial," Applied Economics, Taylor & Francis Journals, vol. 50(3), pages 216-232, January.
    9. Daiki Maki & Yasushi Ota, 2021. "Testing for Time-Varying Properties Under Misspecified Conditional Mean and Variance," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1167-1182, April.
    10. Wasel Shadat, 2011. "On the Nonparametric Tests of Univariate GARCH Regression Models," Economics Discussion Paper Series 1115, Economics, The University of Manchester.
    11. Anatolyev, Stanislav & Tarasyuk, Irina, 2015. "Missing mean does no harm to volatility!," Economics Letters, Elsevier, vol. 134(C), pages 62-64.
    12. Daiki Maki & Yasushi Ota, 2019. "Testing for time-varying properties under misspecified conditional mean and variance," Papers 1907.12107, arXiv.org, revised Aug 2019.
    13. Martin Vance L. & Sarkar Saikat & Kanto Antti Jaakko, 2014. "Modelling nonlinearities in equity returns: the mean impact curve analysis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(1), pages 51-72, February.
    14. Pavlidis Efthymios G & Paya Ivan & Peel David A, 2010. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-40, May.
    15. Kyrtsou, Catherine, 2008. "Re-examining the sources of heteroskedasticity: The paradigm of noisy chaotic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6785-6789.
    16. Lee Jinu, 2019. "A Neural Network Method for Nonlinear Time Series Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-18, January.
    17. Sitzia, Bruno & Iovino, Doriana, 2008. "Nonlinearities in Exchange rates: Double EGARCH Threshold Models for Forecasting Volatility," MPRA Paper 8661, University Library of Munich, Germany.
    18. Sadek Melhem & Mahmoud Melhem, 2012. "Comments on “Re-examining the source of Heteroskedasticity: The paradigm of noisy chaotic models”," Working Papers 12-13, LAMETA, Universtiy of Montpellier, revised Apr 2012.

  112. Hasan Bakhshi & George Kapetanios & Anthony Yates, 2003. "Rational expectations and fixed-event forecasts: an application to UK inflation," Bank of England working papers 176, Bank of England.

    Cited by:

    1. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
    2. Winkelried, Diego, 2014. "Inferring inflation expectations from fixed-event forecasts," Working Papers 2014-016, Banco Central de Reserva del Perú.
    3. Pedersen, Michael, 2015. "What affects the predictions of private forecasters? The role of central bank forecasts in Chile," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1043-1055.
    4. Kuethe, Todd H. & Hubbs, Todd & Sanders, Dwight R., 2018. "Evaluating the USDA’s Net Farm Income Forecast," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(3), September.
    5. Wagner Piazza Gaglianone & João Victor Issler, 2014. "Microfounded Forecasting," Working Papers Series 372, Central Bank of Brazil, Research Department.
    6. Winkelried, Diego, 2023. "Simple interpolations of inflation expectations," Economics Letters, Elsevier, vol. 229(C).
    7. Gaglianone, Wagner Piazza & Pereira, Ana Luiza Louzada, 2005. "Um ensaio sobre expectativas da taxa de câmbio no Brasil [An essay on the foreign exchange rate expectations in Brazil]," MPRA Paper 20840, University Library of Munich, Germany.
    8. Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2021. "Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 290-314.
    9. Simón Sosvilla-Rivero & María del Carmen Ramos-Herrera, 2018. "Inflation, real economic growth and unemployment expectations: an empirical analysis based on the ECB survey of professional forecasters," Applied Economics, Taylor & Francis Journals, vol. 50(42), pages 4540-4555, September.
    10. Michael Berlemann & Forrest Nelson, 2005. "Forecasting Inflation via Experimental Stock Markets Some Results from Pilot Markets," ifo Working Paper Series 10, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    11. Péter Gábriel & Klára Pintér, 2006. "Whom should we believe? Information content of the yield curve and analysts’ expectations," MNB Bulletin (discontinued), Magyar Nemzeti Bank (Central Bank of Hungary), vol. 1(2), pages 6-13, December.
    12. Wolfgang Nierhaus, 2008. "Wirtschaftskonjunktur 2007: Prognose und Wirklichkeit," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 61(03), pages 21-26, February.
    13. Berlemann, Michael, 2008. "Forecasting the ECB's main refinancing rate. A field experiment," Economics Letters, Elsevier, vol. 99(2), pages 379-383, May.
    14. Capistrán Carlos, 2007. "Optimality Tests for Multi-Horizon Forecasts," Working Papers 2007-14, Banco de México.
    15. Hakan Kara & Hande Kucuk Tuger, 2005. "Some Evidence on the Irrationality of Inflation Expectations in Turkey," Working Papers 0512, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

  113. Andrea Cipollini & George Kapetanios, 2003. "A Dynamic Factor Analysis of Financial Contagion in Asia," Working Papers 498, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Andrea Cipollini & George Kapetanios, 2008. "Forecasting Financial Crises and Contagion in Asia using Dynamic Factor Analysis," Center for Economic Research (RECent) 014, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".

  114. George Kapetanios & Yongcheol Shin, 2003. "Unit Root Tests in Three-Regime SETAR Models," Edinburgh School of Economics Discussion Paper Series 104, Edinburgh School of Economics, University of Edinburgh.

    Cited by:

    1. Kapetanios, George & Shin, Yongcheol, 2008. "GLS detrending-based unit root tests in nonlinear STAR and SETAR models," Economics Letters, Elsevier, vol. 100(3), pages 377-380, September.
    2. Zisimos Koustas & Jean-Francois Lamarche & Apostolos Serletis, 2006. "Threshold Random Walks in the U.S. Stock Market," Working Papers 0602, Brock University, Department of Economics, revised May 2006.
    3. Yang, Yang & Zhao, Zhao, 2020. "Quantile nonlinear unit root test with covariates and an application to the PPP hypothesis," Economic Modelling, Elsevier, vol. 93(C), pages 728-736.
    4. Kamrul Hassan & Ariful Hoque & Ananth Rao, 2015. "Revisiting the Link Between Stock Prices and Goods Prices in OECD Countries," Australian Economic Papers, Wiley Blackwell, vol. 54(3), pages 135-150, September.
    5. Frédérique Bec & Mélika Ben Salem, 2004. "L'ajustement à seuil des processus cointégrés. Que sait-on des modèles à trois régimes ?," Revue d'économie politique, Dalloz, vol. 114(4), pages 467-488.
    6. Sollis, Robert, 2011. "Testing the unit root hypothesis against TAR nonlinearity using STAR-based tests," Economics Letters, Elsevier, vol. 112(1), pages 19-22, July.
    7. Osińska, Magdalena & Kufel, Tadeusz & Blazejowski, Marcin & Kufel, Pawel, 2016. "Does economic growth really depend on the magnitude of debt? A threshold model approach," MPRA Paper 71476, University Library of Munich, Germany.
    8. Basci Erdem & Caner Mehmet, 2005. "Are Real Exchange Rates Nonlinear or Nonstationary? Evidence from a New Threshold Unit Root Test," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-21, December.
    9. Myunghwan Seo, 2004. "Unit Root Test in a Threshold Autoregression: Asymptotic Theory and Residual-based Block Bootstrap," Econometric Society 2004 North American Summer Meetings 494, Econometric Society.
    10. Rodolphe Blavy & Luciana Juvenal, 2008. "Mexico's integration into NAFTA markets: a view from sectoral real exchange rates," Working Papers 2008-046, Federal Reserve Bank of St. Louis.
    11. George Kapetanios & Yongcheol Shin & Andy Snell, 2003. "Testing for Cointegration in Nonlinear STAR Error Correction Models," Working Papers 497, Queen Mary University of London, School of Economics and Finance.
    12. Chen, Shyh-Wei, 2011. "Are current account deficits really sustainable in the G-7 countries?," Japan and the World Economy, Elsevier, vol. 23(3), pages 190-201.
    13. Kim, Hyeongwoo & Moh, Young-Kyu, 2009. "A Century of Purchasing Power Parity Confirmed: The Role of Nonlinearity," MPRA Paper 17488, University Library of Munich, Germany.
    14. Dilem Yildirim & Ralf Becker & Denise R Osborn, 2009. "Bootstrap Unit Root Tests for Nonlinear Threshold Models," Economics Discussion Paper Series 0915, Economics, The University of Manchester.
    15. Andrew Phiri & Peter Lusanga, 2011. "Can asymmetries account for the empirical failure of the Fisher effect in South Africa?," Economics Bulletin, AccessEcon, vol. 31(3), pages 1968-1979.
    16. Niels Haldrup & Robinson Kruse & Timo Teräsvirta & Rasmus T. Varneskov, 2013. "Unit roots, non-linearities and structural breaks," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 4, pages 61-94, Edward Elgar Publishing.
    17. Phiri, Andrew, 2014. "Purchasing power parity (PPP) between South Africa and her main currency exchange partners: Evidence from asymmetric unit root tests and threshold co-integration analysis," MPRA Paper 53659, University Library of Munich, Germany.
    18. Zisimos Koustas & Jean-Francois Lamarche, 2006. "Policy-Induced Mean Reversion in the Real Interest Rate?," Working Papers 0601, Brock University, Department of Economics.
    19. Kim, Bong-Han & Min, Hong-Ghi & Moh, Young-Kyu, 2010. "Nonlinear dynamics in exchange rate deviations from the monetary fundamentals: An empirical study," Economic Modelling, Elsevier, vol. 27(5), pages 1167-1177, September.
    20. Wolff, Christian & van Tol, Michel R, 2005. "Forecasting the Spot Exchange Rate with the Term Structure of Forward Premia: Multivariate Threshold Cointegration," CEPR Discussion Papers 4958, C.E.P.R. Discussion Papers.
    21. Chen, Shyh-Wei, 2011. "Current account deficits and sustainability: Evidence from the OECD countries," Economic Modelling, Elsevier, vol. 28(4), pages 1455-1464, July.
    22. Muhammad Farid Ahmed & Stephen Satchell, 2019. "Some Dynamic and Steady-State Properties of Threshold Auto-Regressions with Applications to Stationarity and Local Explosivity," JRFM, MDPI, vol. 12(3), pages 1-18, July.
    23. Andrew Phiri, 2017. "Nonlinear adjustment effects in the purchasing power parity," EERI Research Paper Series EERI RP 2017/08, Economics and Econometrics Research Institute (EERI), Brussels.
    24. Mario Cerrato & Hyunsok Kim & Ronald MacDonald, 2008. "3-Regime symmetric STAR modeling and exchange rate reversion," Working Papers 2009_05, Business School - Economics, University of Glasgow, revised Feb 2009.
    25. Nilgun Yavuz & Veli Yilanci, 2013. "Convergence in Per Capita Carbon Dioxide Emissions Among G7 Countries: A TAR Panel Unit Root Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 54(2), pages 283-291, February.
    26. BESSEC Marie, 2010. "The Asymmetric Exchange Rate Dynamics in the EMS: a Time-Varying Threshold Test," EcoMod2003 330700015, EcoMod.
    27. Magdalena Osińska & Tadeusz Kufel & Marcin Błażejowski & Paweł Kufel, 2020. "Modeling mechanism of economic growth using threshold autoregression models," Empirical Economics, Springer, vol. 58(3), pages 1381-1430, March.
    28. Bec, Frederique & Guay, Alain & Guerre, Emmanuel, 2008. "Adaptive consistent unit-root tests based on autoregressive threshold model," Journal of Econometrics, Elsevier, vol. 142(1), pages 94-133, January.
    29. Sandberg, Rickard, 2016. "Trends, unit roots, structural changes, and time-varying asymmetries in U.S. macroeconomic data: the Stock and Watson data re-examined," Economic Modelling, Elsevier, vol. 52(PB), pages 699-713.
    30. Andrew Phiri, 2012. "Threshold effects and inflation persistence in South Africa," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 4(3), pages 247-269, July.
    31. International Monetary Fund, 2007. "Mexico: Selected Issues," IMF Staff Country Reports 2007/378, International Monetary Fund.
    32. Georgios Chortareas & George Kapetanios & Merih Uctum, 2008. "Nonlinear Alternatives to Unit Root Tests and Public Finances Sustainability: Some Evidence from Latin American and Caribbean Countries," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(5), pages 645-663, October.
    33. Marcella Niglio & Cosimo Damiano Vitale, 2012. "Local Unit Roots and Global Stationarity of TARMA Models," Methodology and Computing in Applied Probability, Springer, vol. 14(1), pages 17-34, March.
    34. Chen, Shyh-Wei, 2014. "Smooth transition, non-linearity and current account sustainability: Evidence from the European countries," Economic Modelling, Elsevier, vol. 38(C), pages 541-554.
    35. Frédéric BEC & Alain GUAY, 2020. "A simple unit root test consistent against any stationary alternative," Working Papers 2020-28, Center for Research in Economics and Statistics.
    36. Kung-Sik Chan & Simone Giannerini & Greta Goracci & Howell Tong, 2020. "Testing for threshold regulation in presence of measurement error with an application to the PPP hypothesis," Papers 2002.09968, arXiv.org, revised Nov 2021.
    37. Santeramo, Fabio Gaetano & Cioffi, Antonio & Vitale, Cosimo Damiano, 2014. "A Threshold-Var Approach To Assess The Efficacy Of The Eu Import Regime," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 2(1), pages 1-12, January.
    38. Georgios Chortareas & George Kapetanios & Merih Uctum, 2003. "An Investigation of Current Account Solvency in Latin America Using Non Linear Stationarity Tests," Working Papers 485, Queen Mary University of London, School of Economics and Finance.
    39. Gawon Yoon, 2010. "Nonlinear mean reversion in real exchange rates: threshold autoregressive models and stochastic unit root processes," Applied Economics Letters, Taylor & Francis Journals, vol. 17(8), pages 797-804.
    40. Antonio Cioffi & Fabio Gaetano Santeramo & Cosimo Damiano Vitale, 2011. "The price stabilization effects of the EU entry price scheme for fruit and vegetables," Agricultural Economics, International Association of Agricultural Economists, vol. 42(3), pages 405-418, May.
    41. Chin-Ping King, 2012. "Half Life of the Real Exchange Rate: Evidence from the Nonlinear Approach in Emerging Economies," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 8(1), pages 1-23, January.
    42. Alebachew Abebe & Aboma Temesgen & Belete Kebede, 2023. "Modeling inflation rate factors on present consumption price index in Ethiopia: threshold autoregressive models approach," Future Business Journal, Springer, vol. 9(1), pages 1-12, December.
    43. Zisimos Koustas & Jean-Francois Lamarche, 2010. "Evidence of nonlinear mean reversion in the real interest rate," Applied Economics, Taylor & Francis Journals, vol. 42(2), pages 237-248.
    44. Jaya Krishnakumar & David Neto, 2012. "Testing Uncovered Interest Rate Parity and Term Structure Using a Three‐regime Threshold Unit Root VECM: An Application to the Swiss ‘Isle’ of Interest Rates," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 180-202, April.
    45. Maki Daiki, 2010. "Detection of Stationarity in Nonlinear Processes: A Comparison between Structural Breaks and Three-Regime TAR Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-43, September.
    46. Aaron D. Smallwood, 2016. "A Monte Carlo Investigation of Unit Root Tests and Long Memory in Detecting Mean Reversion in I(0) Regime Switching, Structural Break, and Nonlinear Data," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 986-1012, June.
    47. Vladimir Andric & Dusko Bodroza & Mihajlo Djukic, 2024. "A Commentary on US Sovereign Debt Persistence and Nonlinear Fiscal Adjustment," Mathematics, MDPI, vol. 12(20), pages 1-33, October.
    48. Dobronravova, Elizaveta & Perevyshin, Yury & Skrobotov, Anton & Shemyakina, Kira, 2019. "Limits of regional food price differences and invisible hand," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 53, pages 30-54.
    49. Sami Saafi & Meriem Bel Haj Mohamed & Abdeljelil Farhat, 2017. "Untangling the causal relationship between tax burden distribution and economic growth in 23 OECD countries: Fresh evidence from linear and non-linear Granger causality," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 14(2), pages 265-301, December.
    50. Daiki Maki & Shin-ichi Kitasaka, 2015. "Residual-based tests for cointegration with three-regime TAR adjustment," Empirical Economics, Springer, vol. 48(3), pages 1013-1054, May.
    51. Mr. Gene L. Leon & Serineh Najarian, 2003. "Asymmetric Adjustment and Nonlinear Dynamics in Real Exchange Rates," IMF Working Papers 2003/159, International Monetary Fund.
    52. Hyginus Leon & Serineh Najarian, 2005. "Asymmetric adjustment and nonlinear dynamics in real exchange rates," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 10(1), pages 15-39.
    53. Frédérique Bec & Alain Guay & Heino Bohn Nielsen & Sarra Saïdi, 2022. "Power of unit root tests against nonlinear and noncausal alternatives," THEMA Working Papers 2022-14, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    54. Abm Nasir & Abdullah M. Noman, 2012. "Sustainability of external debt: further evidence from non-linear framework," International Review of Applied Economics, Taylor & Francis Journals, vol. 26(5), pages 673-685, December.
    55. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
    56. Yoon, Gawon, 2010. "Do real exchange rates really follow threshold autoregressive or exponential smooth transition autoregressive models?," Economic Modelling, Elsevier, vol. 27(2), pages 605-612, March.
    57. Luciana Juvenal & Mr. Rodolphe Blavy, 2008. "Mexico’s Integration into NAFTA Markets: A View from Sectoral Real Exchange Rates and Transaction Costs," IMF Working Papers 2008/123, International Monetary Fund.
    58. Joon Y. Park & Mototsugu Shintani, 2006. "Testing for a Unit Root against Transitional Autoregressive Models," Levine's Bibliography 321307000000000316, UCLA Department of Economics.
    59. Cathy Chen & Shu-Yu Chen & Sangyeol Lee, 2013. "Bayesian Unit Root Test in Double Threshold Heteroskedastic Models," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 471-490, December.
    60. Shahbaz, Muhammad & Khraief, Naceur & Hammoudeh, Shawkat, 2019. "How Do Carbon Emissions Respond to Economic Shocks? Evidence from Low-, Middle- and High-Income Countries," MPRA Paper 93976, University Library of Munich, Germany, revised 15 May 2019.
    61. Sergio H. Lence & GianCarlo Moschini & Fabio Gaetano Santeramo, 2018. "Threshold cointegration and spatial price transmission when expectations matter," Agricultural Economics, International Association of Agricultural Economists, vol. 49(1), pages 25-39, January.
    62. Pelin ÖGE GÜNEY, 2013. "The Term Structure of Interest Rates: A Cointegration Analysis in the Non-Linear STAR Framework," Journal of Economics and Behavioral Studies, AMH International, vol. 5(12), pages 851-860.
    63. Frederique Bec & Alain Guay, 2020. "A Simple Unit Root Test Consistent Against Any Stationary Alternative," Working Papers 20-20, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    64. Ahmad Jameel Khadaroo, 2016. "Current Account Deficit in Mauritius: Risks and Prospects," South African Journal of Economics, Economic Society of South Africa, vol. 84(1), pages 109-128, March.
    65. Bothwell Nyoni & Andrew Phiri, 2018. "The Electricity-growth Nexus in South Africa: Evidence from Asymmetric Cointegration and Co-feature Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 8(6), pages 80-88.
    66. Cioffi, Antonio & Santeramo, Fabio Gaetano & Vitale, Cosimo, 2009. "The Price Stabilisation Effects of the EU import regime of fruit and vegetables: the case of tomatoes," MPRA Paper 25718, University Library of Munich, Germany.
    67. Daiki Maki, 2008. "The Performance of Variance Ratio Unit Root Tests Under Nonlinear Stationary TAR and STAR Processes: Evidence from Monte Carlo Simulations and Applications," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 77-94, February.
    68. Noriega Antonio E. & Rodríguez-Pérez Cid Alonso, 2011. "Stationarity, structural breaks, and economic growth in Mexico: 1895-2008," Working Papers 2011-11, Banco de México.
    69. Mr. Gene L. Leon & Serineh Najarian, 2003. "Time-Varying Thresholds: An Application to Purchasing Power Parity," IMF Working Papers 2003/181, International Monetary Fund.
    70. Burak Güris & Burcu Kiran, 2011. "Foreign Trade Deficit Sustainability of Turkey," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 167-174.

  115. George Kapetanios, 2003. "A Note on Joint Estimation of Common Cycles and Common Trends in Nonstationary Multivariate Systems," Working Papers 483, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Jane Haltmaier, 2011. "Empirical estimation of trend and cyclical export elasticities," International Finance Discussion Papers 1030, Board of Governors of the Federal Reserve System (U.S.).
    2. Chukiat Chaiboonsri & Prasert Chaitip & N. Rangaswamy, 2009. "Modelling International Tourism Demand in Thailand," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 9(3), pages 125-146.

  116. George Kapetanios, 2003. "A New Nonparametric Test of Cointegration Rank," Working Papers 482, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Georgios Chortareas & George Kapetanios, 2004. "How Puzzling is the PPP Puzzle? An Alternative Half-Life Measure of Convergence to PPP," Working Papers 522, Queen Mary University of London, School of Economics and Finance.

  117. George Kapetanios & Massimiliano Marcellino, 2003. "A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions," Working Papers 489, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Nektarios Aslanidis & Andrea Cipollini, 2007. "Leading indicator properties of the US corporate spreads," Money Macro and Finance (MMF) Research Group Conference 2006 115, Money Macro and Finance Research Group.
    2. Farmer, Roger & Henry, Jerome & Marcellino, Massimiliano & Beyer, Andreas, 2005. "Factor Analysis in a New-Keynesian Model," CEPR Discussion Papers 5266, C.E.P.R. Discussion Papers.
    3. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
    4. Moneta, Fabio & Rüffer, Rasmus, 2006. "Business cycle synchronisation in East Asia," Working Paper Series 671, European Central Bank.
    5. Andrea Cipollini & George Kapetanios, 2004. "A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data," Working Papers 506, Queen Mary University of London, School of Economics and Finance.
    6. Andrea Cipollini & George Kapetanios, 2008. "Forecasting Financial Crises and Contagion in Asia using Dynamic Factor Analysis," Center for Economic Research (RECent) 014, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    7. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    8. Todd E. Clark, 2003. "Disaggregate evidence on the persistence of consumer price inflation," Research Working Paper RWP 03-11, Federal Reserve Bank of Kansas City.
    9. Eickmeier, Sandra, 2007. "Business cycle transmission from the US to Germany--A structural factor approach," European Economic Review, Elsevier, vol. 51(3), pages 521-551, April.
    10. Aslanidis, Nektarios & Cipollini, Andrea, 2010. "Leading indicator properties of US high-yield credit spreads," Journal of Macroeconomics, Elsevier, vol. 32(1), pages 145-156, March.
    11. Darracq Pariès, Matthieu & Maurin, Laurent, 2008. "The role of country-specific trade and survey data in forecasting euro area manufacturing production: perspective from large panel factor models," Working Paper Series 894, European Central Bank.
    12. Eickmeier, Sandra & Breitung, Jörg, 2005. "How synchronized are central and east European economies with the euro area? Evidence from a structural factor model," Discussion Paper Series 1: Economic Studies 2005,20, Deutsche Bundesbank.
    13. George Kapetanios, 2004. "Dynamic Factor Extraction of Cross-Sectional Dependence in Panel Unit Root Tests," Working Papers 509, Queen Mary University of London, School of Economics and Finance.
    14. Andrea Cipollini & George Kapetanios, 2003. "A Dynamic Factor Analysis of Financial Contagion in Asia," Working Papers 498, Queen Mary University of London, School of Economics and Finance.
    15. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    16. Matthias Burgert & Stephane Dees, 2009. "Forecasting World Trade: Direct Versus “Bottom-Up” Approaches," Open Economies Review, Springer, vol. 20(3), pages 385-402, July.
    17. Eickmeier, Sandra, 2005. "Common stationary and non-stationary factors in the euro area analyzed in a large-scale factor model," Discussion Paper Series 1: Economic Studies 2005,02, Deutsche Bundesbank.
    18. Eickmeier, Sandra & Moll, Katharina, 2008. "The global dimension of inflation: evidence from factor-augmented Phillips curves," Discussion Paper Series 1: Economic Studies 2008,16, Deutsche Bundesbank.
    19. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "A Dynamic Factor Analysis of Business Cycle on Firm-Level Data," LEM Papers Series 2006/27, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

  118. George Kapetanios, 2003. "Using Extraneous Information and GMM to Estimate Threshold Parameters in TAR Models," Working Papers 494, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Araujo-Enciso, Sergio Rene, 2011. "The Takayama and Judge Price and Allocation Model and its Application in Non-linear Techniques for Spatial Market Integration," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114225, European Association of Agricultural Economists.

  119. Kapetanios, G. & Weeks, M., 2003. "Non-nested Models and the likelihood Ratio Statistic: A Comparison of Simulation and Bootstrap-based Tests," Cambridge Working Papers in Economics 0308, Faculty of Economics, University of Cambridge.

    Cited by:

    1. George Kapetanios & Yongcheol Shin, 2011. "Testing the Null Hypothesis of Nonstationary Long Memory Against the Alternative Hypothesis of a Nonlinear Ergodic Model," Econometric Reviews, Taylor & Francis Journals, vol. 30(6), pages 620-645.
    2. Park, Seong Cheol & Brorsen, B. Wade & Stoecker, Arthur L. & Hattey, Jeffory A., 2012. "Forage Response to Swine Effluent: A Cox Nonnested Test of Alternative Functional Forms Using a Fast Double Bootstrap," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 44(4), pages 1-14, November.

  120. George Kapetanios, 2002. "Testing for Structural Breaks in Nonlinear Dynamic Models Using Artificial Neural Network Approximations," Working Papers 470, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. J. Hoyo & G. Llorente & C. Rivero, 2019. "Testing for Constant Parameters in Nonlinear Models: A Quick Procedure with an Empirical Illustration," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 113-137, June.
    2. Onyango, Christopher H., 2010. "Liberalization of Services and its Implications on Cross-Border Agricultural Trade in Eastern Africa," Conference papers 332028, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    3. Kefei You & Nicholas Sarantis, 2013. "Structural breaks, rural transformation and total factor productivity growth in China," Journal of Productivity Analysis, Springer, vol. 39(3), pages 231-242, June.

  121. George Kapetanios, 2002. "Unit Root Testing against the Alternative Hypothesis of up to m Structural Breaks," Working Papers 469, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Sefa Işik & Fatih Cemil Özbugday, 2021. "The impact of agricultural input costs on food prices in Turkey: A case study," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(3), pages 101-110.
    2. D., Ivan, 2017. "Stability of the labour shares: evidence from OECD economies," MPRA Paper 79822, University Library of Munich, Germany.
    3. Corbet, Shaen & Goodell, John W. & Günay, Samet, 2020. "Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19," Energy Economics, Elsevier, vol. 92(C).
    4. Hlongwane, Nyiko Worship & Daw, Olebogeng David, 2022. "Determinants of public debt in South Africa: A Regime-Switching Approach," MPRA Paper 113203, University Library of Munich, Germany.
    5. Chi‐Wei Su & Hsu‐Ling Chang & Yan Liu, 2013. "Real Interest Rate Parity and Two Structural Breaks: African Countries Evidence," African Development Review, African Development Bank, vol. 25(4), pages 478-484, December.
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  122. George Kapetanios & Yongcheol Shin, 2002. "GLS Detrending for Nonlinear Unit Root Tests," Working Papers 472, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Haluk Erlat, 2004. "Unit roots or nonlinear stationarity in Turkish real exchange rates," Applied Economics Letters, Taylor & Francis Journals, vol. 11(10), pages 645-650.

  123. George Kapetanios, 2002. "A Note on an Iterative Least Squares Estimation Method for ARMA and VARMA Models," Working Papers 467, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Athanasopouolos, George & Poskitt, Don & Vahid, Farshid & Yao, Wenying, 2014. "Forecasting with EC-VARMA models," Working Papers 2014-07, University of Tasmania, Tasmanian School of Business and Economics, revised 22 Feb 2014.
    2. Jean-Marie Dufour & Tarek Jouini, 2011. "Asymptotic Distributions for Some Quasi-Efficient Estimators in Echelon VARMA Models," CIRANO Working Papers 2011s-25, CIRANO.
    3. Christian Kascha & Karel Mertens, 2006. "Business Cycle Analysis and VARMA models," Economics Working Papers ECO2006/37, European University Institute.
    4. Gustavo Fruet Dias & George Kapetanios, 2014. "Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets," CREATES Research Papers 2014-37, Department of Economics and Business Economics, Aarhus University.
    5. George Athanasopoulos & Donald S. Poskitt & Farshid Vahid & Wenying Yao, 2016. "Determination of Long‐run and Short‐run Dynamics in EC‐VARMA Models via Canonical Correlations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1100-1119, September.
    6. Christian Kascha, 2012. "A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 297-324.
    7. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
    8. Poloni, Federico & Sbrana, Giacomo, 2019. "Closed-form results for vector moving average models with a univariate estimation approach," Econometrics and Statistics, Elsevier, vol. 10(C), pages 27-52.

  124. George Kapetanios, 2002. "Factor Analysis Using Subspace Factor Models: Some Theoretical Results and an Application to UK Inflation Forecasting," Working Papers 466, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas, 2008. "Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 399-413.
    2. George Kapetanios, 2002. "Modelling Core Inflation for the UK Using a New Dynamic Factor Estimation Method and a Large Disaggregated Price Index Dataset," Working Papers 471, Queen Mary University of London, School of Economics and Finance.
    3. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2006. "Forecasting Inflation and GDP growth: Comparison of Automatic Leading Indicator (ALI) Method with Macro Econometric Structural Models (MESMs)," Working Papers 554, Queen Mary University of London, School of Economics and Finance.
    4. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2007. "Automatic Leading Indicators (ALIs) versus Macro Econometric Structural Models (MESMs): Comparison of Inflation and GDP growth Forecasting," EcoMod2007 23900072, EcoMod.

  125. George Kapetanios, 2002. "Modelling Core Inflation for the UK Using a New Dynamic Factor Estimation Method and a Large Disaggregated Price Index Dataset," Working Papers 471, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Fan Ding & Alexander L. Wolman, 2005. "Inflation and changing expenditure shares," Economic Quarterly, Federal Reserve Bank of Richmond, vol. 91(Win), pages 1-20.
    2. Mikael Khan & Louis Morel & Patrick Sabourin, 2013. "The Common Component of CPI: An Alternative Measure of Underlying Inflation for Canada," Staff Working Papers 13-35, Bank of Canada.
    3. Camba-Méndez, Gonzalo & Kapetanios, George, 2004. "Forecasting euro area inflation using dynamic factor measures of underlying inflation," Working Paper Series 402, European Central Bank.
    4. Bjarni G. Einarsson, 2014. "A Dynamic Factor Model for Icelandic Core Inflation," Economics wp67, Department of Economics, Central bank of Iceland.

  126. George Kapetanios, 2002. "Testing for Neglected Nonlinearity in Long Memory Models," Working Papers 474, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Smallwood Aaron D, 2005. "Joint Tests for Non-linearity and Long Memory: The Case of Purchasing Power Parity," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-30, June.
    2. Aaron Smallwood, 2004. "Joint Tests for Long Memory and Non-linearity: The Case of Purchasing Power Parity," Computing in Economics and Finance 2004 23, Society for Computational Economics.

  127. Camba-Méndez, Gonzalo & Kapetanios, George, 2001. "Spectral based methods to identify common trends and common cycles," Working Paper Series 62, European Central Bank.

    Cited by:

    1. Patrick M. Crowley, 2005. "An intuitive guide to wavelets for economists," GE, Growth, Math methods 0508009, University Library of Munich, Germany.
    2. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, April.
    3. Crowley, Patrick M., 2010. "Long cycles in growth: explorations using new frequency domain techniques with US data," Bank of Finland Research Discussion Papers 6/2010, Bank of Finland.
    4. Crowley, Patrick M. & Maraun, Douglas & Mayes, David, 2006. "How hard is the euro area core? An evaluation of growth cycles using wavelet analysis," Bank of Finland Research Discussion Papers 18/2006, Bank of Finland.
    5. Pami Dua & Vineeta Sharma, 2013. "Measurement And Patterns Of International Synchronization-- A Spectral Approach," Working papers 224, Centre for Development Economics, Delhi School of Economics.

  128. Camba-Méndez, Gonzalo & Kapetanios, George, 2001. "Testing the rank of the Hankel matrix: a statistical approach," Working Paper Series 45, European Central Bank.

    Cited by:

    1. Bauer, Dietmar, 2009. "Estimating ARMAX systems for multivariate time series using the state approach to subspace algorithms," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 397-421, March.
    2. Zaka Ratsimalahelo, 2003. "Rank Test Based On Matrix Perturbation Theory," Econometrics 0306008, University Library of Munich, Germany.
    3. Chortareas, Georgios & Kapetanios, George, 2009. "Getting PPP right: Identifying mean-reverting real exchange rates in panels," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 390-404, February.
    4. d’Artis Kancs & Julda Kielyte, 2002. "Migration in the Enlarged European Union: Empirical Evidence for Labour Mobility in the Baltic States," EERI Research Paper Series EERI_RP_2002_04, Economics and Econometrics Research Institute (EERI), Brussels.
    5. Stephen G. Donald & Natércia Fortuna & Vladas Pipiras, 2005. "On rank estimation in symmetric matrices: the case of indefinite matrix estimators," FEP Working Papers 167, Universidade do Porto, Faculdade de Economia do Porto.
    6. George Kapetanios, 2003. "Determining the Poolability of Individual Series in Panel Datasets," Working Papers 499, Queen Mary University of London, School of Economics and Finance.
    7. George Kapetanios, 2003. "Determining the Stationarity Properties of Individual Series in Panel Datasets," Working Papers 495, Queen Mary University of London, School of Economics and Finance.
    8. Dietmar Bauer, 2004. "Using Subspace Methods for Estimating ARMA Models for Multivariate Time Series with Conditionally Heteroskedastic Innovations," Cowles Foundation Discussion Papers 1452, Cowles Foundation for Research in Economics, Yale University.
    9. Düker, Marie-Christine & Pipiras, Vladas & Sundararajan, Raanju, 2022. "Cotrending: Testing for common deterministic trends in varying means model," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    10. George Kapetanios, 2003. "A New Nonparametric Test of Cointegration Rank," Working Papers 482, Queen Mary University of London, School of Economics and Finance.
    11. Gonzalo Camba-Mendez & George Kapetanios, 2005. "Statistical Tests of the Rank of a Matrix and Their Applications in Econometric Modelling," Working Papers 541, Queen Mary University of London, School of Economics and Finance.
    12. Gonzalo Camba-Mendez & George Kapetanios, 2002. "Bootstrap Statistical Tests of Rank Determination for System Identification," Working Papers 468, Queen Mary University of London, School of Economics and Finance.

  129. George Kapetanios, 2000. "Information Criteria, Model Selection Uncertainty and the Determination of Cointegration Rank," National Institute of Economic and Social Research (NIESR) Discussion Papers 166, National Institute of Economic and Social Research.

    Cited by:

    1. Medhioub, Imed, 2007. "Asymétrie des cycles économiques et changement de régimes : cas de la Tunisie," L'Actualité Economique, Société Canadienne de Science Economique, vol. 83(4), pages 529-553, décembre.

  130. George Kapetanios, 2000. "Incorporating lag order selection uncertainty in parameter inference for AR models," National Institute of Economic and Social Research (NIESR) Discussion Papers 167, National Institute of Economic and Social Research.

    Cited by:

    1. John H. Huston & Roger W. Spencer, 2016. "The Wealth Effects of Quantitative Easing," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 44(4), pages 471-486, December.
    2. Hannes Leeb & Benedikt M. Potscher, 2003. "Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators?," Cowles Foundation Discussion Papers 1444, Cowles Foundation for Research in Economics, Yale University.
    3. Apostolos Thomadakis, 2012. "Contagion or Flight-to-Quality Phenomena in Stock and Bond Returns," School of Economics Discussion Papers 0612, School of Economics, University of Surrey.
    4. Rashmi Banga, 2007. "Liberalisation and Wage Inequality In India," Working Papers id:805, eSocialSciences.
    5. Rashmi Banga, 2007. "Impact of Liberalisation on Wages and Employment in Indian Manufacturing Industries," Working Papers id:989, eSocialSciences.
    6. Matija Rojec & Mark Knell, 2018. "Why Is There A Lack Of Evidence On Knowledge Spillovers From Foreign Direct Investment?," Journal of Economic Surveys, Wiley Blackwell, vol. 32(3), pages 579-612, July.

  131. George Kapetanios, 2000. "Model Selection Uncertainty and Dynamic Models," National Institute of Economic and Social Research (NIESR) Discussion Papers 165, National Institute of Economic and Social Research.

    Cited by:

    1. Debby Lanser & Henk Kranendonk, 2008. "Investigating uncertainty in macroeconomic forecasts by stochastic simulation," CPB Discussion Paper 112, CPB Netherlands Bureau for Economic Policy Analysis.

  132. George Kapetanios & Yongcheol Shin & Andy Snell, 2000. "Testing for a Unit Root against Nonlinear STAR Models," Edinburgh School of Economics Discussion Paper Series 69, Edinburgh School of Economics, University of Edinburgh.

    Cited by:

    1. JamesR. Lothian & MarkP. Taylor, 2008. "Real Exchange Rates Over the Past Two Centuries: How Important is the Harrod-Balassa-Samuelson Effect?," Economic Journal, Royal Economic Society, vol. 118(532), pages 1742-1763, October.
    2. Mario Cerrato & Christian de Peretti & Rolf Larsson & Nicholas Sarantis, 2011. "A nonlinear panel unit root test under cross section dependence," Working Papers 2011_08, Business School - Economics, University of Glasgow.
    3. Zisimos Koustas & Jean-Francois Lamarche & Apostolos Serletis, 2006. "Threshold Random Walks in the U.S. Stock Market," Working Papers 0602, Brock University, Department of Economics, revised May 2006.
    4. Sophocles N. Brissimis & Petros M. Migiakis, 2016. "Inflation persistence, learning dynamics and the rationality of inflation expectations," Empirical Economics, Springer, vol. 51(3), pages 963-979, November.
    5. Venus Khim-Sen Liew, 2003. "The Validity of PPP Revisited: An Application of Non-linear Unit Root Test," International Finance 0308001, University Library of Munich, Germany.
    6. Ivan Paya & David Peel, 2005. "The process followed by PPP data. On the properties of linearity tests," Applied Economics, Taylor & Francis Journals, vol. 37(21), pages 2515-2522.
    7. Kuswanto, Heri & Sibbertsen, Philipp, 2009. "Testing for Long Memory Against ESTAR Nonlinearities," Hannover Economic Papers (HEP) dp-427, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    8. Kurmas Akdogan & Yunus Aksoy, 2007. "Exchange Rates and Fundamentals : Is there a Role for Nonlinearities in Real Time?," Working Papers 0703, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    9. Venus Khim-Sen Liew & Kian-Ping Lim & Evan Lau & Chee-Keong Choong, 2003. "Exchange Rate – Relative Price Relationship: Nonlinear Evidence from Malaysia," International Finance 0311014, University Library of Munich, Germany.
    10. He, Changli & Sandberg, Rickard, 2005. "Dickey-Fuller Type of Tests against Nonlinear Dynamic Models," SSE/EFI Working Paper Series in Economics and Finance 580, Stockholm School of Economics.
    11. Li, Yushu & Shukur, Ghazi, 2009. "Testing for Unit Root against LSTAR model – wavelet improvements under GARCH distortion," Working Paper Series in Economics and Institutions of Innovation 184, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    12. Frédérique Bec & Songlin Zeng, 2013. "Are Southeast Asian Real Exchange Rates Mean Reverting?," Post-Print hal-02979368, HAL.
    13. Juan Carlos Cuestas, 2007. "Purchasing Power Parity In Central And Eastern European Countries: An Analysis Of Unit Roots And Nonlinearities," Working Papers. Serie AD 2007-22, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    14. David G. McMillan & Mark E. Wohar, 2010. "Stock return predictability and dividend-price ratio: a nonlinear approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 351-365.
    15. Gilles Dufrénot & Valérie Mignon & Théo Naccache, 2009. "The slow convergence of per capita income between the developing countries: “growth resistance” and sometimes “growth tragedy”," Discussion Papers 09/03, University of Nottingham, CREDIT.
    16. Frédérique Bec & Mélika Ben Salem & Marine Carrasco, 2010. "Detecting Mean Reversion in Real Exchange Rates from a Multiple Regime STAR Model," Post-Print hal-00685810, HAL.
    17. Dimitris Christopoulos, 2004. "Does Stationarity Characterize Real GDP Movements? Results from Non-Linear Unit Root Tests," Macroeconomics 0406002, University Library of Munich, Germany.
    18. Francesco Battaglia & Mattheos K. Protopapas, 2011. "Time‐varying multi‐regime models fitting by genetic algorithms," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 237-252, May.
    19. Mario Cerrato & Hyunsok Kim & Ronald Macdonald, 2010. "Three-Regime Asymmetric STAR Modeling and Exchange Rate Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(7), pages 1447-1467, October.
    20. Kim, Hyeongwoo & Moh, Young-Kyu, 2009. "A Century of Purchasing Power Parity Confirmed: The Role of Nonlinearity," MPRA Paper 17488, University Library of Munich, Germany.
    21. Matteo Lanzafame, 2006. "The Nature of Regional Unemployment in Italy," ERSA conference papers ersa06p155, European Regional Science Association.
    22. Million, N., 2008. "Test simultané de la non-stationnarité et de la non-linéarité : une application au taux d.intérêt réel américain," Working papers 201, Banque de France.
    23. Jiti Gao & Maxwell King, 2011. "A New Test in Parametric Linear Models against Nonparametric Autoregressive Errors," Monash Econometrics and Business Statistics Working Papers 20/11, Monash University, Department of Econometrics and Business Statistics.
    24. Habibullah, M.S. & Dayang-Afizzah, A.M. & Liew, Venus Khim-Sen & Lim, Kian-Ping, 2008. "Testing nonlinear convergence in Malaysia,1965-2003," MPRA Paper 12110, University Library of Munich, Germany.
    25. Venus Khim-Sen Liew & Ahmad Zubaidi Baharumshah & Kian-Ping Lim, 2003. "On Singaporean Dollar-U.S. Dollar and Purchasing Power Parity," International Finance 0309001, University Library of Munich, Germany, revised 01 Nov 2004.
    26. Dilem Yildirim & Ralf Becker & Denise R Osborn, 2009. "Bootstrap Unit Root Tests for Nonlinear Threshold Models," Economics Discussion Paper Series 0915, Economics, The University of Manchester.
    27. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2011. "Unit-root and stationarity testing with empirical application on industrial production of CEE-4 countries," MPRA Paper 29648, University Library of Munich, Germany.
    28. Vasudeva N. R. Murthy & Emmanuel Anoruo, 2009. "Are Per Capita Real GDP Series in African Countries Non-stationary or Non-linear? What does Empirical Evidence Reveal?," Economics Bulletin, AccessEcon, vol. 29(4), pages 2492-2504.
    29. Noriega Antonio E. & Ventosa-Santaulària Daniel, 2010. "Spurious Long-Horizon Regression in Econometrics," Working Papers 2010-06, Banco de México.
    30. Venus Khim-sen Liew & Ahmad Zubaidi Baharumshah & Terence Tai-leung Chong, 2003. "Are Asian Real Exchange Rates Stationary?," International Finance 0307002, University Library of Munich, Germany, revised 01 Nov 2004.
    31. Andros Gregoriou & Alexandros Kontonikas, 2006. "Inflation Targeting And The Stationarity Of Inflation: New Results From An Estar Unit Root Test," Bulletin of Economic Research, Wiley Blackwell, vol. 58(4), pages 309-322, October.
    32. Ozdemir, Zeynel Abidin & Balcilar, Mehmet & Tansel, Aysit, 2011. "International Labour Force Participation Rates by Gender: Unit Root or Structural Breaks?," IZA Discussion Papers 6063, Institute of Labor Economics (IZA).
    33. Georgios Chortareas & George Kapetanios, 2006. "The yen real exchange rate may be stationary after all: evidence from non-linear unit root tests," Bank of England working papers 311, Bank of England.
    34. George Kapetanios & Yongcheol Shin, 2002. "Unit Root Tests in Three-Regime SETAR Models," Working Papers 465, Queen Mary University of London, School of Economics and Finance.
    35. Byeongseon Seo, 2004. "Testing for Nonlinear Adjustment in Smooth Transition Vector Error Correction Models," Econometric Society 2004 Far Eastern Meetings 749, Econometric Society.
    36. Holmes, Mark J. & Otero, Jesús & Panagiotidis, Theodore, 2011. "Real interest parity: A note on Asian countries using panel stationarity tests," Journal of Asian Economics, Elsevier, vol. 22(6), pages 550-557.
    37. Ahmad Zubaidi Baharumshah & Venus Khim‐Sen Liew & Chan Tze Haw, 2009. "The Real Interest Rate Differential: International Evidence Based On Non‐Linear Unit Root Tests," Bulletin of Economic Research, Wiley Blackwell, vol. 61(1), pages 83-94, January.
    38. Shelley, Gary L. & Wallace, Frederick H., 2011. "Further evidence regarding nonlinear trend reversion of real GDP and the CPI," Economics Letters, Elsevier, vol. 112(1), pages 56-59, July.
    39. Tiwari, Aviral & Shahbaz, Muhammad & Shabbir, Muhammad, 2011. "Is per capita GDP non-linear stationary in SAARC countries?," MPRA Paper 29109, University Library of Munich, Germany.
    40. Muhammad, Shahbaz & Tiwari, Aviral Kumar & Khan, Saleheen, 2012. "Is Energy Consumption Per Capita Stationary? Evidence from First and Second Generation Panel Unit Root Tests," MPRA Paper 41607, University Library of Munich, Germany, revised 27 Sep 2012.
    41. Robinson Kruse, 2011. "A new unit root test against ESTAR based on a class of modified statistics," Statistical Papers, Springer, vol. 52(1), pages 71-85, February.
    42. Mario Cerrato & Hyunsok Kim & Ronald MacDonald, 2008. "3-Regime symmetric STAR modeling and exchange rate reversion," Working Papers 2009_05, Business School - Economics, University of Glasgow, revised Feb 2009.
    43. Venus Khim-Sen Liew & Yusuf Ahmad, 2009. "Income convergence: fresh evidence from the Nordic countries," Applied Economics Letters, Taylor & Francis Journals, vol. 16(12), pages 1245-1248.
    44. Harvey David I & Leybourne Stephen J & Xiao Bin, 2008. "A Powerful Test for Linearity When the Order of Integration is Unknown," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-24, September.
    45. Harrison, Barry & Moore, Winston, 2009. "Stock Market Como Vement In The European Union And Transition Countries," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 13(3), pages 124-151.
    46. Tsoulfidis, Lefteris & Tsaliki, Persefoni, 2011. "Classical competition and regulating capital: theory and empirical evidence," MPRA Paper 51334, University Library of Munich, Germany, revised 2013.
    47. Lucchetti, Riccardo & Palomba, Giulio, 2008. "Nonlinear Adjustment in US Bond Yields: an Empirical Analysis with Conditional Heteroskedasticity," MPRA Paper 11571, University Library of Munich, Germany.
    48. Juan Jiménez-Martin & M. Robles-Fernandez, 2010. "PPP: Delusion or Reality? Evidence from a Nonlinear Analysis," Open Economies Review, Springer, vol. 21(5), pages 679-704, November.
    49. Baharom, A.H. & Habibullah, M.S. & Royfaizal, R. C, 2008. "Convergence of violent crime in the United States: Time series test of nonlinear," MPRA Paper 11926, University Library of Munich, Germany.
    50. Peter Martey Addo & Monica Billio & Dominique Guegan, 2011. "A test for a new modelling: The Univariate MT-STAR Model," Documents de travail du Centre d'Economie de la Sorbonne 11083, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    51. Olivier Darné & Amélie Charles, 2009. "Large shocks in U.S. macroeconomic time series: 1860–1988," Working Papers hal-00422502, HAL.
    52. Gustavsson, Magnus & Österholm, Pär, 2006. "Does Unemployment Hysteresis Equal Employment Hysteresis?," Working Paper Series 2006:15, Uppsala University, Department of Economics.
    53. Haluk Erlat, 2004. "Unit roots or nonlinear stationarity in Turkish real exchange rates," Applied Economics Letters, Taylor & Francis Journals, vol. 11(10), pages 645-650.
    54. Andrew P. Blake & George Kapetanios, 2003. "Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 253-267, May.
    55. George Kapetanios, 2000. "Testing for a Unit Root against Nonlinear STAR Models," National Institute of Economic and Social Research (NIESR) Discussion Papers 164, National Institute of Economic and Social Research.
    56. Fuyu Yang, 2007. "Bayesian Analysis of Deterministic Time Trend and Changes in Persistence Using a Generalised Stochastic Unit Root Model," Discussion Papers in Economics 07/11, Division of Economics, School of Business, University of Leicester.
    57. Joanna Tyrowicz & Piotr Wojcik, 2011. "Nonlinear Stochastic Convergence Analysis of Regional Unemployment Rates in Poland," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 3(1), pages 59-79, July.
    58. Hasanov, Mübariz & Araç, Aysen & Telatar, Funda, 2010. "Nonlinearity and structural stability in the Phillips curve: Evidence from Turkey," Economic Modelling, Elsevier, vol. 27(5), pages 1103-1115, September.
    59. David O. Cushman, 2008. "Real exchange rates may have nonlinear trends," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 13(2), pages 158-173.
    60. Juan Carlos Cuestas & Karsten Staehr, 2011. "Fiscal shocks and budget balance persistence in the EU countries from Central and Eastern Europe," Bank of Estonia Working Papers wp2011-08, Bank of Estonia, revised 13 Jul 2011.
    61. Omay, Nazli C. & Karadagli, Ece C., 2010. "Testing Weak Form Market Efficiency for Emerging Economies: A Nonlinear Approach," MPRA Paper 27312, University Library of Munich, Germany.
    62. Shu-Ling Chen & Hyeongwoo Kim, 2011. "Nonlinear Mean Reversion across National Stock Markets: Evidence from Emerging Asian Markets," International Economic Journal, Taylor & Francis Journals, vol. 25(2), pages 239-250.
    63. Landajo, Manuel & Presno, María José, 2010. "Nonparametric pseudo-Lagrange multiplier stationarity testing," MPRA Paper 25659, University Library of Munich, Germany.
    64. María Presno & Manuel Landajo, 2010. "Computation of limiting distributions in stationarity testing with a generic trend," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(2), pages 165-183, March.
    65. Julián Ramajo Hernández(1) & Montserrat Ferré Carracedo(2), "undated". "Testing For Long-Run Purchasing Power Parity In The Post Bretton Woods Era: Evidence From Old And New Tests," Working Papers 24-05 Classification-JEL , Instituto de Estudios Fiscales.
    66. Yunus Aksoy & Miguel A. Leon-Ledesma, 2007. "Non-linearities and Unit Roots in G7 Macroeconomic Variables," Birkbeck Working Papers in Economics and Finance 0710, Birkbeck, Department of Economics, Mathematics & Statistics.
    67. Dimitris, Christopoulos & Miguel, Leon-Ledesma, 2009. "Smooth Breaks and Nonlinear Mean Reversion: Post-Bretton Woods Real Exchange Rates," MPRA Paper 22553, University Library of Munich, Germany.
    68. Antonio Cioffi & Fabio Gaetano Santeramo & Cosimo Damiano Vitale, 2011. "The price stabilization effects of the EU entry price scheme for fruit and vegetables," Agricultural Economics, International Association of Agricultural Economists, vol. 42(3), pages 405-418, May.
    69. Dimitris K. Christopoulos & Miguel León-Ledesma, 2004. "Current Account Sustainability in the US: What Do We Really Know About It?," Studies in Economics 0412, School of Economics, University of Kent.
    70. Duasa, Jarita, 2008. "Income convergence of divergence? Study on selected Muslim countries," MPRA Paper 11563, University Library of Munich, Germany.
    71. Rothe, Christoph & Sibbertsen, Philipp, 2005. "Phillips-Perron-type unit root tests in the nonlinear ESTAR framework," Hannover Economic Papers (HEP) dp-315, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    72. Liew, Venus Khim-Sen & Ahmad, Yusuf, 2006. "Income convergence? Evidence of non-linearity in the East Asian Economies: A comment," MPRA Paper 519, University Library of Munich, Germany.
    73. Daiki Maki, 2006. "Non-linear adjustment in the term structure of interest rates: a cointegration analysis in the non-linear STAR framework," Applied Financial Economics, Taylor & Francis Journals, vol. 16(17), pages 1301-1307.
    74. Shu-Chen Chang, 2008. "Asymmetric cointegration relationship among Asian exchange rates," Economic Change and Restructuring, Springer, vol. 41(2), pages 125-141, June.
    75. Hyginus Leon & Serineh Najarian, 2005. "Asymmetric adjustment and nonlinear dynamics in real exchange rates," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 10(1), pages 15-39.
    76. Aaron Smallwood & Stefan Norrbin, 2004. "Estimating cointegrating vectors using near unit root variables," Applied Economics Letters, Taylor & Francis Journals, vol. 11(12), pages 781-784.
    77. Mubariz Hasanov & Tolga Omay, 2007. "Are the Transition Stock Markets Efficient? Evidence from Non-Linear Unit Root Tests," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 7(2), pages 1-12.
    78. George Kapetanios & Yongcheol Shin, 2003. "GLS Detrending-Based Unit Root Tests in Nonlinear STAR and SETAR Frameworks," Edinburgh School of Economics Discussion Paper Series 108, Edinburgh School of Economics, University of Edinburgh.
    79. Copeland, Laurence & Heravi, Saeed, 2006. "Structural Breaks in the Real Exchange Rate Adjustment Mechanism," Cardiff Economics Working Papers E2006/21, Cardiff University, Cardiff Business School, Economics Section.
    80. Joon Y. Park & Mototsugu Shintani, 2006. "Testing for a Unit Root against Transitional Autoregressive Models," Levine's Bibliography 321307000000000316, UCLA Department of Economics.
    81. Christos Emmanouilides & Panos Fousekis, 2009. "Non-Linear Catching-up and Long-Run Convergence in the Agricultural Productivity of US States," Economics Bulletin, AccessEcon, vol. 29(1), pages 182-189.
    82. Kian-Ping Lim & Venus Khim-Sen Liew, 2003. "Testing for Non-Linearity in ASEAN Financial Markets," Finance 0308002, University Library of Munich, Germany.
    83. Stephen Norman, 2009. "Testing for a unit root against ESTAR nonlinearity with a delay parameter greater than one," Economics Bulletin, AccessEcon, vol. 29(3), pages 2152-2173.
    84. Venus Khim-Sen Liew & Zhuo Qiao & Wing-keung Wong, 2010. "Linearity and stationarity of G7 government bond returns," Economics Bulletin, AccessEcon, vol. 30(4), pages 2642-2655.
    85. Cioffi, Antonio & Santeramo, Fabio Gaetano & Vitale, Cosimo, 2009. "The Price Stabilisation Effects of the EU import regime of fruit and vegetables: the case of tomatoes," MPRA Paper 25718, University Library of Munich, Germany.
    86. Paresh Kumar Narayan, 2006. "Are bilateral real exchange rates stationary? Evidence from Lagrange multiplier unit root tests for India," Applied Economics, Taylor & Francis Journals, vol. 38(1), pages 63-70.
    87. Ozturk, Ilhan & Kalyoncu, Huseyin, 2007. "Is Per Capita Real GDP Stationary in the OECD Countries? Evidence from a Panel Unit Root Test," MPRA Paper 9635, University Library of Munich, Germany.
    88. Kisswani, Khalid/ M. & Nusair, Salah/ A., 2011. "Non-linear convergence in Asian interest rates and inflation rates," MPRA Paper 34179, University Library of Munich, Germany.
    89. Daiki Maki, 2008. "The Performance of Variance Ratio Unit Root Tests Under Nonlinear Stationary TAR and STAR Processes: Evidence from Monte Carlo Simulations and Applications," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 77-94, February.
    90. Donauer, Stefanie & Heinen, Florian & Sibbertsen, Philipp, 2010. "Identification problems in ESTAR models and a new model," Hannover Economic Papers (HEP) dp-444, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    91. Andy Snell & George Kapetanios & Yongcheol Shin, 2004. "Testing for nonlinear cointegration between stock prices and dividends," Money Macro and Finance (MMF) Research Group Conference 2003 90, Money Macro and Finance Research Group.

  133. Nigel Pain, 2000. "Inward investment and technical progress in the United Kingdom manufacturing sector," National Institute of Economic and Social Research (NIESR) Discussion Papers 175, National Institute of Economic and Social Research.

    Cited by:

    1. Petr Kral, 2004. "Identification and Measurement of Relationships Concerning Inflow of FDI: The Case of the Czech Republic," Working Papers 2004/05, Czech National Bank.
    2. Christian Bellak, 2004. "How Domestic and Foreign Firms Differ and Why Does It Matter?," Department of Economics Working Papers wuwp087, Vienna University of Economics and Business, Department of Economics.
    3. Colin Ellis & Simon Price, 2004. "UK Business Investment and the User Cost of Capital," Manchester School, University of Manchester, vol. 72(s1), pages 72-93, September.

  134. George Kapetanios & Yongcheol Shin, 2000. "Testing for a Linear Unit Root against Nonlinear Threshold Stationarity," Edinburgh School of Economics Discussion Paper Series 60, Edinburgh School of Economics, University of Edinburgh.

    Cited by:

    1. Andrew P. Blake & George Kapetanios, 2003. "Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 253-267, May.
    2. Copeland, Laurence & Heravi, Saeed, 2006. "Structural Breaks in the Real Exchange Rate Adjustment Mechanism," Cardiff Economics Working Papers E2006/21, Cardiff University, Cardiff Business School, Economics Section.

  135. Dr Martin Weale & Dr. James Mitchell, 2000. "Cointegrating VAR models with endogenous I(0) variables: theoretical extensions and an application to UK monetary policy," National Institute of Economic and Social Research (NIESR) Discussion Papers 169, National Institute of Economic and Social Research.

    Cited by:

    1. Luis Fernando Escobar Patiño & Pablo Hernán Mendieta Ossio, 2006. "Inflación y depreciación en una economía dolarizada: el caso de Bolivia," Monetaria, CEMLA, vol. 0(1), pages 1-39, enero-mar.
    2. Andrew P. Blake, 2000. "Optimality and Taylor Rules," National Institute Economic Review, National Institute of Economic and Social Research, vol. 174(1), pages 80-91, October.

  136. Dr Martin Weale, 1999. "An Automatic Leading Indicator of Economic Activity: Forecasting GDP Growth for European Countries," National Institute of Economic and Social Research (NIESR) Discussion Papers 149, National Institute of Economic and Social Research.

    Cited by:

    1. Stefan Gerlach & Matthew S. Yiu, 2004. "A Dynamic Factor Model for Current-Quarter Estimates of Economic Activity in Hong Kong," Working Papers 162004, Hong Kong Institute for Monetary Research.
    2. Martin Schneider & Martin Spitzer, 2004. "Forecasting Austrian GDP using the generalized dynamic factor model," Working Papers 89, Oesterreichische Nationalbank (Austrian Central Bank).
    3. Qin, Duo & Tan, Tao, 2009. "How much intraregional exchange rate variability could a currency union remove? The case of ASEAN+3," Journal of Banking & Finance, Elsevier, vol. 33(10), pages 1793-1803, October.
    4. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    5. Duo Qin, 2006. "Uncover Latent PPP by Dynamic Factor Error Correction Model (DF-ECM) Approach: Evidence from Five OECD Countries," Working Papers 575, Queen Mary University of London, School of Economics and Finance.
    6. Hansson, Jesper & Jansson, Per & Löf, Mårten, 2003. "Business Survey Data: Do They Help in Forecasting the Macro Economy?," Working Paper Series 151, Sveriges Riksbank (Central Bank of Sweden).
    7. D R Osborn & M Sensier, 2002. "The Prediction of Business Cycle Phases: Financial Variables and International Linkages," Centre for Growth and Business Cycle Research Discussion Paper Series 15, Economics, The University of Manchester.
    8. Shikha Gupta & Nand Kumar, 2023. "Time varying dynamics of globalization effect in India," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 22(1), pages 81-97, January.
    9. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 25-44, February.
    10. Fabio Canova & Matteo Ciccarelli, 2009. "Estimating Multicountry Var Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 929-959, August.
    11. K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze & G. Rünstler, 2008. "Short-term forecasting of GDP using large monthly datasets – A pseudo real-time forecast evaluation exercise," Working Paper Research 133, National Bank of Belgium.
    12. Burcu Gurcihan Yunculer & Gonul Sengul & Arzu Yavuz, 2014. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(1), pages 23-45.
    13. El-Shagi, Makram, 2011. "Inflation expectations: Does the market beat econometric forecasts?," The North American Journal of Economics and Finance, Elsevier, vol. 22(3), pages 298-319.
    14. Nicholas Apergis & James E. Payne, 2013. "New Evidence on the Information and Predictive Content of the Baltic Dry Index," IJFS, MDPI, vol. 1(3), pages 1-19, July.
    15. Massimiliano Marcellino & George Kapetanios, 2006. "The Role of Search Frictions and Bargaining for Inflation Dynamics," Working Papers 305, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    16. Rudrani Bhattacharya & Parma Chakravartti & Sudipto Mundle, 2019. "Forecasting India’s economic growth: a time-varying parameter regression approach," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 12(3), pages 205-228, September.
    17. Hwee Kwan Chow & Keen Meng Choy, 2004. "Forecasting the Global Electronics Cycle with Leading Indicators: A VAR Approach," Working Papers 16-2004, Singapore Management University, School of Economics.
    18. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas F. Quising, 2006. "Measuring Regional Market Integration by Dynamic Factor Error Correction Model (DF-ECM) Approach - The Case of Developing Asia," Working Papers 565, Queen Mary University of London, School of Economics and Finance.
    19. Silva, Thiago Christiano & Wilhelm, Paulo Victor Berri & Amancio, Diego R., 2024. "Machine learning and economic forecasting: The role of international trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 649(C).
    20. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    21. Inklaar, Robert & Jacobs, Jan & Romp, Ward, 2003. "Business cycle indexes: does a heap of data help?," CCSO Working Papers 200312, University of Groningen, CCSO Centre for Economic Research.
    22. Tatiana Cesaroni, 2011. "The cyclical behavior of the Italian business survey data," Empirical Economics, Springer, vol. 41(3), pages 747-768, December.
    23. Qiao, Zhuo & Chu, Patrick Kuok-Kun, 2014. "Does fine wine price contain useful information to forecast GDP? Evidence from major developed countries," Economic Modelling, Elsevier, vol. 38(C), pages 75-79.
    24. Chris Birchenhall & Denise Osborn & Marianne Sensier, 2001. "Predicting UK Business Cycle Regimes," Scottish Journal of Political Economy, Scottish Economic Society, vol. 48(2), pages 179-195, May.
    25. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas, 2008. "Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 399-413.
    26. Fabio Canova & Matteo Ciccarelli, 2002. "Panel Index Var Models: Specification, Estimation, Testing And Leading Indicators," Working Papers. Serie AD 2002-21, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    27. Lu, Fei & Ma, Feng & Feng, Lin, 2024. "Carbon dioxide emissions and economic growth: New evidence from GDP forecasting," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    28. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    29. Katerina Arnostova & David Havrlant & Luboš Rùžièka & Peter Tóth, 2011. "Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(6), pages 566-583, December.
    30. Daniel Grenouilleau, 2006. "The Stacked Leading Indicators Dynamic Factor Model: A Sensitivity Analysis of Forecast Accuracy using Bootstrapping," European Economy - Economic Papers 2008 - 2015 249, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    31. Curran, Declan & Funke, Michael, 2006. "Taking the temperature: forecasting GDP growth for mainland in China," BOFIT Discussion Papers 6/2006, Bank of Finland Institute for Emerging Economies (BOFIT).
    32. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2006. "Forecasting Inflation and GDP growth: Comparison of Automatic Leading Indicator (ALI) Method with Macro Econometric Structural Models (MESMs)," Working Papers 554, Queen Mary University of London, School of Economics and Finance.
    33. Chow, Hwee Kwan & Choy, Keen Meng, 2006. "Forecasting the global electronics cycle with leading indicators: A Bayesian VAR approach," International Journal of Forecasting, Elsevier, vol. 22(2), pages 301-315.
    34. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    35. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    36. Schumacher Christian & Dreger Christian, 2004. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models? / Die Schätzung von großen Faktormodellen für die deutsche Volkswirtschaft: Übertreffen sie ei," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(6), pages 731-750, December.
    37. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    38. Tak-Kuen Siu & Wai-Ki Ching & Eric Fung & Michael Ng, 2005. "Extracting Information from Spot Interest Rates and Credit Ratings using Double Higher-Order Hidden Markov Models," Computational Economics, Springer;Society for Computational Economics, vol. 26(3), pages 69-102, November.
    39. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
    40. Shikha Gupta & Nand Kumar, 2021. "Dynamics of globalization effect in India," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1394-1406, September.
    41. Oliver Hülsewig & Johannes Mayr & Stéphane Sorbe, 2007. "Assessing the Forecast Properties of the CESifo World Economic Climate Indicator: Evidence for the Euro Area," ifo Working Paper Series 46, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    42. Christian Hawkesby & Ian W Marsh & Ibrahim Stevens, 2005. "Comovements in the prices of securities issued by large complex financial institutions," Bank of England working papers 256, Bank of England.
    43. Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020. "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, vol. 58(1), pages 139-167, January.
    44. El-Shagi, Makram, 2009. "Inflation Expectations: Does the Market Beat Professional Forecasts?," IWH Discussion Papers 16/2009, Halle Institute for Economic Research (IWH).
    45. Marcellino, Massimiliano & Kapetanios, George, 2006. "Impulse Response Functions from Structural Dynamic Factor Models: A Monte Carlo Evaluation," CEPR Discussion Papers 5621, C.E.P.R. Discussion Papers.
    46. Gonzalo Camba-Mendez & George Kapetanios, 2005. "Statistical Tests of the Rank of a Matrix and Their Applications in Econometric Modelling," Working Papers 541, Queen Mary University of London, School of Economics and Finance.
    47. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Staff Working Papers 07-8, Bank of Canada.
    48. Mäkinen, Mikko, 2016. "Nowcasting of Russian GDP growth," BOFIT Policy Briefs 4/2016, Bank of Finland Institute for Emerging Economies (BOFIT).
    49. Otter, Pieter W. & Jacobs, Jan P.A.M., 2006. "On information in static and dynamic factor models," CCSO Working Papers 200605, University of Groningen, CCSO Centre for Economic Research.
    50. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
    51. Dreger, Christian & Schumacher, Christian, 2002. "Estimating large-scale factor models for economic activity in Germany: Do they outperform simpler models?," HWWA Discussion Papers 199, Hamburg Institute of International Economics (HWWA).
    52. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2007. "Automatic Leading Indicators (ALIs) versus Macro Econometric Structural Models (MESMs): Comparison of Inflation and GDP growth Forecasting," EcoMod2007 23900072, EcoMod.
    53. Han, Liyan & Jin, Jiayu & Wu, Lei & Zeng, Hongchao, 2020. "The volatility linkage between energy and agricultural futures markets with external shocks," International Review of Financial Analysis, Elsevier, vol. 68(C).
    54. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas F., 2007. "Measuring Regional Market Integration in Developing Asia: a Dynamic Factor Error Correction Model (DF-ECM) Approach," Working Papers on Regional Economic Integration 8, Asian Development Bank.
    55. George Kapetanios & Massimiliano Marcellino, 2009. "A parametric estimation method for dynamic factor models of large dimensions," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 208-238, March.
    56. Françoise Charpin & Hervé Péléraux, 2000. "L'indicateur avancé de l'OFCE," SciencePo Working papers Main hal-01011215, HAL.
    57. George Kapetanios & Fotis Papailias, 2021. "UK Economic Conditions during the Pandemic: Assessing the Economy using ONS Faster Indicators," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2021-10, Economic Statistics Centre of Excellence (ESCoE).

  137. Dr Martin Weale & Gonzalo Camba-Mendez & George Kapetanios & Ray Smith, 1999. "The Forecasting Performance of the OECD Composite Leading Indicators for France, Germany, Italy," National Institute of Economic and Social Research (NIESR) Discussion Papers 155, National Institute of Economic and Social Research.

    Cited by:

    1. Jennifer Castle & David Hendry & Nicholas W.P. Fawcett, 2011. "Forecasting breaks and forecasting during breaks," Economics Series Working Papers 535, University of Oxford, Department of Economics.
    2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    3. Fichtner, Ferdinand & Rüffer, Rasmus & Schnatz, Bernd, 2009. "Leading indicators in a globalised world," Working Paper Series 1125, European Central Bank.
    4. Hahn, Elke & de Bondt, Gabe, 2010. "Predicting recessions and recoveries in real time: The euro area-wide leading indicator (ALI)," Working Paper Series 1246, European Central Bank.
    5. Blaskowitz, Oliver J. & Herwartz, Helmut, 2008. "Adaptive forecasting of the EURIBOR swap term structure," SFB 649 Discussion Papers 2008-017, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  138. George Kapetanios, 1999. "A Test of M Structural Breaks Under the Unit Root Hypothesis," National Institute of Economic and Social Research (NIESR) Discussion Papers 152, National Institute of Economic and Social Research.

    Cited by:

    1. Nowak-Lehmann D., Felicitas, 2003. "Trade Policy and its Impact On Economic Growth: The Chilean Experience in the Period of 1960 to 1998," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 3(2).
    2. Julien Fouquau & Philippe K. Spieser, 2015. "Statistical evidence about LIBOR manipulation: A "Sherlock Holmes" investigation," Post-Print hal-01160060, HAL.
    3. Zeynel Abidin Ozdemir, 2008. "Efficient market hypothesis: evidence from a small open-economy," Applied Economics, Taylor & Francis Journals, vol. 40(5), pages 633-641.
    4. Felicitas Nowak-Lehmann D. & Dierk Herzer & Sebastian Vollmer & Inmaculada Martínez-Zarzoso, 2006. "Chile´s Market Share in the EU Market: The Role of Price Competition in a Panel Analysis Setting," Ibero America Institute for Econ. Research (IAI) Discussion Papers 139, Ibero-America Institute for Economic Research.

  139. Kapetanios, G., 1999. "Model Selection in Threshold Models," Cambridge Working Papers in Economics 9906, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Driffill, John & Sola, Martin & Kenc, Turalay & Spagnolo, Fabio, 2004. "On Model Selection and Markov Switching: A Empirical Examination of Term Structure Models with Regime Shifts," CEPR Discussion Papers 4165, C.E.P.R. Discussion Papers.
    2. Michael J. Dueker & Zacharias Psaradakis & Martin Sola & Fabio Spagnolo, 2007. "Multivariate contemporaneous threshold autoregressive models," Working Papers 2007-019, Federal Reserve Bank of St. Louis.
    3. Francesco Giordano & Marcella Niglio & Cosimo Damiano Vitale, 2023. "Linear approximation of the Threshold AutoRegressive model: an application to order estimation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 27-56, March.
    4. Dr. James Mitchell, 2010. "A Nonlinear Panel Data Model of Cross-sectional Dependence," National Institute of Economic and Social Research (NIESR) Discussion Papers 370, National Institute of Economic and Social Research.
    5. J-P.Guironnet, 2006. "Analyse cliométrique des cycles de croissance de l'éducation en France (1815-2003): vers un modèle à seuil autorégressif," Economies et Sociétés (Serie 'Histoire Economique Quantitative'), Association Française de Cliométrie (AFC), issue 34, pages 193-214, February.
    6. Kapetanios, G., 1999. "Threshold Models for Trended Time Series," Cambridge Working Papers in Economics 9905, Faculty of Economics, University of Cambridge.
    7. Huseyin Tastan & Nuri Yildirim, 2008. "Business cycle asymmetries in Turkey: an application of Markov-switching autoregressions," International Economic Journal, Taylor & Francis Journals, vol. 22(3), pages 315-333.
    8. Candelon, Bertrand & Lieb, Lenard, 2013. "Fiscal policy in good and bad times," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2679-2694.
    9. Sergey V. Smirnov & Nikolai V. Kondrashov & Anna V. Petronevich, 2016. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," HSE Working papers WP BRP 122/EC/2016, National Research University Higher School of Economics.
    10. Alqaralleh, Huthaifa & Canepa, Alessandra, 2020. "Housing market cycles in large urban areas," Economic Modelling, Elsevier, vol. 92(C), pages 257-267.
    11. Sumru Altug & Melike Bildirici, 2010. "Business Cycles around the Globe: A Regime Switching Approach," Koç University-TUSIAD Economic Research Forum Working Papers 1009, Koc University-TUSIAD Economic Research Forum.
    12. Rinke Saskia & Sibbertsen Philipp, 2016. "Information criteria for nonlinear time series models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 325-341, June.
    13. Michael Dueker & Martin Sola & Fabio Spagnolo, 2006. "Contemporaneous Threshold Autoregressive Models: Estimation, Testing and Forecasting," Department of Economics Working Papers 2006-04, Universidad Torcuato Di Tella.
    14. Wolff, Christian & van Tol, Michel R, 2005. "Forecasting the Spot Exchange Rate with the Term Structure of Forward Premia: Multivariate Threshold Cointegration," CEPR Discussion Papers 4958, C.E.P.R. Discussion Papers.
    15. Coakley, Jerry & Fuertes, Ana-Maria, 2006. "Testing for sign and amplitude asymmetries using threshold autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 30(4), pages 623-654, April.
    16. Gomes, Pedro & Kurter, Zeynep O. & Morita, Rubens, 2022. "European Sovereign Bond and Stock Market Granger Causality Dynamics," The Warwick Economics Research Paper Series (TWERPS) 1405, University of Warwick, Department of Economics.
    17. Ferrara, L. & Marcellino, M. & Mogliani, M., 2012. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," Working papers 383, Banque de France.
    18. Gao, Yan & Zhang, Xinyu & Wang, Shouyang & Chong, Terence Tai Leung & Zou, Guohua, 2017. "Frequentist model averaging for threshold models," MPRA Paper 92036, University Library of Munich, Germany.
    19. Coakley, Jerry & Fuertes, Ana-Maria & Perez, Maria-Teresa, 2003. "Numerical issues in threshold autoregressive modeling of time series," Journal of Economic Dynamics and Control, Elsevier, vol. 27(11-12), pages 2219-2242, September.
    20. Kapetanios George, 2003. "Bootstrap Neural Network Cointegration Tests Against Nonlinear Alternative Hypotheses," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-16, July.
    21. Snaith, S & Kellard, NM & Ahmad, N, 2015. "Open outcry versus electronic trading: tests of market efficiency on crude palm oil futures," Essex Finance Centre Working Papers 15373, University of Essex, Essex Business School.
    22. Ravn, Morten & Sola, Martin & Psaradakis, Zacharias, 2003. "Markov Switching Causality and the Money-Output Relationship," CEPR Discussion Papers 3803, C.E.P.R. Discussion Papers.
    23. George Kapetanios & James Mitchell & Yongcheol Shin, 2010. "A Nonlinear Panel Model of Cross-sectional Dependence," Working Papers 673, Queen Mary University of London, School of Economics and Finance.
    24. van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.
    25. Zacharias Psaradakis & Martin Sola & Fabio Spagnolo & Nicola Spagnolo, 2009. "Selecting nonlinear time series models using information criteria," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(4), pages 369-394, July.
    26. PREMINGER, Arie & WETTSTEIN, David, 2005. "Using the penalized likelihood method for model selection with nuisance parameters present only under the alternative: an application to switching regression models," LIDAM Reprints CORE 1811, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    27. Chalamandaris, George & Pagratis, Spyros, 2019. "Limits to arbitrage and CDS–bond dynamics around the financial crisis," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 213-235.
    28. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Modified information criteria and selection of long memory time series models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 116-131.
    29. Nan Li & Simon S. Kwok, 2021. "Jointly determining the state dimension and lag order for Markov‐switching vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 471-491, July.
    30. Richard T. Baillie & George Kapetanios, 2006. "Nonlinear Models with Strongly Dependent Processes and Applications to Forward Premia and Real Exchange Rates," Working Papers 570, Queen Mary University of London, School of Economics and Finance.
    31. Galeano, Pedro, 2004. "Model selection criteria and quadratic discrimination in ARMA and SETAR time series models," DES - Working Papers. Statistics and Econometrics. WS ws041406, Universidad Carlos III de Madrid. Departamento de Estadística.
    32. Li, Dong & Ling, Shiqing, 2012. "On the least squares estimation of multiple-regime threshold autoregressive models," Journal of Econometrics, Elsevier, vol. 167(1), pages 240-253.
    33. Eugene Canjels & Gauri Prakash-Canjels & Alan M. Taylor, 2004. "Measuring Market Integration: Foreign Exchange Arbitrage and the Gold Standard, 1879-1913," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 868-882, November.
    34. Baillie, Richard T. & Kapetanios, George, 2008. "Nonlinear models for strongly dependent processes with financial applications," Journal of Econometrics, Elsevier, vol. 147(1), pages 60-71, November.
    35. Amine LAHIANI & Olivier SCAILLET, 2008. "Testing for threshold effect in ARFIMA models: Application to US unemployment rate data," Swiss Finance Institute Research Paper Series 08-42, Swiss Finance Institute.
    36. A. J. Khadaroo, 2005. "A threshold in inflation dynamics: evidence from emerging countries," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 719-723.
    37. PREMINGER, Arie & SAKATA, Shinichi, 2005. "A model selection method for S-estimation," LIDAM Discussion Papers CORE 2005073, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    38. Christopoulos, Dimitris & McAdam, Peter & Tzavalis, Elias, 2018. "Dealing with endogeneity in threshold models using copulas: an illustration to the foreign trade multiplier," Working Paper Series 2136, European Central Bank.
    39. Ahmad Jameel Khadaroo, 2016. "Current Account Deficit in Mauritius: Risks and Prospects," South African Journal of Economics, Economic Society of South Africa, vol. 84(1), pages 109-128, March.
    40. Driffill John & Kenc Turalay & Sola Martin & Spagnolo Fabio, 2009. "The Effects of Different Parameterizations of Markov-Switching in a CIR Model of Bond Pricing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(1), pages 1-24, March.
    41. Rinke, Saskia, 2016. "The Influence of Additive Outliers on the Performance of Information Criteria to Detect Nonlinearity," Hannover Economic Papers (HEP) dp-575, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

  140. Andrew Blake, 1999. "A Radial Basis Function Artificial Neural Network Test for ARCH," National Institute of Economic and Social Research (NIESR) Discussion Papers 154, National Institute of Economic and Social Research.

    Cited by:

    1. Tatiana Kirsanova, 2004. "A note on timeless perspective policy design," Money Macro and Finance (MMF) Research Group Conference 2003 50, Money Macro and Finance Research Group.
    2. Jensen, Christian, 2016. "Discretion Rather than Rules? Binding Commitments versus Discretionary Policymaking," MPRA Paper 76838, University Library of Munich, Germany.
    3. Benigno, Pierpaolo & Woodford, Michael, 2012. "Linear-quadratic approximation of optimal policy problems," Journal of Economic Theory, Elsevier, vol. 147(1), pages 1-42.
    4. Seung Hyun Hong & Peter C. B. Phillips, 2005. "Testing Linearity in Cointegrating Relations with an Application to Purchasing Power Parity," Cowles Foundation Discussion Papers 1541, Cowles Foundation for Research in Economics, Yale University.
    5. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    6. KANAZAWA, Nobuyuki & 金澤, 伸幸, 2018. "Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks," Discussion paper series HIAS-E-64, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    7. Di Bartolomeo, Giovanni & Di Pietro, Marco & Giannini, Bianca, 2016. "Optimal monetary policy in a New Keynesian model with heterogeneous expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 373-387.
    8. Brendon, Charles & Ellison, Martin, 2018. "Time-Consistently Undominated Policies," CEPR Discussion Papers 12656, C.E.P.R. Discussion Papers.
    9. Andrew P. Blake & George Kapetanios, 2003. "Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 253-267, May.
    10. Paez-Farrell, Juan, 2011. "Timeless perspective versus discretionary policymaking when the degree of inflation persistence is unknown," Economic Modelling, Elsevier, vol. 28(6), pages 2432-2438.
    11. Lee Tae-Hwy, 2001. "Neural Network Test and Nonparametric Kernel Test for Neglected Nonlinearity in Regression Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(4), pages 1-15, January.
    12. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    13. Yen-Ming Chiang & Wei-Guo Cheng & Fi-John Chang, 2012. "A hybrid artificial neural network-based agri-economic model for predicting typhoon-induced losses," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 769-787, September.
    14. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.

  141. Dr Martin Weale, 1999. "Tests of Rank in Reduced Rank Regression Models," National Institute of Economic and Social Research (NIESR) Discussion Papers 150, National Institute of Economic and Social Research.

    Cited by:

    1. Cubadda, Gianluca, 2004. "A Reduced Rank Regression Approach to Coincident and Leading Indexes Building," Economics & Statistics Discussion Papers esdp04022, University of Molise, Department of Economics.
    2. Fujiwara, Ippei & Koga, Maiko, 2004. "A Statistical Forecasting Method for Inflation Forecasting: Hitting Every Vector Autoregression and Forecasting under Model Uncertainty," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 22(1), pages 123-142, March.
    3. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.
    4. Stephen G. Donald & Natércia Fortuna & Vladas Pipiras, 2005. "On rank estimation in symmetric matrices: the case of indefinite matrix estimators," FEP Working Papers 167, Universidade do Porto, Faculdade de Economia do Porto.
    5. Marcellino, Massimiliano & Kapetanios, George, 2006. "Impulse Response Functions from Structural Dynamic Factor Models: A Monte Carlo Evaluation," CEPR Discussion Papers 5621, C.E.P.R. Discussion Papers.

  142. Kapetanios, G., 1999. "Threshold Models for Trended Time Series," Cambridge Working Papers in Economics 9905, Faculty of Economics, University of Cambridge.

    Cited by:

    1. Baillie, Richard T. & Kapetanios, George, 2007. "Testing for Neglected Nonlinearity in Long-Memory Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 447-461, October.
    2. J-P.Guironnet, 2006. "Analyse cliométrique des cycles de croissance de l'éducation en France (1815-2003): vers un modèle à seuil autorégressif," Economies et Sociétés (Serie 'Histoire Economique Quantitative'), Association Française de Cliométrie (AFC), issue 34, pages 193-214, February.
    3. Birgit Strikholm & Timo Teräsvirta, 2006. "A sequential procedure for determining the number of regimes in a threshold autoregressive model," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 472-491, November.
    4. Frédérique Bec & Annabelle de Gaye, 2019. "Le modèle autorégressif autorégressif à seuil avec effet rebond : Une application aux rendements boursiers français et américains ," Working Papers hal-02014663, HAL.
    5. Huiqing Li & Yang Su, 2021. "The nonlinear causal relationship between short‐ and long‐term interest rates: An empirical assessment of the United States, the United Kingdom, and Japan," International Finance, Wiley Blackwell, vol. 24(3), pages 332-355, December.
    6. Coakley, Jerry & Fuertes, Ana-Maria & Perez, Maria-Teresa, 2003. "Numerical issues in threshold autoregressive modeling of time series," Journal of Economic Dynamics and Control, Elsevier, vol. 27(11-12), pages 2219-2242, September.
    7. Frédérique Bec & Othman Bouabdallah & Laurent Ferrara, 2013. "The European Way out of Recession," Post-Print hal-02980626, HAL.
    8. Strikholm, Birgit & Teräsvirta, Timo, 2005. "Determining the Number of Regimes in a Threshold Autoregressive Model Using Smooth Transition Autoregressions," SSE/EFI Working Paper Series in Economics and Finance 578, Stockholm School of Economics, revised 11 Feb 2005.
    9. M Sensier & D R Osborn & N Öcal, 2002. "Asymmetric Interest Rate Effects for the UK Real Economy," Centre for Growth and Business Cycle Research Discussion Paper Series 10, Economics, The University of Manchester.
    10. George Kapetanios, 2002. "Testing for Neglected Nonlinearity in Long Memory Models," Working Papers 474, Queen Mary University of London, School of Economics and Finance.
    11. Andrew P. Blake & George Kapetanios, 2003. "Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 253-267, May.
    12. Frédérique Bec & Othman Bouabdallah & Laurent Ferrara, 2014. "The way out of recessions: A forecasting analysis for some Euro area countries," Post-Print hal-02979744, HAL.
    13. Baillie, Richard T. & Kapetanios, George, 2008. "Nonlinear models for strongly dependent processes with financial applications," Journal of Econometrics, Elsevier, vol. 147(1), pages 60-71, November.

Articles

  1. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    See citations under working paper version above.
  2. Ilias Chronopoulos & Aristeidis Raftapostolos & George Kapetanios, 2024. "Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 636-669.
    See citations under working paper version above.
  3. George Kapetanios & Laura Serlenga & Yongcheol Shin, 2023. "Testing for correlation between the regressors and factor loadings in heterogeneous panels with interactive effects," Empirical Economics, Springer, vol. 64(6), pages 2611-2659, June.

    Cited by:

    1. Pigini, Claudia & Pionati, Alessandro & Valentini, Francesco, 2023. "Specification testing with grouped fixed effects," MPRA Paper 117821, University Library of Munich, Germany.

  4. Baillie, Richard T. & Diebold, Francis X. & Kapetanios, George & Kim, Kun Ho, 2023. "A new test for market efficiency and uncovered interest parity," Journal of International Money and Finance, Elsevier, vol. 130(C).
    See citations under working paper version above.
  5. George Kapetanios & Fotis Papailias, 2022. "Investigating the predictive ability of ONS big data‐based indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 252-258, March.

    Cited by:

    1. Schlaich, Tim & Hoberg, Kai, 2024. "When is the next order? Nowcasting channel inventories with Point-of-Sales data to predict the timing of retail orders," European Journal of Operational Research, Elsevier, vol. 315(1), pages 35-49.

  6. Richard T. Baillie & Fabio Calonaci & George Kapetanios, 2022. "Hierarchical Time-Varying Estimation of Asset Pricing Models," JRFM, MDPI, vol. 15(1), pages 1-26, January.

    Cited by:

    1. Amjad Taha & Gulcay Tuna, 2023. "Oil Price and Composite Risk Exposure within International Capital Asset Pricing Model: A Case of Saudi Arabia and Turkey," Energies, MDPI, vol. 16(7), pages 1-18, March.

  7. Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022. "Making text count: Economic forecasting using newspaper text," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 896-919, August.
    See citations under working paper version above.
  8. Dendramis, Yiannis & Giraitis, Liudas & Kapetanios, George, 2021. "Estimation Of Time-Varying Covariance Matrices For Large Datasets," Econometric Theory, Cambridge University Press, vol. 37(6), pages 1100-1134, December.
    See citations under working paper version above.
  9. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    See citations under working paper version above.
  10. Kapetanios, George & Serlenga, Laura & Shin, Yongcheol, 2021. "Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 504-531.
    See citations under working paper version above.
  11. Kapetanios, George & Price, Simon & Tasiou, Menelaos & Ventouri, Alexia, 2021. "State-level wage Phillips curves," Econometrics and Statistics, Elsevier, vol. 18(C), pages 1-11.
    See citations under working paper version above.
  12. Giraitis, Liudas & Kapetanios, George & Marcellino, Massimiliano, 2021. "Time-varying instrumental variable estimation," Journal of Econometrics, Elsevier, vol. 224(2), pages 394-415.
    See citations under working paper version above.
  13. Kapetanios, G. & Pesaran, M.H. & Reese, S., 2021. "Detection of units with pervasive effects in large panel data models," Journal of Econometrics, Elsevier, vol. 221(2), pages 510-541.

    Cited by:

    1. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.

  14. Sinem Hacıoğlu Hoke & George Kapetanios, 2021. "Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 125-150, January.

    Cited by:

    1. Mariam Camarero & Alejandro Muñoz & Cecilio Tamarit, 2022. "The rise and fall of global financial flows in EU 15: new evidence using dynamic panels with common correlated effects," Working Papers 2212, Department of Applied Economics II, Universidad de Valencia.

  15. Churm, Rohan & Joyce, Michael & Kapetanios, George & Theodoridis, Konstantinos, 2021. "Unconventional monetary policies and the macroeconomy: The impact of the UK's QE2 and funding for lending scheme," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 721-736.

    Cited by:

    1. Sharon Lai & Kevin Lane & Laura Nunn, 2022. "The Term Funding Facility: Has It Encouraged Business Lending?," RBA Research Discussion Papers rdp2022-07, Reserve Bank of Australia.
    2. Emilie Da Silva & Vincent Grossmann-Wirth & Benoit Nguyen & Miklos Vari, 2021. "Paying Banks to Lend? Evidence from the Eurosystem's TLTRO and the Euro Area Credit Registry," Working papers 848, Banque de France.
    3. Gavazza, Alessandro & Benetton, Matteo & Surico, Paolo, 2021. "Mortgage Pricing and Monetary Policy," CEPR Discussion Papers 16456, C.E.P.R. Discussion Papers.
    4. Laine, Olli-Matti & Nelimarkka, Jaakko, 2023. "Assessing targeted longer-term refinancing operations: Identification through search intensity," Bank of Finland Research Discussion Papers 13/2023, Bank of Finland.
    5. Li, Xiao-Lin & Xie, Pinyi & Ding, Hui & Si, Deng-Kui, 2023. "Central bank lending facility and investment efficiency of non-SOEs: evidence from China," Economic Modelling, Elsevier, vol. 126(C).

  16. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    See citations under working paper version above.
  17. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2020. "Time-varying cointegration with an application to the UK Great Ratios," Economics Letters, Elsevier, vol. 193(C).

    Cited by:

    1. Polbin, Andrey & Skrobotov, Anton, 2022. "On decrease in oil price elasticity of GDP and investment in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 5-24.
    2. Yicong Lin & Mingxuan Song, 2023. "Robust bootstrap inference for linear time-varying coefficient models: Some Monte Carlo evidence," Tinbergen Institute Discussion Papers 23-049/III, Tinbergen Institute.

  18. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2019. "Large time‐varying parameter VARs: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1027-1049, November.
    See citations under working paper version above.
  19. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.

    Cited by:

    1. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2024. "Lessons from nowcasting GDP across the world," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 8, pages 187-217, Edward Elgar Publishing.
    2. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    3. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    4. Ballarin, Giovanni & Dellaportas, Petros & Grigoryeva, Lyudmila & Hirt, Marcel & van Huellen, Sophie & Ortega, Juan-Pablo, 2024. "Reservoir computing for macroeconomic forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1206-1237.
    5. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Papers 2008.01714, arXiv.org, revised Mar 2021.
    6. Ana Beatriz Galvão & Marta Lopresto, 2020. "Real-time Probabilistic Nowcasts of UK Quarterly GDP Growth using a Mixed-Frequency Bottom-up Approach," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-06, Economic Statistics Centre of Excellence (ESCoE).
    7. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    8. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang & Österholm, Pär, 2021. "Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances," Working Papers 2021:9, Örebro University, School of Business.
    9. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    10. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    11. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2021. "Forecasting natural gas prices using highly flexible time-varying parameter models," Economic Modelling, Elsevier, vol. 105(C).
    12. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
    13. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    14. Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
    15. Ba Chu & Shafiullah Qureshi, 2021. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers 21-12, Carleton University, Department of Economics.
    16. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    17. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2020. "Forecasting natural gas prices using highly flexible time-varying parameter models," Working Papers 2020-01, University of Tasmania, Tasmanian School of Business and Economics.
    18. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    19. Danilo Cascaldi-Garcia & Thiago Revil T. Ferreira & Domenico Giannone & Michele Modugno, 2021. "Back to the Present: Learning about the Euro Area through a Now-casting Model," International Finance Discussion Papers 1313, Board of Governors of the Federal Reserve System (U.S.).
    20. Michael P. Clements & Ana Beatriz Galvão, 2023. "Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 164-185, March.
    21. Yolanda S. Stander, 2023. "The Governance and Disclosure of IFRS 9 Economic Scenarios," JRFM, MDPI, vol. 16(1), pages 1-27, January.
    22. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    23. Kim C. Raath & Katherine B. Ensor, 2023. "Wavelet-L2E Stochastic Volatility Models: an Application to the Water-Energy Nexus," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 150-176, May.

  20. Kapetanios, George & Masolo, Riccardo M. & Petrova, Katerina & Waldron, Matthew, 2019. "A time-varying parameter structural model of the UK economy," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.
    See citations under working paper version above.
  21. Chiu, Ching-Wai (Jeremy) & Hayes, Simon & Kapetanios, George & Theodoridis, Konstantinos, 2019. "A new approach for detecting shifts in forecast accuracy," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1596-1612.
    See citations under working paper version above.
  22. George Kapetanios & Fotis Papailias & A. M. Robert Taylor, 2019. "A Generalised Fractional Differencing Bootstrap for Long Memory Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(4), pages 467-492, July.
    See citations under working paper version above.
  23. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2019. "Exponent of Cross-sectional Dependence for Residuals," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 46-102, September.
    See citations under working paper version above.
  24. Kapetanios, George & Konstantinidi, Eirini & Neumann, Michael & Skiadopoulos, George, 2019. "Jumps in option prices and their determinants: Real-time evidence from the E-mini S&P 500 options market," Journal of Financial Markets, Elsevier, vol. 46(C).
    See citations under working paper version above.
  25. Kapetanios, George & Price, Simon & Young, Garry, 2018. "A UK financial conditions index using targeted data reduction: Forecasting and structural identification," Econometrics and Statistics, Elsevier, vol. 7(C), pages 1-17.
    See citations under working paper version above.
  26. A. Chudik & G. Kapetanios & M. Hashem Pesaran, 2018. "A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High‐Dimensional Linear Regression Models," Econometrica, Econometric Society, vol. 86(4), pages 1479-1512, July.
    See citations under working paper version above.
  27. Georgios Chortareas & George Kapetanios & Georgios Magkonis, 2018. "Resuscitating real interest rate parity: new evidence from panels," The European Journal of Finance, Taylor & Francis Journals, vol. 24(14), pages 1176-1189, September.

    Cited by:

    1. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2022. "Globalization, long memory, and real interest rate convergence: a historical perspective," Empirical Economics, Springer, vol. 63(5), pages 2331-2355, November.
    2. Zixiong Xie & Shyh-Wei Chen & An-Chi Wu, 2023. "Real interest rate parity in the Pacific Rim countries: new empirical evidence," Empirical Economics, Springer, vol. 64(3), pages 1471-1515, March.

  28. Dias, Gustavo Fruet & Kapetanios, George, 2018. "Estimation and forecasting in vector autoregressive moving average models for rich datasets," Journal of Econometrics, Elsevier, vol. 202(1), pages 75-91.
    See citations under working paper version above.
  29. Liudas Giraitis & George Kapetanios & Tony Yates, 2018. "Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(2), pages 129-149, March.
    See citations under working paper version above.
  30. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    See citations under working paper version above.
  31. Baltas, Konstantinos N. & Kapetanios, George & Tsionas, Efthymios & Izzeldin, Marwan, 2017. "Liquidity creation through efficient M&As: A viable solution for vulnerable banking systems? Evidence from a stress test under a panel VAR methodology," Journal of Banking & Finance, Elsevier, vol. 83(C), pages 36-56.

    Cited by:

    1. Guo-Hua Cao & Jing Zhang, 2021. "Is a sustainable loop of economy and entrepreneurial ecosystem possible? a structural perspective," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 7002-7040, May.
    2. Guo‐Hua Cao & Jing Zhang, 2022. "The entrepreneurial ecosystem of inclusive finance and entrepreneurship: A theoretical and empirical test in China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1547-1568, January.
    3. Budny Katarzyna & Krasodomska Joanna & Świetla Katarzyna, 2019. "Performance Changes Around Banks Mergers and Acquisitions: Evidence from Poland," Financial Sciences. Nauki o Finansach, Sciendo, vol. 24(2), pages 28-45, June.
    4. Yacoub Sleibi & Fabrizio Casalin & Giorgio Fazio, 2020. "Bank-specific shocks and aggregate leverage: Empirical evidence from a panel of developed countries," Post-Print hal-03133033, HAL.
    5. Duan, Ying & Niu, Jijun, 2020. "Liquidity creation and bank profitability," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    6. Osoro, Jared & Josea, Kiplangat, 2022. "Banking system adjustment to shock: The Kenyan case of liquidity-profitability trade-offs," KBA Centre for Research on Financial Markets and Policy Working Paper Series 56, Kenya Bankers Association (KBA).
    7. Muhammad Farhan Basheer & Waeibrorheem Waemustafa & Mohamad Helmi Bin Hidthiir & Saira Ghulam Hassan, 2021. "Explaining the endogeneity between the credit risk, liquidity risk, and off-balance sheet activities in commercial banks: a case of South Asian economies," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 14(2), pages 166-187.
    8. Chiaramonte, Laura & Dreassi, Alberto & Piserà, Stefano & Khan, Ashraf, 2023. "Mergers and acquisitions in the financial industry: A bibliometric review and future research directions," Research in International Business and Finance, Elsevier, vol. 64(C).
    9. Mike G. Tsionas & Konstantinos N. Baltas, 2022. "On identifying risk-adjusted efficiency gains or losses of prospective mergers and acquisitions," Annals of Operations Research, Springer, vol. 318(1), pages 619-683, November.
    10. Guo, Pin & Zhang, Cheng, 2023. "The impact of bank FinTech on liquidity creation: Evidence from China," Research in International Business and Finance, Elsevier, vol. 64(C).

  32. Galvão, Ana Beatriz & Giraitis, Liudas & Kapetanios, George & Petrova, Katerina, 2016. "A time varying DSGE model with financial frictions," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 690-716.
    See citations under working paper version above.
  33. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    See citations under working paper version above.
  34. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    See citations under working paper version above.
  35. George Kapetanios & Zacharias Psaradakis, 2016. "Semiparametric Sieve-Type Generalized Least Squares Inference," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 951-985, June.

    Cited by:

    1. Mayer, Alexander, 2020. "(Consistently) testing strict exogeneity against the alternative of predeterminedness in linear time-series models," Economics Letters, Elsevier, vol. 193(C).

  36. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.

    Cited by:

    1. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    2. Krzysztof DRACHAL, 2020. "Forecasting the Inflation Rate in Poland and U.S. Using Dynamic Model Averaging (DMA) and Google Queries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 18-34, July.
    3. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    4. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
    5. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    6. Marcel Ausloos & Roy Cerqueti & Francesca Bartolacci & Nicola G. Castellano, 2018. "SME investment best strategies. Outliers for assessing how to optimize performance," Papers 1807.09583, arXiv.org.
    7. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    8. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," IREA Working Papers 201801, University of Barcelona, Research Institute of Applied Economics, revised Jan 2018.
    9. Jović, Srđan & Maksimović, Goran & Jovović, David, 2016. "Appraisal of natural resources rents and economic development," Resources Policy, Elsevier, vol. 50(C), pages 289-291.
    10. Đokić, Aleksandar & Jović, Srđan, 2017. "Evaluation of agriculture and industry effect on economic health by ANFIS approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 396-399.
    11. Goran Maksimović & Srđan Jović & David Jovović & Marina Jovović, 2019. "RETRACTED ARTICLE: Analyses of Economic Development Based on Different Factors," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1103-1109, March.
    12. Maksimović, Goran & Jović, Srđan & Jovanović, Radomir, 2017. "Economic growth rate management by soft computing approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 520-524.
    13. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
    14. Petri Kuosmanen & Juuso Vataja, 2017. "The return of financial variables in forecasting GDP growth in the G-7," Economic Change and Restructuring, Springer, vol. 50(3), pages 259-277, August.
    15. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    16. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    17. Oscar Claveria & Enric Monte & Salvador Torra, 2021. ""Nowcasting and forecasting GDP growth with machine-learning sentiment indicators"," IREA Working Papers 202103, University of Barcelona, Research Institute of Applied Economics, revised Feb 2021.

  37. Georges Kapetanios & Lynda Khalaf & Massimiliano Marcellino, 2016. "Factor‐Based Identification‐Robust Interference in IV Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(5), pages 821-842, August.

    Cited by:

    1. Omer Bayar, 2022. "Reducing large datasets to improve the identification of estimated policy rules," Empirical Economics, Springer, vol. 63(1), pages 113-140, July.

  38. Chortareas, Georgios & Kapetanios, George & Ventouri, Alexia, 2016. "Credit market freedom and cost efficiency in US state banking," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 173-185.

    Cited by:

    1. Andrea Calef & Ifigenia Georgiou & Alfonsina Iona, 2022. "Credit Market Freedom and Corporate Decisions," University of East Anglia School of Economics Working Paper Series 2022-09, School of Economics, University of East Anglia, Norwich, UK..
    2. Sapci, Ayse & Miles, Bradley, 2019. "Bank size, returns to scale, and cost efficiency," Journal of Economics and Business, Elsevier, vol. 105(C).
    3. Lalith Seelanatha, 2021. "Political Instability, Civil War and Cost Efficiency of Banking Firms: A Case Study in Sri Lanka," Asian Economic Journal, East Asian Economic Association, vol. 35(3), pages 294-316, September.
    4. Lin Tian & Liang Han & Biao Mi, 2020. "Bank competition, information specialization and innovation," Review of Quantitative Finance and Accounting, Springer, vol. 54(3), pages 1011-1035, April.
    5. Pi, Tianlei & Yang, Xiaobing, 2023. "Board culture and bank innovation: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 732-755.
    6. Iosifidi, Maria & Panopoulou, Ekaterini & Tsoumas, Chris, 2021. "Mortgage loan demand and banks’ operational efficiency," Journal of Financial Stability, Elsevier, vol. 53(C).
    7. Abreu, Emmanuel Sousa de & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2019. "What is going on with studies on banking efficiency?," Research in International Business and Finance, Elsevier, vol. 47(C), pages 195-219.
    8. Iona, Alfonsina & Leonida, Leone & Limosani, Michele & Maimone Ansaldo Patti, Dario & Navarra, Pietro, 2024. "Does economic liberalization foster corporate investment? Theory and evidence from US and Canadian firms," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    9. Nguyen, Dung Thuy Thi & Diaz-Rainey, Ivan & Roberts, Helen & Le, Minh, 2022. "The non-monotonic relationship between financial integration and cost efficiency: Evidence from East Asian commercial banks," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 418-438.
    10. Barrell, Ray & Karim, Dilruba & Ventouri, Alexia, 2017. "Interest rate liberalization and capital adequacy in models of financial crises," Journal of Financial Stability, Elsevier, vol. 33(C), pages 261-272.
    11. Li, Yang, 2020. "Analyzing efficiencies of city commercial banks in China: An application of the bootstrapped DEA approach," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    12. Ren, Xingzi & Song, Ke & Zhu, Kairan & Yang, Xinyu, 2024. "Does digital transformation increase bank profit efficiency? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
    13. Degl'Innocenti, Marta & Grant, Kevin & Šević, Aleksandar & Tzeremes, Nickolaos G., 2018. "Financial stability, competitiveness and banks' innovation capacity: Evidence from the Global Financial Crisis," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 35-46.
    14. Blau, Benjamin M., 2017. "Economic freedom and crashes in financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 47(C), pages 33-46.
    15. Segev, Nimrod & Schaffer, Matthew, 2020. "Monetary policy, bank competition and regional credit cycles: Evidence from a quasi-natural experiment," Journal of Corporate Finance, Elsevier, vol. 64(C).
    16. Woon Kan Yap & Siong Hock Law & Judhiana Abdul-Ghani, 2019. "Effects of Credit Market Freedom on Output Reallocation in China's Banking Sector Through the Intermediation of Cost X-inefficiency," Annals of Economics and Finance, Society for AEF, vol. 20(2), pages 691-720, November.
    17. Tan, Yong & Floros, Christos, 2018. "Risk, competition and efficiency in banking: Evidence from China," Global Finance Journal, Elsevier, vol. 35(C), pages 223-236.
    18. Ebrahimi, Sajad & Ebrahimnejad, Ali & Rastad, Mahdi, 2023. "Number of creditors and the real effects of credit supply disruptions," Emerging Markets Review, Elsevier, vol. 55(C).

  39. Liudas Giraitis & George Kapetanios & Anne Wetherilt & Filip ŽIKEŠ, 2016. "Estimating the Dynamics and Persistence of Financial Networks, with an Application to the Sterling Money Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 58-84, January.

    Cited by:

    1. Piero Mazzarisi & Paolo Barucca & Fabrizio Lillo & Daniele Tantari, 2017. "A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market," Papers 1801.00185, arXiv.org.
    2. Galvão, Ana Beatriz & Giraitis, Liudas & Kapetanios, George & Petrova, Katerina, 2016. "A time varying DSGE model with financial frictions," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 690-716.
    3. Dungey, Mardi & Luciani, Matteo & Veredas, David, 2018. "Systemic risk in the US: Interconnectedness as a circuit breaker," Economic Modelling, Elsevier, vol. 71(C), pages 305-315.
    4. Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2019. "Crisis transmission: visualizing vulnerability," Working Papers 2019-07, University of Tasmania, Tasmanian School of Business and Economics.
    5. Sun, Hang, 2016. "Crisis-Contingent Dynamics of Connectedness: An SVAR-Spatial-Network “Tripod” Model with Thresholds," Research Memorandum 032, Maastricht University, Graduate School of Business and Economics (GSBE).
    6. Dungey, Mardi & Chowdhury, Biplob & Kangogo, Moses & Sayeed, Mohammad Abu & Volkov, Vladimir, 2018. "The Changing Network of Financial Market Linkages: The Asian Experience," ADB Economics Working Paper Series 558, Asian Development Bank.
    7. Dungey, Mardi & Harvey, John & Siklos, Pierre & Volkov, Vladimir, 2017. "Signed spillover effects building on historical decompositions," Working Papers 2017-11, University of Tasmania, Tasmanian School of Business and Economics.
    8. Ruben Hipp, 2020. "On Causal Networks of Financial Firms: Structural Identification via Non-parametric Heteroskedasticity," Staff Working Papers 20-42, Bank of Canada.
    9. Dungey, Mardi & Harvey, John & Volkov, Vladimir, 2017. "The changing international network of sovereign debt and financial institutions," Working Papers 2017-04, University of Tasmania, Tasmanian School of Business and Economics.
    10. Fernando Linardi & Cees Diks & Marco van der Leij & Iuri Lazier, 2018. "Dynamic Interbank Network Analysis Using Latent Space Models," Working Papers Series 487, Central Bank of Brazil, Research Department.
    11. Domenico Di Gangi & Giacomo Bormetti & Fabrizio Lillo, 2022. "Score Driven Generalized Fitness Model for Sparse and Weighted Temporal Networks," Papers 2202.09854, arXiv.org, revised Mar 2022.
    12. Mardi Dungey & Marius Matei & Matteo Luciani & David Veredas, 2017. "Surfing through the GFC: Systemic Risk in Australia," The Economic Record, The Economic Society of Australia, vol. 93(300), pages 1-19, March.
    13. Kobayashi, Teruyoshi & Takaguchi, Taro, 2018. "Identifying relationship lending in the interbank market: A network approach," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 20-36.
    14. Dias, Gustavo Fruet, 2017. "The time-varying GARCH-in-mean model," Economics Letters, Elsevier, vol. 157(C), pages 129-132.
    15. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
    16. Ana Beatriz Galvão & Liudas Giraitis & George Kapetanios & Katerina Petrova, 2015. "A Bayesian Local Likelihood Method for Modelling Parameter Time Variation in DSGE Models," Working Papers 770, Queen Mary University of London, School of Economics and Finance.
    17. Kapetanios, George & Masolo, Riccardo M. & Petrova, Katerina & Waldron, Matthew, 2019. "A time-varying parameter structural model of the UK economy," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.
    18. Shu Takahashi & Kento Yamamoto & Shumpei Kobayashi & Ryoma Kondo & Ryohei Hisano, 2024. "Dynamic Link and Flow Prediction in Bank Transfer Networks," Papers 2409.08718, arXiv.org, revised Oct 2024.
    19. Morteza Alaeddini & Philippe Madiès & Paul J. Reaidy & Julie Dugdale, 2023. "Interbank money market concerns and actors’ strategies—A systematic review of 21st century literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 573-654, April.
    20. Daniela Scidá, 2023. "Structural VAR and financial networks: A minimum distance approach to spatial modeling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 49-68, January.
    21. Filip Zikes, 2017. "Measuring Transaction Costs in the Absence of Timestamps," Finance and Economics Discussion Series 2017-045, Board of Governors of the Federal Reserve System (U.S.).
    22. Kapetanios, George & Zikes, Filip, 2018. "Time-varying Lasso," Economics Letters, Elsevier, vol. 169(C), pages 1-6.
    23. Fabrizio Lillo & Giorgio Rizzini, 2024. "Modelling shock propagation and resilience in financial temporal networks," Papers 2407.09340, arXiv.org.

  40. Kapetanios, George & Maule, Becky & Young, Garry, 2016. "A new summary measure of inflation expectations," Economics Letters, Elsevier, vol. 149(C), pages 83-85.

    Cited by:

    1. Jason Lennard & Finn Meinecke & Solomos Solomou, 2023. "Measuring inflation expectations in interwar Britain," Economic History Review, Economic History Society, vol. 76(3), pages 844-870, August.
    2. McNeil, James, 2023. "Monetary policy and the term structure of inflation expectations with information frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    3. Dzmitry Kruk, 2016. "SVAR Approach for Extracting Inflation Expectations Given Severe Monetary Shocks: Evidence from Belarus," BEROC Working Paper Series 39, Belarusian Economic Research and Outreach Center (BEROC).
    4. José Vicente Romero & Sara Naranjo-Saldarriaga, 2022. "Weather Shocks and Inflation Expectations in Semi-Structural Models," Borradores de Economia 1218, Banco de la Republica de Colombia.

  41. Caggiano, Giovanni & Calice, Pietro & Leonida, Leone & Kapetanios, George, 2016. "Comparing logit-based early warning systems: Does the duration of systemic banking crises matter?," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 104-116.

    Cited by:

    1. Mirjana Jemović & Srđan Marinković, 2021. "Determinants of financial crises—An early warning system based on panel logit regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 103-117, January.
    2. Krzysztof Biegun & Jacek Karwowski & Piotr Luty, 2021. "How Effective is Macroeconomic Imbalance Procedure (MIP) in Predicting Negative Macroeconomic Phenomena?," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 822-837.
    3. Cheng, Xian & Zhao, Haichuan, 2019. "Modeling, analysis and mitigation of contagion in financial systems," Economic Modelling, Elsevier, vol. 76(C), pages 281-292.
    4. Calice, Pietro & Leonida, Leone & Muzzupappa, Eleonora, 2021. "Concentration-stability vs concentration-fragility. New cross-country evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    5. Pigini, Claudia, 2021. "Penalized maximum likelihood estimation of logit-based early warning systems," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1156-1172.
    6. John Nkwoma Inekwe, 2019. "The exploration of economic crises: parameter uncertainty and predictive ability," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(2), pages 290-313, May.
    7. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2024. "MCMC conditional maximum likelihood for the two-way fixed-effects logit," Econometric Reviews, Taylor & Francis Journals, vol. 43(6), pages 379-404, July.
    8. Mathonnat, Clément & Minea, Alexandru, 2018. "Financial development and the occurrence of banking crises," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 344-354.
    9. Babasyan,Davit & Gu,Yunfan & Melecky,Martin, 2022. "Late Banking Transitions : Comparing Uzbekistan to Earlier Reformers," Policy Research Working Paper Series 9984, The World Bank.
    10. Bentes, Sónia R., 2021. "On the hysteresis of financial crises in the US: Evidence from S&P 500," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    11. Allaj, Erindi & Sanfelici, Simona, 2023. "Early Warning Systems for identifying financial instability," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1777-1803.
    12. Maria Siranova & Karol Zelenak, 2023. "Every crisis does matter: Comparing the databases of financial crisis events," Review of International Economics, Wiley Blackwell, vol. 31(2), pages 652-686, May.
    13. Hossein Dastkhan, 2021. "Network‐based early warning system to predict financial crisis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 594-616, January.
    14. Xianglong Liu, 2023. "Towards Better Banking Crisis Prediction: Could an Automatic Variable Selection Process Improve the Performance?," The Economic Record, The Economic Society of Australia, vol. 99(325), pages 288-312, June.
    15. Filippopoulou, Chryssanthi & Galariotis, Emilios & Spyrou, Spyros, 2020. "An early warning system for predicting systemic banking crises in the Eurozone: A logit regression approach," Journal of Economic Behavior & Organization, Elsevier, vol. 172(C), pages 344-363.
    16. Panayotis Michaelides & Mike Tsionas & Panos Xidonas, 2020. "A Bayesian Signals Approach for the Detection of Crises," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 551-585, September.
    17. Chung‐Hua Shen & Hsing‐Hua Hsu, 2022. "The determinants of Asian banking crises—Application of the panel threshold logit model," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 248-277, March.
    18. Huynh, Tran & Uebelmesser, Silke, 2024. "Early warning models for systemic banking crises: Can political indicators improve prediction?," European Journal of Political Economy, Elsevier, vol. 81(C).
    19. Maria Siranova & Menbere Workie Tiruneh & Brian Konig, 2024. "From abnormal FDI to a normal driver of sudden stop episodes," Working Papers 2024.02, International Network for Economic Research - INFER.
    20. I. Fustos & R. Abarca-del-Rio & P. Moreno-Yaeger & M. Somos-Valenzuela, 2020. "Rainfall-Induced Landslides forecast using local precipitation and global climate indexes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(1), pages 115-131, May.
    21. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    22. Bauer, Gregory H., 2017. "International house price cycles, monetary policy and credit," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 88-114.
    23. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
    24. Irfan Nurfalah & Aam Slamet Rusydiana & Nisful Laila & Eko Fajar Cahyono, 2018. "Early Warning to Banking Crises in the Dual Financial System in Indonesia: The Markov Switching Approach التحذير المبكر من الأزمات المصرفية في النظام المالي المزدوج في إندونيسيا: مقاربة ماركوف للتحويل," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 31(2), pages 133-156, July.
    25. Fu, Junhui & Zhou, Qingling & Liu, Yufang & Wu, Xiang, 2020. "Predicting stock market crises using daily stock market valuation and investor sentiment indicators," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).

  42. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2016. "Exponent of Cross‐Sectional Dependence: Estimation and Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 929-960, September.
    See citations under working paper version above.
  43. Kapetanios, G. & Mitchell, J. & Price, S. & Fawcett, N., 2015. "Generalised density forecast combinations," Journal of Econometrics, Elsevier, vol. 188(1), pages 150-165.
    See citations under working paper version above.
  44. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2015. "Shifts in volatility driven by large stock market shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 130-147.

    Cited by:

    1. Abrar, Afsheen & Naeem, Muhammad Abubakr & Karim, Sitara & Lucey, Brian M. & Vigne, Samuel A., 2024. "Shining in or fading out: Do precious metals sparkle for cryptocurrencies?," Resources Policy, Elsevier, vol. 90(C).
    2. Saeed, Tareq & Bouri, Elie & Alsulami, Hamed, 2021. "Extreme return connectedness and its determinants between clean/green and dirty energy investments," Energy Economics, Elsevier, vol. 96(C).
    3. Cai, Mei-Ling & Chen, Zhang-HangJian & Li, Sai-Ping & Xiong, Xiong & Zhang, Wei & Yang, Ming-Yuan & Ren, Fei, 2022. "New volatility evolution model after extreme events," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    4. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    5. Mei-Ling Cai & Zhang-HangJian Chen & Sai-Ping Li & Xiong Xiong & Wei Zhang & Ming-Yuan Yang & Fei Ren, 2022. "New volatility evolution model after extreme events," Papers 2201.03213, arXiv.org.
    6. Wen, Fenghua & Wang, Kangsheng & Zeng, Aiqing, 2024. "Return spillover across the carbon market and financial markets: A quantile-based approach," Research in International Business and Finance, Elsevier, vol. 69(C).
    7. Syed Jawad Hussain Shahzad & Elie Bouri & Ladislav Kristoufek & Tareq Saeed, 2021. "Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-23, December.
    8. Urom, Christian & Ndubuisi, Gideon & Guesmi, Khaled, 2024. "Global macroeconomic factors and the connectedness among NFTs and (un)conventional assets," Research in International Business and Finance, Elsevier, vol. 71(C).
    9. Tomohiro Ando & Matthew Greenwood-Nimmo & Yongcheol Shin, 2022. "Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks," Management Science, INFORMS, vol. 68(4), pages 2401-2431, April.
    10. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2023. "Quantile spillovers and connectedness analysis between oil and African stock markets," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 60-83.
    11. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Adewuyi, Adeolu O. & Lee, Chien-Chiang, 2022. "Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Energy Economics, Elsevier, vol. 113(C).
    12. Zhongzheng, Wang, 2023. "Extreme risk transmission mechanism between oil, green bonds and new energy vehicles," Innovation and Green Development, Elsevier, vol. 2(3).
    13. Urom, C. & Ndubuisi, Gideon & Guesmi, K., 2022. "Quantile return and volatility connectedness among Non-Fungible Tokens (NFTs) and (un)conventional asset," MERIT Working Papers 2022-017, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    14. Bouri, Elie & Harb, Etienne, 2022. "The size of good and bad volatility shocks does matter for spillovers," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    15. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2020. "Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis," International Review of Financial Analysis, Elsevier, vol. 72(C).
    16. Zhou, Xiaoran & Enilov, Martin & Parhi, Mamata, 2024. "Does oil spin the commodity wheel? Quantile connectedness with a common factor error structure across energy and agricultural markets," Energy Economics, Elsevier, vol. 132(C).
    17. Bouri, Elie & Saeed, Tareq & Vo, Xuan Vinh & Roubaud, David, 2021. "Quantile connectedness in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    18. Dendramis, Y. & Tzavalis, E. & Varthalitis, P. & Athanasiou, E., 2020. "Predicting default risk under asymmetric binary link functions," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1039-1056.
    19. Dai, Zhifeng & Zhang, Xiaotong & Yin, Zhujia, 2023. "Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Evidence from a quantile-based analysis," Energy Economics, Elsevier, vol. 118(C).
    20. Yaoxun Deng & Guobin Fang & Jun Zhang & Huimin Ma, 2024. "Dynamic Connectedness Among Oil, Food Commodity, and Renewable Energy Markets: Novel Perspective from Quantile Dependence and Deep Learning," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 9935-9974, September.
    21. Mihailo Jovanović & Vladica Stojanović & Kristijan Kuk & Brankica Popović & Petar Čisar, 2022. "Asymptotic Properties and Application of GSB Process: A Case Study of the COVID-19 Dynamics in Serbia," Mathematics, MDPI, vol. 10(20), pages 1-28, October.
    22. Meligkotsidou, Loukia & Tzavalis, Elias & Vrontos, Ioannis, 2017. "On Bayesian analysis and unit root testing for autoregressive models in the presence of multiple structural breaks," Econometrics and Statistics, Elsevier, vol. 4(C), pages 70-90.
    23. Chen, Jinyu & Liang, Zhipeng & Ding, Qian & Liu, Zhenhua, 2022. "Extreme spillovers among fossil energy, clean energy, and metals markets: Evidence from a quantile-based analysis," Energy Economics, Elsevier, vol. 107(C).

  45. Kapetanios, George & Price, Simon & Theodoridis, Konstantinos, 2015. "A new approach to multi-step forecasting using dynamic stochastic general equilibrium models," Economics Letters, Elsevier, vol. 136(C), pages 237-242.

    Cited by:

    1. Michal Franta, 2016. "Iterated Multi-Step Forecasting with Model Coefficients Changing Across Iterations," Working Papers 2016/05, Czech National Bank.

  46. Giraitis, L. & Kapetanios, G. & Yates, T., 2014. "Inference on stochastic time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 179(1), pages 46-65.

    Cited by:

    1. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    2. Galvão, Ana Beatriz & Giraitis, Liudas & Kapetanios, George & Petrova, Katerina, 2016. "A time varying DSGE model with financial frictions," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 690-716.
    3. Cheng, Ka Ming, 2022. "Doubts on natural rate of unemployment: Evidence and policy implications," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 230-239.
    4. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    5. Richard T. Baillie & Dooyeon Cho, 2016. "Assessing Euro Crises from a Time Varying International CAPM Approach," Working Paper series 16-03, Rimini Centre for Economic Analysis.
    6. Marcellino, Massimiliano & Kapetanios, George & Giraitis, Liudas, 2020. "Time-Varying Instrumental Variable Estimation," CEPR Discussion Papers 15210, C.E.P.R. Discussion Papers.
    7. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2019. "Time-varying cointegration and the UK great ratios," Bank of England working papers 789, Bank of England.
    8. Lorenzo Trapani, 2018. "Testing for strict stationarity in a random coefficient autoregressive model," Discussion Papers 18/02, University of Nottingham, Granger Centre for Time Series Econometrics.
    9. Buono, Ines & Formai, Sara, 2018. "New evidence on the evolution of the anchoring of inflation expectations," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 39-54.
    10. Magnus Reif, 2022. "Time‐Varying Dynamics of the German Business Cycle: A Comprehensive Investigation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(1), pages 80-102, February.
    11. Mathias Klein & Ludger Linnemann, 2018. "Macroeconomic Effects of Government Spending: The Great Recession Was (Really) Different," Discussion Papers of DIW Berlin 1754, DIW Berlin, German Institute for Economic Research.
    12. Niko Hauzenberger & Florian Huber & Massimiliano Marcellino & Nico Petz, 2021. "Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty," Papers 2112.01995, arXiv.org, revised Nov 2022.
    13. Chronopoulos, Ilias & Kapetanios, George & Petrova, Katerina, 2021. "Kernel-based Volatility Generalised Least Squares," Econometrics and Statistics, Elsevier, vol. 20(C), pages 2-11.
    14. Baillie, Richard T. & Cho, Dooyeon, 2014. "Time variation in the standard forward premium regression: Some new models and tests," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 52-63.
    15. Klein, Mathias & Linnemann, Ludger, 2020. "The time-varying effect of fiscal policy on inflation: Evidence from historical US data," Economics Letters, Elsevier, vol. 186(C).
    16. Karanasos, Menelaos & Paraskevopoulos,Alexandros & Canepa, Alessandra, 2020. "Unified Theory for the Large Family of Time Varying Models with Arma Representations: One Solution Fits All," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202008, University of Turin.
    17. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    18. Baruník, Jozef & Bevilacqua, Mattia & Faff, Robert, 2024. "Dynamic industry uncertainty networks and the business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
    19. Petrova, Katerina, 2022. "Asymptotically valid Bayesian inference in the presence of distributional misspecification in VAR models," Journal of Econometrics, Elsevier, vol. 230(1), pages 154-182.
    20. Henrik Jensen & Ivan Petrella & Søren Hove Ravn & Emiliano Santoro, 2020. "Leverage and Deepening Business-Cycle Skewness," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(1), pages 245-281, January.
    21. Mikihito Nishi, 2024. "Estimating Time-Varying Parameters of Various Smoothness in Linear Models via Kernel Regression," Papers 2406.14046, arXiv.org, revised Oct 2024.
    22. Laura Liu & Christian Matthes & Katerina Petrova, 2022. "Monetary Policy Across Space and Time," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 37-64, Emerald Group Publishing Limited.
    23. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    24. Anna Bykhovskaya & Peter C. B. Phillips, 2017. "Boundary Limit Theory for Functional Local to Unity Regression," Cowles Foundation Discussion Papers 2108, Cowles Foundation for Research in Economics, Yale University.
    25. Yubo Tao & Peter C.B. Phillips & Jun Yu, 2017. "Random Coefficient Continuous Systems: Testing for Extreme Sample Path Behaviour," Cowles Foundation Discussion Papers 2114, Cowles Foundation for Research in Economics, Yale University.
    26. Hongjun Li & Zhongjian Lin & Cheng Hsiao, 2015. "Testing purchasing power parity hypothesis: a semiparametric varying coefficient approach," Empirical Economics, Springer, vol. 48(1), pages 427-438, February.
    27. Maria Kulikova & Gennady Kulikov, 2023. "Estimation of market efficiency process within time-varying autoregressive models by extended Kalman filtering approach," Papers 2310.04125, arXiv.org.
    28. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2020. "Time-varying cointegration with an application to the UK Great Ratios," Economics Letters, Elsevier, vol. 193(C).
    29. Yoosoon Chang & Boreum Kwak, 2017. "U.S. Monetary-Fiscal Regime Changes in the Presence of Endogenous Feedback in Policy Rules," CAEPR Working Papers 2017-016, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    30. Gerald Carlino & Nicholas Zarra & Robert Inman & Thorsten Drautzburg, 2019. "Fiscal Policy in Monetary Unions: State Partisanship and its Macroeconomic Effects," 2019 Meeting Papers 434, Society for Economic Dynamics.
    31. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
    32. Degui Li & Peter C.B. Phillips & Jiti Gao, 2017. "Kernel-Based Inference In Time-Varying Coefficient Cointegrating Regression," Cowles Foundation Discussion Papers 2109, Cowles Foundation for Research in Economics, Yale University.
    33. Stefano Neri & Fabio Busetti & Cristina Conflitti & Francesco Corsello & Davide Delle Monache & Alex Tagliabracci, 2023. "Energy price shocks and inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 792, Bank of Italy, Economic Research and International Relations Area.
    34. Anuradha Patnaik, 2025. "Are inflation expectations anchored in India? A peek through the credibility lens," International Economics and Economic Policy, Springer, vol. 22(1), pages 1-23, February.
    35. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.
    36. Polbin, Andrey & Skrobotov, Anton, 2022. "On decrease in oil price elasticity of GDP and investment in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 5-24.
    37. Jiang, Shangrong & Li, Yuze & Lu, Quanying & Wang, Shouyang & Wei, Yunjie, 2022. "Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets," Research in International Business and Finance, Elsevier, vol. 59(C).
    38. Papailias, Fotis & Liu, Jiadong & Thomakos, Dimitrios D., 2019. "Return Signal Momentum," QBS Working Paper Series 2019/04, Queen's University Belfast, Queen's Business School.
    39. Li, Xixi & Yuan, Jingsong, 2024. "DeepTVAR: Deep learning for a time-varying VAR model with extension to integrated VAR," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1123-1133.
    40. Lucchetti, Riccardo & Valentini, Francesco, 2021. "Kernel-based Time-Varying IV estimation: handle with care," MPRA Paper 110033, University Library of Munich, Germany.
    41. Li, Dong & Zhang, Xingfa & Zhu, Ke & Ling, Shiqing, 2018. "The ZD-GARCH model: A new way to study heteroscedasticity," Journal of Econometrics, Elsevier, vol. 202(1), pages 1-17.
    42. Mr. Nooman Rebei, 2012. "What (Really) Accounts for the Fall in Hours After a Technology Shock?," IMF Working Papers 2012/211, International Monetary Fund.
    43. Kapetanios, George & Masolo, Riccardo M. & Petrova, Katerina & Waldron, Matthew, 2019. "A time-varying parameter structural model of the UK economy," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.
    44. Jinshun Wu & Luyao Wu, 2024. "Bayesian Local Likelihood Estimation of Time-Varying DSGE Models: Allowing for Indeterminacy," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2437-2476, October.
    45. Laura Liu & Christian Matthes & Katerina Petrova, 2018. "Monetary Policy across Space and Time," Working Paper 18-14, Federal Reserve Bank of Richmond.
    46. Zongwu Cai & Xiyuan Liu, 2021. "Solving the Price Puzzle Via A Functional Coefficient Factor-Augmented VAR Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202106, University of Kansas, Department of Economics, revised Jan 2021.
    47. Papailias, Fotis, 2022. "US and EA yield curve persistence during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 44(C).
    48. Guido Bulligan & Eliana Viviano, 2017. "Has the wage Phillips curve changed in the euro area?," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 6(1), pages 1-22, December.
    49. Trapani, Lorenzo, 2021. "A test for strict stationarity in a random coefficient autoregressive model of order 1," Statistics & Probability Letters, Elsevier, vol. 177(C).
    50. Yu Bai & Massimiliano Marcellino & George Kapetanios, 2023. "Mean Group Instrumental Variable Estimation of Time-Varying Large Heterogeneous Panels with Endogenous Regressors," Monash Econometrics and Business Statistics Working Papers 13/23, Monash University, Department of Econometrics and Business Statistics.
    51. Karanasos, Menelaos & Paraskevopoulos, Alexandros & Magdalinos, Anastasios & Canepa, Alessandra, 2024. "A Unified Theory for Arma Models with Varying Coefficients: One Solution Fits All," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202413, University of Turin.
    52. Richard T. Baillie & Fabio Calonaci & George Kapetanios, 2019. "Hierarchical Time Varying Estimation of a Multi Factor Asset Pricing Model," Working Papers 879, Queen Mary University of London, School of Economics and Finance.
    53. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    54. Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.
    55. Kapetanios, George & Zikes, Filip, 2018. "Time-varying Lasso," Economics Letters, Elsevier, vol. 169(C), pages 1-6.

  47. Kapetanios, George & Mitchell, James & Shin, Yongcheol, 2014. "A nonlinear panel data model of cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 179(2), pages 134-157.
    See citations under working paper version above.
  48. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2014. "Level shifts in stock returns driven by large shocks," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 41-51.

    Cited by:

    1. Evangelos Charalambakis & Yiannis Dendramis & Elias Tzavalis, 2017. "On the determinants of NPLS: lessons from Greece," Working Papers 220, Bank of Greece.
    2. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2015. "Shifts in volatility driven by large stock market shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 130-147.
    3. Chourdakis, Kyriakos & Dendramis, Yiannis & Tzavalis, Elias, 2014. "Are regime-shift sources of risk priced in the market?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 151-170.
    4. Dendramis, Y. & Tzavalis, E. & Adraktas, G., 2018. "Credit risk modelling under recessionary and financially distressed conditions," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 160-175.
    5. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    6. Mihailo Jovanović & Vladica Stojanović & Kristijan Kuk & Brankica Popović & Petar Čisar, 2022. "Asymptotic Properties and Application of GSB Process: A Case Study of the COVID-19 Dynamics in Serbia," Mathematics, MDPI, vol. 10(20), pages 1-28, October.
    7. Martin, Vance L. & Tang, Chrismin & Yao, Wenying, 2018. "News and expected returns in East Asian equity markets: The RV-GARCHM model," Journal of Asian Economics, Elsevier, vol. 57(C), pages 36-52.

  49. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.

    Cited by:

    1. Li, Han & O’Hare, Colin, 2017. "Semi-parametric extensions of the Cairns–Blake–Dowd model: A one-dimensional kernel smoothing approach," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 166-176.
    2. Arteche, Josu & Orbe, Jesus, 2016. "A bootstrap approximation for the distribution of the Local Whittle estimator," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 645-660.
    3. Uwe Hassler & Marc-Oliver Pohle, 2019. "Forecasting under Long Memory and Nonstationarity," Papers 1910.08202, arXiv.org.
    4. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    5. Arteche, Josu & Orbe, Jesus, 2017. "A strategy for optimal bandwidth selection in Local Whittle estimation," Econometrics and Statistics, Elsevier, vol. 4(C), pages 3-17.

  50. George Kapetanios & Tony Yates, 2014. "Evolving UK and US macroeconomic dynamics through the lens of a model of deterministic structural change," Empirical Economics, Springer, vol. 47(1), pages 305-345, August.
    See citations under working paper version above.
  51. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Modified information criteria and selection of long memory time series models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 116-131.

    Cited by:

    1. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    2. Dalla, Violetta, 2015. "Power transformations of absolute returns and long memory estimation," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 1-18.

  52. Georgios Chortareas & George Kapetanios, 2013. "How Puzzling Is The Ppp Puzzle? An Alternative Half‐Life Measure Of Convergence To Ppp," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 435-457, April.
    See citations under working paper version above.
  53. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.

    Cited by:

    1. Jari Hännikäinen, 2014. "Multi-step forecasting in the presence of breaks," Working Papers 1494, Tampere University, Faculty of Management and Business, Economics.
    2. Duncan, Roberto & Martínez-García, Enrique, 2019. "New perspectives on forecasting inflation in emerging market economies: An empirical assessment," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1008-1031.
    3. Jari Hännikäinen, 2016. "Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks," Working Papers 1692, Tampere University, Faculty of Management and Business, Economics.
    4. Mardi Dungey & Jan P.A.M. Jacobs & Jing Tian, 2017. "Forecasting output gaps in the G-7 countries: the role of correlated innovations and structural breaks," Applied Economics, Taylor & Francis Journals, vol. 49(45), pages 4554-4566, September.
    5. Gantungalag Altansukh & Denise R. Osborn, 2022. "Using structural break inference for forecasting time series," Empirical Economics, Springer, vol. 63(1), pages 1-41, July.

  54. Atak, Alev & Kapetanios, George, 2013. "A factor approach to realized volatility forecasting in the presence of finite jumps and cross-sectional correlation in pricing errors," Economics Letters, Elsevier, vol. 120(2), pages 224-228.

    Cited by:

    1. Till Weigt & Bernd Wilfling, 2016. "A new combination approach to reducing forecast errors with an application to volatility forecasting," CQE Working Papers 4616, Center for Quantitative Economics (CQE), University of Muenster.
    2. Vortelinos, Dimitrios I., 2016. "Incremental information of stock indicators," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 79-97.
    3. Vortelinos, Dimitrios I. & Lakshmi, Geeta, 2015. "Market risk of BRIC Eurobonds in the financial crisis period," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 295-310.
    4. E. Otranto, 2024. "A Vector Multiplicative Error Model with Spillover Effects and Co-movements," Working Paper CRENoS 202404, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    5. Giampiero M. Gallo & Edoardo Otranto, 2018. "Combining sharp and smooth transitions in volatility dynamics: a fuzzy regime approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(3), pages 549-573, April.
    6. Ji‐Eun Choi & Dong Wan Shin, 2018. "Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 691-704, September.

  55. Richard T. Baillie & George Kapetanios, 2013. "Estimation and inference for impulse response functions from univariate strongly persistent processes," Econometrics Journal, Royal Economic Society, vol. 16(3), pages 373-399, October.

    Cited by:

    1. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    2. Simone D. Grose & Gael M. Martin & D.S. Poskitt, 2014. "Bias Correction of Persistence Measures in Fractionally Integrated Models," Monash Econometrics and Business Statistics Working Papers 19/14, Monash University, Department of Econometrics and Business Statistics.
    3. Baillie Richard T. & Kapetanios George, 2016. "On the estimation of short memory components in long memory time series models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 365-375, September.
    4. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    5. Dalla, Violetta, 2015. "Power transformations of absolute returns and long memory estimation," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 1-18.
    6. Richard T. Baillie & Dooyeon Cho & Seunghwa Rho, 2023. "Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility," Empirical Economics, Springer, vol. 64(6), pages 2911-2937, June.
    7. Baillie, Richard T. & Cho, Dooyeon & Rho, Seunghwa, 2024. "Combining Long and Short Memory in Time Series Models: the Role of Asymptotic Correlations of the MLEs," Econometrics and Statistics, Elsevier, vol. 29(C), pages 88-112.
    8. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    9. Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.

  56. Jan J. J. Groen & George Kapetanios, 2013. "Model Selection Criteria for Factor-Augmented Regressions-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 37-63, February.

    Cited by:

    1. Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Jack Fosten, 2016. "Model selection with factors and variables," University of East Anglia School of Economics Working Paper Series 2016-07, School of Economics, University of East Anglia, Norwich, UK..
    3. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    4. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    5. Galbraith, John W. & Zinde-Walsh, Victoria, 2020. "Simple and reliable estimators of coefficients of interest in a model with high-dimensional confounding effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 609-632.
    6. Tu, Yundong & Wang, Siwei, 2024. "Selection inconsistency for factor-augmented regressions," Economics Letters, Elsevier, vol. 241(C).
    7. Demetrescu, Matei & Hacioglu Hoke, Sinem, 2018. "Predictive regressions under asymmetric loss: factor augmentation and model selection," Bank of England working papers 723, Bank of England.
    8. Fornaro, Paolo, 2016. "Predicting Finnish economic activity using firm-level data," International Journal of Forecasting, Elsevier, vol. 32(1), pages 10-19.
    9. Kapetanios, G & Price, SG & Young, G, 2017. "A UK financial conditions index using targeted data reduction: forecasting and structural identification," Essex Finance Centre Working Papers 20328, University of Essex, Essex Business School.
    10. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
    11. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    12. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.

  57. Jan J. J. Groen & George Kapetanios & Simon Price, 2013. "Multivariate Methods For Monitoring Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 250-274, March.
    See citations under working paper version above.
  58. Giraitis, Liudas & Kapetanios, George & Price, Simon, 2013. "Adaptive forecasting in the presence of recent and ongoing structural change," Journal of Econometrics, Elsevier, vol. 177(2), pages 153-170.
    See citations under working paper version above.
  59. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.

    Cited by:

    1. Igor Masten & Massimiliano Marcellino & Anindya Banerjeey, 2009. "Forecasting with Factor-augmented Error Correction Models," RSCAS Working Papers 2009/32, European University Institute.
    2. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 1-33.
    3. Alberto Caruso & Laura Coroneo, 2023. "Does Real‐Time Macroeconomic Information Help to Predict Interest Rates?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2027-2059, December.
    4. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    5. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    6. Florian Huber & Gregor Kastner & Martin Feldkircher, 2016. "Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models," Papers 1607.04532, arXiv.org, revised Jul 2018.
    7. Andrea Carriero & Lorenzo Ricci & Elisabetta Vangelista, 2022. "Expectations and term premia in EFSF bond yields," Working Papers 54, European Stability Mechanism.
    8. Byrne, JP & Cao, S & Korobilis, D, 2016. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," Essex Finance Centre Working Papers 18195, University of Essex, Essex Business School.
    9. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    10. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," Working Papers 20-27, Federal Reserve Bank of Cleveland.
    11. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian inference in large Vector Autoregressions with hierarchical shrinkage," CAMA Working Papers 2019-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Juan Antolin-Diaz & Ivan Petrella & Juan F. Rubio-Ramirez, 2021. "Dividend Momentum and Stock Return Predictability: A Bayesian Approach," FRB Atlanta Working Paper 2021-25, Federal Reserve Bank of Atlanta.
    13. Argyropoulos Efthymios & Tzavalis Elias, 2015. "Term spread regressions of the rational expectations hypothesis of the term structure allowing for risk premium effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 49-70, February.
    14. Koop, Gary & Korobilis, Dimitris, 2014. "A new index of financial conditions," European Economic Review, Elsevier, vol. 71(C), pages 101-116.
    15. Oskar Gustafsson & Mattias Villani & Pär Stockhammar, 2023. "Bayesian optimization of hyperparameters from noisy marginal likelihood estimates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 577-595, June.
    16. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    17. Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Adaptive hierarchical priors for high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 212(1), pages 241-271.
    18. Dimitrios P. Louzis, 2017. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs," Empirical Economics, Springer, vol. 53(2), pages 569-598, September.
    19. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    20. Guanhao Feng & Nicholas Polson, 2020. "Regularizing Bayesian predictive regressions," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 591-608, December.
    21. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    22. Joshua C.C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2018. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2018-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    23. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2018. "Directed Graphs and Variable Selection in Large Vector Autoregressive Models," Working Paper Series of the Department of Economics, University of Konstanz 2018-08, Department of Economics, University of Konstanz.
    24. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2023. "General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 69-87, January.
    25. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    26. Shevelev A.A., 2017. "Bayesian approach to evaluate the impact of external shocks on Russian macroeconomics indicators," World of economics and management / Vestnik NSU. Series: Social and Economics Sciences, Socionet, vol. 17(1), pages 26-40.
    27. Gregory R. Duffee, 2012. "Forecasting interest rates," Economics Working Paper Archive 599, The Johns Hopkins University,Department of Economics.
    28. Almeida, Caio & Ardison, Kym & Kubudi, Daniela, 2014. "Approximating Risk Premium on a Parametric Arbitrage-free Term Structure Model," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 34(2), November.
    29. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
    30. Andrea Carriero & Davide Pettenuzzo & Shubhranshu Shekhar, 2024. "Macroeconomic Forecasting with Large Language Models," Papers 2407.00890, arXiv.org.
    31. Carriero, Andrea & Clements, Michael P. & Galvão, Ana Beatriz, 2015. "Forecasting with Bayesian multivariate vintage-based VARs," International Journal of Forecasting, Elsevier, vol. 31(3), pages 757-768.
    32. Doo Won Bang & HyuckShin Kwon, 2022. "Policy Impact Analysis of Housing Policies Using Housing Cycles," SAGE Open, , vol. 12(3), pages 21582440221, July.
    33. William Gatt & Germano Ruisi, 2020. "Housing demand shocks, foreign labour inflows and consumption," CBM Working Papers WP/07/2020, Central Bank of Malta.
    34. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
    35. Sebastian Ankargren & Måns Unosson & Yukai Yang, 2018. "A mixed-frequency Bayesian vector autoregression with a steady-state prior," CREATES Research Papers 2018-32, Department of Economics and Business Economics, Aarhus University.
    36. Prüser Jan & Hanck Christoph, 2021. "A Comparison of Approaches to Select the Informativeness of Priors in BVARs," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(4), pages 501-525, August.
    37. И Управления Мир Экономики, 2017. "Байесовский подход к анализу влияния монетарной политики на макроэкономические показатели России. Bayesian approach to the analysis of monetary policy impact on Russian macroeconomics indicators," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 17(4), pages 53-70.
    38. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2023. "Directed graphs and variable selection in large vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 223-246, March.
    39. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.
    40. Danilo Leiva-Leon, 2017. "Monitoring the Spanish Economy through the Lenses of Structural Bayesian VARs," Occasional Papers 1706, Banco de España.
    41. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.
    42. Dimitris P. Louzis, 2014. "Macroeconomic and credit forecasts in a small economy during crisis: A large Bayesian VAR approach," Working Papers 184, Bank of Greece.

  60. George Kapetanios & Haroon Mumtaz & Ibrahim Stevens & Konstantinos Theodoridis, 2012. "Assessing the Economy‐wide Effects of Quantitative Easing," Economic Journal, Royal Economic Society, vol. 122(564), pages 316-347, November.
    See citations under working paper version above.
  61. Baillie, Richard T. & Kongcharoen, Chaleampong & Kapetanios, George, 2012. "Prediction from ARFIMA models: Comparisons between MLE and semiparametric estimation procedures," International Journal of Forecasting, Elsevier, vol. 28(1), pages 46-53.

    Cited by:

    1. Geoffrey Ngene & Charles Lambert & Ali Darrat, 2015. "Testing Long Memory in the Presence of Structural Breaks: An Application to Regional and National Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 50(4), pages 465-483, May.
    2. Yuan, Xiaohui & Tan, Qingxiong & Lei, Xiaohui & Yuan, Yanbin & Wu, Xiaotao, 2017. "Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine," Energy, Elsevier, vol. 129(C), pages 122-137.
    3. Pietro Murialdo & Linda Ponta & Anna Carbone, 2020. "Long-Range Dependence in Financial Markets: a Moving Average Cluster Entropy Approach," Papers 2004.14736, arXiv.org.
    4. Papailias, Fotis & Fruet Dias, Gustavo, 2015. "Forecasting long memory series subject to structural change: A two-stage approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1056-1066.
    5. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
    6. J. Eduardo Vera‐Valdés, 2020. "On long memory origins and forecast horizons," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 811-826, August.
    7. Uwe Hassler & Marc-Oliver Pohle, 2019. "Forecasting under Long Memory and Nonstationarity," Papers 1910.08202, arXiv.org.

  62. Giovanni Caggiano & George Kapetanios & Vincent Labhard, 2011. "Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(8), pages 736-752, December.
    See citations under working paper version above.
  63. Kapetanios, G. & Pesaran, M. Hashem & Yamagata, T., 2011. "Panels with non-stationary multifactor error structures," Journal of Econometrics, Elsevier, vol. 160(2), pages 326-348, February.
    See citations under working paper version above.
  64. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.
    See citations under working paper version above.
  65. Christopher Tsoukis & George Kapetanios & Joseph Pearlman, 2011. "Elusive Persistence: Wage And Price Rigidities, The New Keynesian Phillips Curve And Inflation Dynamics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(4), pages 737-768, September.

    Cited by:

    1. Thomas Hasenzagl & Filippo Pellegrino & Lucrezia Reichlin & Giovanni Ricco, 2022. "A Model of the Fed's View on Inflation," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 686-704, October.
    2. Di Bartolomeo Giovanni & Di Pietro Marco, 2015. "Intrinsic persistence of wage inflation in New Keynesian models of the business cycles," wp.comunite 0118, Department of Communication, University of Teramo.
    3. Abbas, Syed K. & Bhattacharya, Prasad Sankar & Sgro, Pasquale, 2016. "The new Keynesian Phillips curve: An update on recent empirical advances," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 378-403.
    4. Mavroeidis, Sophocles & Plagborg-Moller, Mikkel & Stock, James H., 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Scholarly Articles 22795845, Harvard University Department of Economics.
    5. PETER McADAM & ALPO WILLMAN, 2013. "Technology, Utilization, and Inflation: What Drives the New Keynesian Phillips Curve?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(8), pages 1547-1579, December.
    6. Wilko Letterie & Øivind A. Nilsen, 2022. "Pricing Behaviour and Menu Costs in Multi‐product Firms," Economica, London School of Economics and Political Science, vol. 89(355), pages 746-769, July.
    7. Jakub Mućk & Peter McAdam & Jakub Growiec, 2018. "Will The “True” Labor Share Stand Up? An Applied Survey On Labor Share Measures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 961-984, September.
    8. Thomas COUDERT, 2015. "Inflation persistence and bargained firing costs," Working Papers of LaRGE Research Center 2015-04, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.
    9. Thomas COUDERT, 2016. "A new insight on the inflation persistence: the role of severance pay," Working Papers of LaRGE Research Center 2016-06, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.

  66. George Kapetanios & Yongcheol Shin, 2011. "Testing the Null Hypothesis of Nonstationary Long Memory Against the Alternative Hypothesis of a Nonlinear Ergodic Model," Econometric Reviews, Taylor & Francis Journals, vol. 30(6), pages 620-645.

    Cited by:

    1. Luis A. Gil-Alana & Rangan Gupta & Olanrewaju I. Shittu & OlaOluwa S. Yaya, 2016. "Market Efficiency of Baltic Stock Markets: A Fractional Integration Approach," Working Papers 201617, University of Pretoria, Department of Economics.
    2. Geoffrey Poitras & John Heaney, 2015. "Classical Ergodicity and Modern Portfolio Theory," Post-Print hal-03680380, HAL.
    3. Goodness C. Aye & Luis A. Gil-Alana & Rangan Gupta & Mark Wohar, 2016. "The Efficiency of the Art Market: Evidence from Variance Ratio Tests, Linear and Nonlinear Fractional Integration Approaches," Working Papers 201610, University of Pretoria, Department of Economics.
    4. Madhavi Latha Challa & Venkataramanaiah Malepati & Siva Nageswara Rao Kolusu, 2020. "S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-19, December.
    5. Poitras, Geoffrey, 2018. "The pre-history of econophysics and the history of economics: Boltzmann versus the marginalists," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 89-98.
    6. Arturo Leccadito & Omar Rachedi & Giovanni Urga, 2015. "True Versus Spurious Long Memory: Some Theoretical Results and a Monte Carlo Comparison," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 452-479, April.
    7. Tolga Omay & Ayşegül Çorakcı & Furkan Emirmahmutoglu, 2017. "Real interest rates: nonlinearity and structural breaks," Empirical Economics, Springer, vol. 52(1), pages 283-307, February.

  67. Kapetanios, George & Marcellino, Massimiliano, 2010. "Cross-sectional averaging and instrumental variable estimation with many weak instruments," Economics Letters, Elsevier, vol. 108(1), pages 36-39, July.
    See citations under working paper version above.
  68. Kapetanios, George, 2010. "Testing For Exogeneity In Threshold Models," Econometric Theory, Cambridge University Press, vol. 26(1), pages 231-259, February.

    Cited by:

    1. Myung Hwan Seo & Yongcheol Shin, 2014. "Dynamic Panels with Threshold Effect and Endogeneity," STICERD - Econometrics Paper Series 577, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Massacci, Daniele, 2013. "A variable addition test for exogeneity in structural threshold models," Economics Letters, Elsevier, vol. 120(1), pages 5-9.
    3. Geraci Andrea & Fabbri Daniele & Monfardini Chiara, 2018. "Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two-stage Residual Inclusion Work?," Journal of Econometric Methods, De Gruyter, vol. 7(1), pages 1-19, January.
    4. Massacci, Daniele, 2012. "A simple test for linearity against exponential smooth transition models with endogenous variables," Economics Letters, Elsevier, vol. 117(3), pages 851-856.
    5. Ping Yu & Peter C.B. Phillips, 2014. "Threshold Regression with Endogeneity," Cowles Foundation Discussion Papers 1966, Cowles Foundation for Research in Economics, Yale University.
    6. Christopoulos, Dimitris & McAdam, Peter & Tzavalis, Elias, 2018. "Dealing with endogeneity in threshold models using copulas: an illustration to the foreign trade multiplier," Working Paper Series 2136, European Central Bank.

  69. George Kapetanios & Tony Yates, 2010. "Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 869-893.

    Cited by:

    1. Valentina Raponi & Cecilia Frale, 2014. "Revisions in official data and forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 451-472, August.
    2. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    3. Marek RUSNAK, 2013. "Revisions to the Czech National Accounts: Properties and Predictability," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(3), pages 244-261, July.

  70. Kapetanios, George & Blake, Andrew P., 2010. "Tests Of The Martingale Difference Hypothesis Using Boosting And Rbf Neural Network Approximations," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1363-1397, October.

    Cited by:

    1. Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023. "Forecasting Value-at-Risk using deep neural network quantile regression," Essex Finance Centre Working Papers 34837, University of Essex, Essex Business School.
    2. Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023. "Deep Neural Network Estimation in Panel Data Models," Papers 2305.19921, arXiv.org.
    3. Weiwei Liu & Zhile Yang & Kexin Bi, 2017. "Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach," Complexity, Hindawi, vol. 2017, pages 1-8, October.
    4. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    5. Lee Jinu, 2019. "A Neural Network Method for Nonlinear Time Series Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-18, January.

  71. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
    See citations under working paper version above.
  72. Kapetanios, George, 2010. "A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 397-409.
    See citations under working paper version above.
  73. Kapetanios, G. & Tzavalis, E., 2010. "Modeling structural breaks in economic relationships using large shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 417-436, March.

    Cited by:

    1. Polemis, Michael L. & Fotis, Panagiotis N., 2014. "The taxation effect on gasoline price asymmetry nexus: Evidence from both sides of the Atlantic," Energy Policy, Elsevier, vol. 73(C), pages 225-233.
    2. Marcellino, Massimiliano & Kapetanios, George & Giraitis, Liudas, 2020. "Time-Varying Instrumental Variable Estimation," CEPR Discussion Papers 15210, C.E.P.R. Discussion Papers.
    3. Juan L. Eugenio-Martin, 2016. "Estimating the Tourism Demand Impact of Public Infrastructure Investment: The Case of Malaga Airport Expansion," Tourism Economics, , vol. 22(2), pages 254-268, April.
    4. Sarah Arndt & Zeno Enders, 2023. "The Transmission of Supply Shocks in Different Inflation Regimes," CESifo Working Paper Series 10839, CESifo.
    5. Managi, Shunsuke & Managi, Shunsuke & Okimoto, Tatsuyoshi, 2013. "Does the price of oil interact with clean energy prices in the stock market?," MPRA Paper 46067, University Library of Munich, Germany.
    6. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    7. George Kapetanios & Tony Yates, 2014. "Evolving UK and US macroeconomic dynamics through the lens of a model of deterministic structural change," Empirical Economics, Springer, vol. 47(1), pages 305-345, August.
    8. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    9. Patricia Chelley‐Steeley & Neophytos Lambertides & Christos S. Savva, 2019. "Sentiment, order imbalance, and co‐movement: An examination of shocks to retail and institutional trading activity," European Financial Management, European Financial Management Association, vol. 25(1), pages 116-159, January.
    10. Arndt, Sarah & Enders, Zeno, 2023. "Shock Transmissions in Different Inflation Regimes," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277682, Verein für Socialpolitik / German Economic Association.
    11. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2014. "Level shifts in stock returns driven by large shocks," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 41-51.
    12. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2015. "Shifts in volatility driven by large stock market shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 130-147.
    13. Tsuji, Chikashi, 2018. "Return transmission and asymmetric volatility spillovers between oil futures and oil equities: New DCC-MEGARCH analyses," Economic Modelling, Elsevier, vol. 74(C), pages 167-185.
    14. Dennis Nchor & Václav Klepáč & Václav Adamec, 2016. "Effects of Oil Price Shocks on the Ghanaian Economy," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 64(1), pages 315-324.
    15. Giraitis, L. & Kapetanios, G. & Yates, T., 2014. "Inference on stochastic time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 179(1), pages 46-65.
    16. Polemis, Michael L. & Tsionas, Mike G., 2016. "An alternative semiparametric approach to the modelling of asymmetric gasoline price adjustment," Energy Economics, Elsevier, vol. 56(C), pages 384-388.
    17. Jana Eklund & George Kapetanios & Simon Price, 2011. "Forecasting in the presence of recent structural change," CAMA Working Papers 2011-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Ioannidis, Christos & Ka, Kook, 2018. "The impact of oil price shocks on the term structure of interest rates," Energy Economics, Elsevier, vol. 72(C), pages 601-620.
    19. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.
    20. Dagoumas, Athanasios S. & Polemis, Michael L. & Soursou, Symeoni-Eleni, 2020. "Revisiting the impact of energy prices on economic growth: Lessons learned from the European Union," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 85-95.
    21. Chelley-Steeley, Patricia & Lambertides, Neophytos & Savva, Christos S., 2013. "Illiquidity shocks and the comovement between stocks: New evidence using smooth transition," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 1-15.
    22. Michael Fotiadis & Michael L Polemis, 2018. "The Role of Sustainability‐Related Strategies on the Biofuel Industry: Trends, Prospects and Challenges," Business Strategy and the Environment, Wiley Blackwell, vol. 27(6), pages 757-772, September.
    23. Ana Gómez-Loscos & Antonio Montañes & Maria Dolores Gadea, 2011. "The impact of oil shocks on the Spanish economy," ERSA conference papers ersa10p835, European Regional Science Association.
    24. Maria Dolores Gadea & Ana Gómez-Loscos & Antonio Montañes, 2011. "ECONOMIC GROWTH, INFLATION AND OIL SHOCKS: ARE THE 1970s COMING BACK?," Post-Print hal-00720578, HAL.
    25. Alisa Yusupova & Nicos G. Pavlidis & Efthymios G. Pavlidis, 2019. "Adaptive Dynamic Model Averaging with an Application to House Price Forecasting," Papers 1912.04661, arXiv.org.
    26. Fukuda, Kosei, 2012. "Illustrating extraordinary shocks causing trend breaks," Economic Modelling, Elsevier, vol. 29(4), pages 1045-1052.
    27. Mihailo Jovanović & Vladica Stojanović & Kristijan Kuk & Brankica Popović & Petar Čisar, 2022. "Asymptotic Properties and Application of GSB Process: A Case Study of the COVID-19 Dynamics in Serbia," Mathematics, MDPI, vol. 10(20), pages 1-28, October.
    28. Chelley-Steeley, Patricia & Lambertides, Neophytos & Savva, Christos S., 2015. "The effect of security and market order flow shocks on co-movement," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 136-155.
    29. Hahn, Warren J. & DiLellio, James A. & Dyer, James S., 2014. "What do market-calibrated stochastic processes indicate about the long-term price of crude oil?," Energy Economics, Elsevier, vol. 44(C), pages 212-221.

  74. Cipollini, A. & Kapetanios, G., 2009. "Forecasting financial crises and contagion in Asia using dynamic factor analysis," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 188-200, March.
    See citations under working paper version above.
  75. George Kapetanios & Massimiliano Marcellino, 2009. "A parametric estimation method for dynamic factor models of large dimensions," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 208-238, March.
    See citations under working paper version above.
  76. Gonzalo Camba-Mendez & George Kapetanios, 2009. "Statistical Tests and Estimators of the Rank of a Matrix and Their Applications in Econometric Modelling," Econometric Reviews, Taylor & Francis Journals, vol. 28(6), pages 581-611.
    See citations under working paper version above.
  77. Carriero, A. & Kapetanios, G. & Marcellino, M., 2009. "Forecasting exchange rates with a large Bayesian VAR," International Journal of Forecasting, Elsevier, vol. 25(2), pages 400-417.
    See citations under working paper version above.
  78. Alastair Cunningham & Jana Eklund & Chris Jeffery & George Kapetanios & Vincent Labhard, 2009. "A State Space Approach to Extracting the Signal From Uncertain Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 173-180, March.
    See citations under working paper version above.
  79. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.

    Cited by:

    1. João Henrique Gonçalves Mazzeu & Esther Ruiz & Helena Veiga, 2018. "Uncertainty And Density Forecasts Of Arma Models: Comparison Of Asymptotic, Bayesian, And Bootstrap Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 388-419, April.
    2. Jacob T. Jones & Tara M. Sinclair & Herman O. Stekler, 2018. "A Textual Analysis of the Bank of England Growth Forecasts," Working Papers 2018-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised May 2019.
    3. Zeyyad Mandalinci, 2015. "Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models," CReMFi Discussion Papers 3, CReMFi, School of Economics and Finance, QMUL.
    4. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    5. Pablo M. Pincheira & Carlos A. Medel, 2016. "Forecasting with a Random Walk," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 539-564, December.
    6. Jan J. J. Groen & George Kapetanios & Simon Price, 2013. "Multivariate Methods For Monitoring Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 250-274, March.
    7. Medel, Carlos A., 2017. "Forecasting Chilean Inflation with the Hybrid New Keynesian Phillips Curve: Globalisation, Combination, and Accuracy," MPRA Paper 78439, University Library of Munich, Germany.
    8. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, C.E.P.R. Discussion Papers.
    9. Medel, Carlos A., 2015. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," MPRA Paper 67081, University Library of Munich, Germany.
    10. Malte Knüppel & Guido Schultefrankenfeld, 2017. "Interest rate assumptions and predictive accuracy of central bank forecasts," Empirical Economics, Springer, vol. 53(1), pages 195-215, August.
    11. Pablo Pincheira, 2011. "A Bunch of Models, a Bunch of Nulls and Inference About Predictive Ability," Working Papers Central Bank of Chile 607, Central Bank of Chile.
    12. Roberto Leon-Gonzalez & Blessings Majoni, 2023. "Exact Likelihood for Inverse Gamma Stochastic Volatility Models," Working Paper series 23-11, Rimini Centre for Economic Analysis.
    13. Tara Sinclair & Herman O. Stekler & Warren Carrow, 2012. "Evaluating a Vector of the Fed's Forecasts," Working Papers 2012-3, The George Washington University, Institute for International Economic Policy.
    14. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    15. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    16. Sinclair, Tara M. & Stekler, H.O., 2013. "Examining the quality of early GDP component estimates," International Journal of Forecasting, Elsevier, vol. 29(4), pages 736-750.
    17. Michael Cai & Marco Del Negro & Marc Giannoni & Abhi Gupta & Pearl Li & Erica Moszkowski, 2018. "DSGE forecasts of the lost recovery," Staff Reports 844, Federal Reserve Bank of New York.
    18. Andriantomanga, Zo, 2023. "The role of survey-based expectations in real-time forecasting of US inflation," MPRA Paper 119904, University Library of Munich, Germany.
    19. Ana Beatriz Galvão & James Mitchell, 2023. "Real‐Time Perceptions of Historical GDP Data Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 457-481, June.
    20. Pablo Pincheira & Carlos A. Medel, 2012. "Forecasting Inflation with a Simple and Accurate Benchmark: a Cross-Country Analysis," Working Papers Central Bank of Chile 677, Central Bank of Chile.
    21. Söderström, Ulf & Iversen, Jens & LASEEN, PER & Lundvall, Henrik, 2016. "Real-Time Forecasting for Monetary Policy Analysis: The Case of Sveriges Riksbank," CEPR Discussion Papers 11203, C.E.P.R. Discussion Papers.
    22. Pablo Pincheira Brown & Álvaro García Marín, 2009. "Forecasting Inflation in Chile With an Accurate Benchmark," Working Papers Central Bank of Chile 514, Central Bank of Chile.
    23. Papavangjeli, Meri & Rama, Arlind, 2018. "A statistical evaluation of GAP's forecasting performance for the Albanian economy," MPRA Paper 116104, University Library of Munich, Germany.
    24. Mr. Jens R Clausen & Bianca Clausen, 2010. "Simulating Inflation Forecasting in Real-Time: How Useful Is a Simple Phillips Curve in Germany, the UK, and the US?," IMF Working Papers 2010/052, International Monetary Fund.
    25. Tara Sinclair & Herman O. Stekler & Warren Carnow, 2012. "A New Approach For Evaluating Economic Forecasts," Working Papers 2012-2, The George Washington University, Institute for International Economic Policy.
    26. Anh Dinh Minh Nguyen, 2017. "U.K. Monetary Policy under Inflation Targeting," Bank of Lithuania Working Paper Series 41, Bank of Lithuania.
    27. Julien Champagne & Guillaume Poulin-Bellisle & Rodrigo Sekkel, 2018. "Evaluating the Bank of Canada Staff Economic Projections Using a New Database of Real-Time Data and Forecasts," Staff Working Papers 18-52, Bank of Canada.
    28. Pablo Pincheira, 2012. "A Joint Test of Superior Predictive Ability for Chilean Inflation Forecasts," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 15(3), pages 04-39, December.
    29. Kapetanios, G & Price, SG & Young, G, 2017. "A UK financial conditions index using targeted data reduction: forecasting and structural identification," Essex Finance Centre Working Papers 20328, University of Essex, Essex Business School.
    30. Hans Christian Müller-Dröge & Tara M. Sinclair & Herman O. Stekler, 2014. "Evaluating Forecasts Of A Vector Of Variables: A German Forecasting Competition," Working Papers 2014-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    31. Matei Demetrescu & Christoph Hanck & Robinson Kruse, 2016. "Fixed-b Inference in the Presence of Time-Varying Volatility," CREATES Research Papers 2016-01, Department of Economics and Business Economics, Aarhus University.
    32. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    33. Pablo Pincheira, 2010. "A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts," Working Papers Central Bank of Chile 556, Central Bank of Chile.
    34. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    35. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.
    36. Susan Sunila Sharma, 2019. "WHICH VARIABLES PREDICT INDONESIA’s INFLATION?," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 22(1), pages 87-102, April.
    37. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F., 2011. "Scoring rules and survey density forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 379-393, April.
    38. Pablo M. Pincheira & Carlos A. Medel, 2015. "Forecasting Inflation with a Simple and Accurate Benchmark: The Case of the US and a Set of Inflation Targeting Countries," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(1), pages 2-29, January.
    39. Tara M. Sinclair & H.O. Stekler, 2011. "Differences in Early GDP Component Estimates Between Recession and Expansion," Working Papers 2011-05, The George Washington University, Institute for International Economic Policy.
    40. Tsuchiya, Yoichi, 2022. "Evaluating the European Central Bank’s uncertainty forecasts," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 321-330.
    41. Pablo Pincheira & Carlos Medel, 2012. "Forecasting Inflation With a Random Walk," Working Papers Central Bank of Chile 669, Central Bank of Chile.
    42. Hamid Baghestani & Cassia Marchon, 2015. "On the accuracy of private forecasts of inflation and growth in Brazil," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(2), pages 370-381, April.

  80. Kapetanios, George, 2009. "Testing for strict stationarity in financial variables," Journal of Banking & Finance, Elsevier, vol. 33(12), pages 2346-2362, December.

    Cited by:

    1. Zacharias Psaradakis & Marián Vávra, 2019. "Portmanteau tests for linearity of stationary time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 248-262, February.
    2. George Kapetanios & Fotis Papailias, 2011. "Block Bootstrap and Long Memory," Working Papers 679, Queen Mary University of London, School of Economics and Finance.
    3. Lee, Sangyeol & Meintanis, Simos G. & Pretorius, Charl, 2022. "Monitoring procedures for strict stationarity based on the multivariate characteristic function," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    4. Zacharias Psaradakis & Marián Vávra, 2015. "A Quantile-based Test for Symmetry of Weakly Dependent Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(4), pages 587-598, July.
    5. Schäfer, Rudi & Guhr, Thomas, 2010. "Local normalization: Uncovering correlations in non-stationary financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3856-3865.
    6. Wang, Zijun, 2010. "Dynamics and causality in industry-specific volatility," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1688-1699, July.
    7. Dichtl, Hubert & Drobetz, Wolfgang, 2011. "Portfolio insurance and prospect theory investors: Popularity and optimal design of capital protected financial products," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1683-1697, July.

  81. Chortareas, Georgios & Kapetanios, George, 2009. "Getting PPP right: Identifying mean-reverting real exchange rates in panels," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 390-404, February.
    See citations under working paper version above.
  82. G. Kapetanios, 2008. "A bootstrap procedure for panel data sets with many cross-sectional units," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 377-395, July.
    See citations under working paper version above.
  83. Baillie, Richard T. & Kapetanios, George, 2008. "Nonlinear models for strongly dependent processes with financial applications," Journal of Econometrics, Elsevier, vol. 147(1), pages 60-71, November.

    Cited by:

    1. Hassler, Uwe, 2012. "Impulse responses of antipersistent processes," Economics Letters, Elsevier, vol. 116(3), pages 454-456.
    2. Vozlyublennaia, Nadia, 2011. "The cross-section of dynamics in idiosyncratic risk," Journal of Empirical Finance, Elsevier, vol. 18(3), pages 461-473, June.
    3. Hassler, Uwe & Meller, Barbara, 2011. "Detecting multiple breaks in long memory: The case of US inflation," Discussion Paper Series 1: Economic Studies 2011,26, Deutsche Bundesbank.
    4. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    5. Eric Hillebrand & Marcelo C. Medeiros, 2016. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 23-41, January.
    6. Manabu Asai & Michael McAleer, 2013. "A Fractionally Integrated Wishart Stochastic Volatility Model," Tinbergen Institute Discussion Papers 13-025/III, Tinbergen Institute.
    7. Baillie Richard T. & Kapetanios George, 2016. "On the estimation of short memory components in long memory time series models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 365-375, September.
    8. Busch, Ulrike & Nautz, Dieter, 2009. "Controllability and persistence of money Market rates along the yield curve: Evidence from the Euro area," SFB 649 Discussion Papers 2009-029, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Modified information criteria and selection of long memory time series models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 116-131.
    10. Cho, Dooyeon, 2018. "On the persistence of the forward premium in the joint presence of nonlinearity, asymmetry, and structural changes," Economic Modelling, Elsevier, vol. 70(C), pages 310-319.
    11. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    12. Chatzikonstanti, Vasiliki & Venetis, Ioannis A., 2015. "Long memory in log-range series: Do structural breaks matter?," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 104-113.
    13. Richard T. Baillie & Dooyeon Cho & Seunghwa Rho, 2023. "Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility," Empirical Economics, Springer, vol. 64(6), pages 2911-2937, June.
    14. Karanasos, M. & Koutroumpis, P. & Karavias, Y. & Kartsaklas, A. & Arakelian, V., 2016. "Inflation convergence in the EMU," Journal of Empirical Finance, Elsevier, vol. 39(PB), pages 241-253.
    15. Baillie, Richard T. & Cho, Dooyeon & Rho, Seunghwa, 2024. "Combining Long and Short Memory in Time Series Models: the Role of Asymptotic Correlations of the MLEs," Econometrics and Statistics, Elsevier, vol. 29(C), pages 88-112.
    16. Baillie, Richard T. & Kongcharoen, Chaleampong & Kapetanios, George, 2012. "Prediction from ARFIMA models: Comparisons between MLE and semiparametric estimation procedures," International Journal of Forecasting, Elsevier, vol. 28(1), pages 46-53.
    17. Richard T. Baillie & Fabio Calonaci & Dooyeon Cho & Seunghwa Rho, 2019. "Long Memory, Realized Volatility and HAR Models," Working Papers 881, Queen Mary University of London, School of Economics and Finance.

  84. Georgios Chortareas & George Kapetanios & Merih Uctum, 2008. "Nonlinear Alternatives to Unit Root Tests and Public Finances Sustainability: Some Evidence from Latin American and Caribbean Countries," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(5), pages 645-663, October.

    Cited by:

    1. Juan Carlos Cuestas, 2016. "Changes in sovereign debt dynamics in Central and Eastern Europe," Working Papers 16-10, Asociación Española de Economía y Finanzas Internacionales.
    2. Piergallini, Alessandro & Postigliola, Michele, 2013. "Non-Linear Budgetary Policies: Evidence from 150 Years of Italian Public Finance," MPRA Paper 48922, University Library of Munich, Germany.
    3. Phiri, Andrew, 2018. "How sustainable are fiscal budgets in the Kingdom of Swaziland?," MPRA Paper 85149, University Library of Munich, Germany.
    4. Piergallini, Alessandro, 2012. "Non-Linear Fiscal Regimes and Interest Rate Policy," MPRA Paper 42671, University Library of Munich, Germany.
    5. Ehrhart Christophe & Matthieu Llorca, 2017. "Fiscal Sustainability in Central and Latin America Countries: Evidence from a Panel Cointegration Approach," Economics Bulletin, AccessEcon, vol. 37(4), pages 2292-2300.
    6. Abderrahim Chibi & Sidi Mohamed Chekouri & Mohamed Benbouziane, 2019. "The dynamics of fiscal policy in Algeria: sustainability and structural change," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-27, December.
    7. Ruthira Naraidoo & Leroi Raputsoane, 2015. "Debt Sustainability and Financial Crises in South Africa," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(1), pages 224-233, January.
    8. Vasudeva N. R. Murthy & Emmanuel Anoruo, 2009. "Are Per Capita Real GDP Series in African Countries Non-stationary or Non-linear? What does Empirical Evidence Reveal?," Economics Bulletin, AccessEcon, vol. 29(4), pages 2492-2504.
    9. Gabriella Legrenzi & Costas Milas, 2011. "Debt Sustainability and Financial Crises: Evidence from the GIIPS," Working Paper series 42_11, Rimini Centre for Economic Analysis.
    10. Jean-Claude Kouakou Brou & Jamal Bouoiyour, 2023. "South Africa's Public Debt: Long-term Dependence, Structural Breaks and Multifractality [La dette publique de l'Afrique du Sud : dépendance à long terme, ruptures structurelles et multifractalité]," Post-Print hal-04327950, HAL.
    11. Cascio, Iolanda Lo, 2015. "A wavelet analysis of US fiscal sustainability," Economic Modelling, Elsevier, vol. 51(C), pages 33-37.
    12. Piergallini, Alessandro, 2018. "Nonlinear Policy Behavior, Multiple Equilibria and Debt-Deflation Attractors," MPRA Paper 88336, University Library of Munich, Germany.
    13. Canofari, Paolo & Marini, Giancarlo & Piergallini, Alessandro, 2020. "Financial Crisis and Sustainability of US Fiscal Deficit: Indicators or Tests?," Journal of Policy Modeling, Elsevier, vol. 42(1), pages 192-204.
    14. Yoon, Gawon, 2012. "War and peace: Explosive U.S. public debt, 1791–2009," Economics Letters, Elsevier, vol. 115(1), pages 1-3.
    15. Abderrahim Chibi & Sidi Mohamed Chekouri & Mohamed Benbouziane, 2015. "Assessing Fiscal Sustainability in Algeria: a Nonlinear Approach," Working Papers 962, Economic Research Forum, revised Oct 2015.
    16. Vicente Esteve & María A. Prats, 2021. "Financial bubbles and sustainability of public debt: The case of Spain," Working Papers 2111, Department of Applied Economics II, Universidad de Valencia.
    17. Gabriella Legrenzi & Costas Milas, 2012. "Long-Run Debt Sustainability and Threshold Adjustments: Non-Linear Empirical Evidence from the GIIPS," Economics Bulletin, AccessEcon, vol. 32(3), pages 2586-2593.
    18. Tilak Abeysinghe & Ananda Jayawickrama, 2013. "A segmented trend model to assess fiscal sustainability: The US experience 1929–2009," Empirical Economics, Springer, vol. 44(3), pages 1129-1141, June.
    19. J. Stephen Ferris & Stanley L. Winer & Bernard Grofman, 2011. "Do Departures from Democratic Accountability Compromise the Stability of Public Finances? Keynesianism, Central Banking, and Minority Governments in the Canadian System of Party Government, 1867 – 200," Carleton Economic Papers 11-09, Carleton University, Department of Economics.
    20. Christophe Ehrhart & Matthieu Llorca, 2017. "Fiscal sustainability in Central and Latin American countries : evidence from a panel cointegration approach," Post-Print hal-04704604, HAL.
    21. Vladimir Andric & Dusko Bodroza & Mihajlo Djukic, 2024. "A Commentary on US Sovereign Debt Persistence and Nonlinear Fiscal Adjustment," Mathematics, MDPI, vol. 12(20), pages 1-33, October.
    22. Thanh Dat Nguyen & Sandy Suardi & Chew Lian Chua, 2017. "The Behavior Of U.S. Public Debt And Deficits During The Global Financial Crisis," Contemporary Economic Policy, Western Economic Association International, vol. 35(1), pages 201-215, January.
    23. Yoon, Gawon, 2012. "Explosive U.S. budget deficit," Economic Modelling, Elsevier, vol. 29(4), pages 1076-1080.
    24. Gabriella Deborah Legrenzi & Costas Milas, 2010. "Spend-and-Tax Adjustments and the Sustainability of the Government's Intertemporal Budget Constraint," CESifo Working Paper Series 2926, CESifo.

  85. Kapetanios, George & Shin, Yongcheol, 2008. "GLS detrending-based unit root tests in nonlinear STAR and SETAR models," Economics Letters, Elsevier, vol. 100(3), pages 377-380, September.

    Cited by:

    1. Michael Frömmel & Darko B. Vukovic & Jinyuan Wu, 2022. "The Dollar Exchange Rate, Adjustment to the Purchasing Power Parity, and the Interest Rate Differential," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    2. S. M. Woahid Murad & Ruhul Salim & Md. Golam Kibria, 2021. "Asymmetric Effects of Economic Policy Uncertainty on the Demand for Money in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(3), pages 451-470, September.
    3. Suleman Sarwar & Rida Waheed & Ghazala Aziz & Simona Andreea Apostu, 2022. "The Nexus of Energy, Green Economy, Blue Economy, and Carbon Neutrality Targets," Energies, MDPI, vol. 15(18), pages 1-19, September.
    4. Mishra, Bibhuti Ranjan & Pradhan, Ashis Kumar & Tiwari, Aviral Kumar & Shahbaz, Muhammad, 2019. "The dynamic causality between gold and silver prices in India: Evidence using time-varying and non-linear approaches," Resources Policy, Elsevier, vol. 62(C), pages 66-76.
    5. Hanck, Christoph, 2012. "On the asymptotic distribution of a unit root test against ESTAR alternatives," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 360-364.
    6. Niels Haldrup & Robinson Kruse & Timo Teräsvirta & Rasmus T. Varneskov, 2013. "Unit roots, non-linearities and structural breaks," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 4, pages 61-94, Edward Elgar Publishing.
    7. Mohammad Ajmal Hameed & Mohammad Mafizur Rahman & Rasheda Khanam, 2024. "Asymmetric effects of long-term war on human resource development in Af…ghanistan: evidence from NARDL approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4807-4832, October.
    8. A Ghoshray & Ashira Perera, 2011. "The Model of Commodity Prices after Sir Arthur Lewis Revisited," Department of Economics Working Papers 08/11, University of Bath, Department of Economics.
    9. Cosimo Magazzino & Mihai Mutascu, 2019. "A wavelet analysis of Italian fiscal sustainability," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-13, December.
    10. Neil Lawton & Liam A. Gallagher, 2020. "The negative side of inflation targeting: revisiting inflation uncertainty in the EMU," Applied Economics, Taylor & Francis Journals, vol. 52(29), pages 3186-3203, June.
    11. Yanglin Li, 2024. "New Unit Root Tests in the Nonlinear ESTAR Framework: The Movement and Volatility Characteristics of Crude oil and Copper Prices," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1757-1776, May.
    12. Sandberg, Rickard, 2016. "Trends, unit roots, structural changes, and time-varying asymmetries in U.S. macroeconomic data: the Stock and Watson data re-examined," Economic Modelling, Elsevier, vol. 52(PB), pages 699-713.
    13. Artur Silva Lopes & Gabriel Florin Zsurkis, 2019. "Are linear models really unuseful to describe business cycle data?," Applied Economics, Taylor & Francis Journals, vol. 51(22), pages 2355-2376, May.
    14. Sauveur Giannoni & Juan M. Hernández & Jorge V. Pérez-Rodríguez, 2020. "Economic growth and market segment choice in tourism-based economies," Empirical Economics, Springer, vol. 59(3), pages 1435-1452, September.
    15. Aaron D. Smallwood, 2016. "A Monte Carlo Investigation of Unit Root Tests and Long Memory in Detecting Mean Reversion in I(0) Regime Switching, Structural Break, and Nonlinear Data," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 986-1012, June.
    16. Oryani, Bahareh & Moridian, Ali & Sarkar, Biswajit & Rezania, Shahabaldin & Kamyab, Hesam & Khan, Muhammad Kamran, 2022. "Assessing the financial rеsоurсе curse hypothesis in Iran: Thе nоvеl dynаmiс АRDL approach," Resources Policy, Elsevier, vol. 78(C).
    17. Silva Lopes, Artur C. & Florin Zsurkis, Gabriel, 2015. "Revisiting non-linearities in business cycles around the world," MPRA Paper 65668, University Library of Munich, Germany.
    18. Awaworyi Churchill, Sefa & Inekwe, John & Ivanovski, Kris & Smyth, Russell, 2023. "Human capital and energy consumption: Six centuries of evidence from the United Kingdom," Energy Economics, Elsevier, vol. 117(C).
    19. Magazzino, Cosimo & Mutascu, Mihai Ioan, 2022. "The Italian fiscal sustainability in a long-run perspective," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    20. E Pavlidis & I Paya & D Peel, 2009. "Forecasting the Real Exchange Rate using a Long Span of Data. A Rematch: Linear vs Nonlinear," Working Papers 601190, Lancaster University Management School, Economics Department.
    21. Atanu Ghoshray & Ashira Perera, 2016. "An Empirical Study of Commodity Prices after Sir Arthur Lewis," Manchester School, University of Manchester, vol. 84(4), pages 551-571, July.
    22. Lopes, Artur Silva & Zsurkis, Gabriel Florin, 2017. "Are linear models really unuseful to describe business cycle data?," Economics Discussion Papers 2017-5, Kiel Institute for the World Economy (IfW Kiel).
    23. Awaworyi Churchill, Sefa & Inekwe, John & Ivanovski, Kris & Smyth, Russell, 2019. "Dynamics of oil price, precious metal prices and the exchange rate in the long-run," Energy Economics, Elsevier, vol. 84(C).

  86. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
    See citations under working paper version above.
  87. Cipollini, A. & Kapetanios, G., 2008. "A stochastic variance factor model for large datasets and an application to S&P data," Economics Letters, Elsevier, vol. 100(1), pages 130-134, July.
    See citations under working paper version above.
  88. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 33-41, January.
    See citations under working paper version above.
  89. Eklund, Jana & Kapetanios, George, 2008. "A review of forecasting techniques for large datasets," National Institute Economic Review, National Institute of Economic and Social Research, vol. 203, pages 109-115, January.
    See citations under working paper version above.
  90. Kapetanios, George, 2008. "Bootstrap-based tests for deterministic time-varying coefficients in regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 534-545, December.

    Cited by:

    1. Emanuela Ciapanna & Marco Taboga, 2019. "Bayesian Analysis of Coefficient Instability in Dynamic Regressions," Econometrics, MDPI, vol. 7(3), pages 1-32, June.
    2. Marina Friedrich & Eric Beutner & Hanno Reuvers & Stephan Smeekes & Jean-Pierre Urbain & Whitney Bader & Bruno Franco & Bernard Lejeune & Emmanuel Mahieu, 2020. "A statistical analysis of time trends in atmospheric ethane," Climatic Change, Springer, vol. 162(1), pages 105-125, September.
    3. Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).

  91. Baillie, Richard T. & Kapetanios, George, 2007. "Testing for Neglected Nonlinearity in Long-Memory Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 447-461, October.
    See citations under working paper version above.
  92. Andrew P. Blake & George Kapetanios, 2007. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 807-826, November.
    See citations under working paper version above.
  93. Blake, Andrew P. & Kapetanios, George, 2007. "Testing for ARCH in the presence of nonlinearity of unknown form in the conditional mean," Journal of Econometrics, Elsevier, vol. 137(2), pages 472-488, April.
    See citations under working paper version above.
  94. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.

    Cited by:

    1. Ivan Savin & Peter Winker, 2010. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection. Application on the Russian Innovative Performance," Working Papers 027, COMISEF.
    2. Seya, Hajime & Yamagata, Yoshiki & Tsutsumi, Morito, 2013. "Automatic selection of a spatial weight matrix in spatial econometrics: Application to a spatial hedonic approach," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 429-444.
    3. Andreas Sachs & Frauke Schleer, 2013. "Labour Market Performance in OECD Countries: A Comprehensive Empirical Modelling Approach of Institutional Interdependencies. WWWforEurope Working Paper No. 7," WIFO Studies, WIFO, number 46851.
    4. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
    5. Ivan Savin, 2010. "A comparative study of the Lasso-type and heuristic model selection methods," Working Papers 042, COMISEF.
    6. Sachs, Andreas & Schleer, Frauke, 2013. "Labour market performance in OECD countries: A comprehensive empirical modelling approach of institutional interdependencies," ZEW Discussion Papers 13-040, ZEW - Leibniz Centre for European Economic Research.
    7. Andreas Sachs & Frauke Schleer, 2019. "Labor Market Performance in OECD Countries: The Role of Institutional Interdependencies," International Economic Journal, Taylor & Francis Journals, vol. 33(3), pages 431-454, July.
    8. Yang, Guijun & Wang, Zhigang & Deng, Wei, 2010. "Unbiased generalized quasi-regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 779-789, March.
    9. Paroli, Roberta & Spezia, Luigi, 2008. "Bayesian inference in non-homogeneous Markov mixtures of periodic autoregressions with state-dependent exogenous variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2311-2330, January.
    10. Johnson, Lorne D. & Sakoulis, Georgios, 2008. "Maximizing equity market sector predictability in a Bayesian time-varying parameter model," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3083-3106, February.
    11. Zak-Szatkowska, Malgorzata & Bogdan, Malgorzata, 2011. "Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2908-2924, November.
    12. Fouskakis, D., 2012. "Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods," European Journal of Operational Research, Elsevier, vol. 220(2), pages 414-422.
    13. Gilli, Manfred & Winker, Peter, 2007. "2nd Special Issue on Applications of Optimization Heuristics to Estimation and Modelling Problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 2-3, September.
    14. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    15. Reynès, Christelle & Sabatier, Robert & Molinari, Nicolas & Lehmann, Sylvain, 2008. "A new genetic algorithm in proteomics: Feature selection for SELDI-TOF data," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4380-4394, May.
    16. Manfred GILLI & Peter WINKER, 2008. "A review of heuristic optimization methods in econometrics," Swiss Finance Institute Research Paper Series 08-12, Swiss Finance Institute.
    17. Pendharkar, Parag C., 2008. "Maximum entropy and least square error minimizing procedures for estimating missing conditional probabilities in Bayesian networks," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3583-3602, March.
    18. Ouysse, Rachida & Kohn, Robert, 2010. "Bayesian variable selection and model averaging in the arbitrage pricing theory model," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3249-3268, December.

  95. Kapetanios, George, 2007. "Estimating deterministically time-varying variances in regression models," Economics Letters, Elsevier, vol. 97(2), pages 97-104, November.
    See citations under working paper version above.
  96. George Kapetanios, 2007. "Measuring Conditional Persistence in Nonlinear Time Series," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(3), pages 363-386, June.

    Cited by:

    1. Villani, Mattias & Kohn, Robert & Giordani, Paolo, 2009. "Regression density estimation using smooth adaptive Gaussian mixtures," Journal of Econometrics, Elsevier, vol. 153(2), pages 155-173, December.
    2. Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023. "Forecasting Value-at-Risk using deep neural network quantile regression," Essex Finance Centre Working Papers 34837, University of Essex, Essex Business School.
    3. Fernandez, Viviana, 2010. "Commodity futures and market efficiency: A fractional integrated approach," Resources Policy, Elsevier, vol. 35(4), pages 276-282, December.
    4. Chan, Wai-Sum, 2022. "On temporal aggregation of some nonlinear time-series models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 38-49.

  97. Kapetanios, G. & Pagan, A. & Scott, A., 2007. "Making a match: Combining theory and evidence in policy-oriented macroeconomic modeling," Journal of Econometrics, Elsevier, vol. 136(2), pages 565-594, February.
    See citations under working paper version above.
  98. George Kapetanios, 2007. "Dynamic factor extraction of cross-sectional dependence in panel unit root tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 313-338.
    See citations under working paper version above.
  99. Kapetanios, George, 2006. "Cluster analysis of panel data sets using non-standard optimisation of information criteria," Journal of Economic Dynamics and Control, Elsevier, vol. 30(8), pages 1389-1408, August.
    See citations under working paper version above.
  100. Kapetanios, George & Labhard, Vincent & Price, Simon, 2006. "Forecasting using predictive likelihood model averaging," Economics Letters, Elsevier, vol. 91(3), pages 373-379, June.
    See citations under working paper version above.
  101. Kapetanios, George, 2006. "Choosing the optimal set of instruments from large instrument sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 612-620, November.

    Cited by:

    1. Marcellino, Massimiliano & Kapetanios, George, 2010. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," CEPR Discussion Papers 7726, C.E.P.R. Discussion Papers.
    2. Malikane, Christopher, 2013. "A New Keynesian Triangle Phillips Curve," MPRA Paper 43548, University Library of Munich, Germany.
    3. Christopher Malikane, 2017. "The labour share and the dynamics of output," Applied Economics, Taylor & Francis Journals, vol. 49(37), pages 3741-3750, August.
    4. Manfred GILLI & Peter WINKER, 2008. "A review of heuristic optimization methods in econometrics," Swiss Finance Institute Research Paper Series 08-12, Swiss Finance Institute.

  102. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2006. "Testing For Cointegration In Nonlinear Smooth Transition Error Correction Models," Econometric Theory, Cambridge University Press, vol. 22(2), pages 279-303, April.

    Cited by:

    1. Kapetanios, George & Shin, Yongcheol, 2008. "GLS detrending-based unit root tests in nonlinear STAR and SETAR models," Economics Letters, Elsevier, vol. 100(3), pages 377-380, September.
    2. Cagli, Efe Caglar & Taskin, Dilvin & Evrim Mandaci, Pınar, 2019. "The short- and long-run efficiency of energy, precious metals, and base metals markets: Evidence from the exponential smooth transition autoregressive models," Energy Economics, Elsevier, vol. 84(C).
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    24. Yersh, Valeryia, 2020. "Current account sustainability and capital mobility in Latin American and Caribbean countries," MPRA Paper 105440, University Library of Munich, Germany.
    25. Daniel Ordoñez-Callamand & Luis F. Melo-Velandia & Oscar M. Valencia-Arana, 2017. "Current Account Sustainability in Latin America Considering Nonlinearities," Borradores de Economia 987, Banco de la Republica de Colombia.
    26. Alpaslan AKÇORAOĞLU & Erkan AĞASLAN, 2009. "Current Account Deficits, Sustainability and Global Financial Crisis: Evidence from Turkey, 1987-2008," Ekonomik Yaklasim, Ekonomik Yaklasim Association, vol. 20(72), pages 1-20.
    27. Kirsi Zongo & Marcellin Ndong Ntah & Jean-Marie Gankou, 2022. "Analysis of the sustainability and determinants of the current account deficit in Sub-Saharan Africa [Analyse de la viabilité et des déterminants du déficit courant en Afrique Sub-saharienne]," Working Papers hal-03577955, HAL.
    28. Garg, Bhavesh & Prabheesh, K.P., 2021. "Testing the intertemporal sustainability of current account in the presence of endogenous structural breaks: Evidence from the top deficit countries," Economic Modelling, Elsevier, vol. 97(C), pages 365-379.
    29. Abm Nasir & Abdullah M. Noman, 2012. "Sustainability of external debt: further evidence from non-linear framework," International Review of Applied Economics, Taylor & Francis Journals, vol. 26(5), pages 673-685, December.
    30. Chen, Shyh-Wei, 2013. "Long memory and regime switching properties of current account deficits in the US," Economic Modelling, Elsevier, vol. 35(C), pages 78-87.
    31. Dissou, Yazid & Nafie, Yousra, 2019. "Sustainability of current account deficits: Evidence from Egypt using an asymmetric ARDL model," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    32. Ibrahim Chowdhury & Gregory Gadzinski & Mathias Hoffmann, 2004. "Asymmetric Dynamics in the Current Account: Evidence from Long-Horizon Data," Working Paper Series in Economics 13, University of Cologne, Department of Economics.

  111. Kapetanios, George, 2004. "The Asymptotic Distribution Of The Cointegration Rank Estimator Under The Akaike Information Criterion," Econometric Theory, Cambridge University Press, vol. 20(4), pages 735-742, August.

    Cited by:

    1. J. Isaac Miller, 2010. "A Nonlinear IV Likelihood-Based Rank Test for Multivariate Time Series and Long Panels," Working Papers 1001, Department of Economics, University of Missouri.
    2. Hansen, Bruce E., 2010. "Averaging estimators for autoregressions with a near unit root," Journal of Econometrics, Elsevier, vol. 158(1), pages 142-155, September.
    3. Xu Cheng & P eter C. B. Phillips, 2009. "Semiparametric cointegrating rank selection," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 83-104, January.
    4. H. Peter Boswijk & Giuseppe Cavaliere & Luca De Angelis & A. M. Robert Taylor, 2023. "Adaptive information-based methods for determining the co-integration rank in heteroskedastic VAR models," Econometric Reviews, Taylor & Francis Journals, vol. 42(9-10), pages 725-757, November.
    5. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    6. Badi H. Baltagi & Zijun Wang, 2006. "Testing for Cointegrating Rank via Model Selection: Evidence from 165 Data Sets," Center for Policy Research Working Papers 83, Center for Policy Research, Maxwell School, Syracuse University.
    7. Miller, J. Isaac & Ratti, Ronald A., 2009. "Crude oil and stock markets: Stability, instability, and bubbles," Energy Economics, Elsevier, vol. 31(4), pages 559-568, July.
    8. Moonsoo Park & Yanhong H. Jin & David A. Bessler, 2008. "The impacts of animal disease crises on the Korean meat market," Agricultural Economics, International Association of Agricultural Economists, vol. 39(2), pages 183-195, September.
    9. Li, Qiaoling & Pan, Jiazhu & Yao, Qiwei, 2009. "On determination of cointegration ranks," LSE Research Online Documents on Economics 24106, London School of Economics and Political Science, LSE Library.
    10. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2020. "Determining the rank of cointegration with infinite variance," Discussion Papers 20/01, University of Nottingham, Granger Centre for Time Series Econometrics.
    11. Maxym Chaban, 2010. "Cointegration analysis with structural breaks and deterministic trends: an application to the Canadian dollar," Applied Economics, Taylor & Francis Journals, vol. 42(23), pages 3023-3037.
    12. Seong, Byeongchan, 2013. "Semiparametric selection of seasonal cointegrating ranks using information criteria," Economics Letters, Elsevier, vol. 120(3), pages 592-595.
    13. Kosei Fukuda, 2011. "Cointegration rank switching model: an application to forecasting interest rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(5), pages 509-522, August.

  112. Kapetanios, George, 2004. "A note on modelling core inflation for the UK using a new dynamic factor estimation method and a large disaggregated price index dataset," Economics Letters, Elsevier, vol. 85(1), pages 63-69, October.

    Cited by:

    1. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    2. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic Factor Models," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 3, pages 25-40, Springer.
    3. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
    4. Alan K. Detmeister, 2011. "The usefulness of core PCE inflation measures," Finance and Economics Discussion Series 2011-56, Board of Governors of the Federal Reserve System (U.S.).
    5. Juraj Hucek & Alexander Karsay & Marian Vavra, 2015. "Short-term Forecasting of Real GDP Using Monthly Data," Working and Discussion Papers OP 1/2015, Research Department, National Bank of Slovakia.
    6. In Choi & Dukpa Kim & Yun Jung Kim & Noh‐Sun Kwark, 2018. "A multilevel factor model: Identification, asymptotic theory and applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 355-377, April.
    7. Camba-Méndez, Gonzalo & Kapetanios, George, 2004. "Forecasting euro area inflation using dynamic factor measures of underlying inflation," Working Paper Series 402, European Central Bank.
    8. Anton Grui & Roman Lysenko, 2017. "Nowcasting Ukraine's GDP Using a Factor-Augmented VAR (FAVAR) Model," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 242, pages 5-13.
    9. Rodríguez Caballero, Carlos Vladimir, 2017. "Estimation of a Dynamic Multilevel Factor Model with possible long-range dependence," DES - Working Papers. Statistics and Econometrics. WS 24614, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
    11. In Choi & Hanbat Jeong, 2019. "Model selection for factor analysis: Some new criteria and performance comparisons," Econometric Reviews, Taylor & Francis Journals, vol. 38(6), pages 577-596, July.
    12. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    13. George Kapetanios, 2004. "Dynamic Factor Extraction of Cross-Sectional Dependence in Panel Unit Root Tests," Working Papers 509, Queen Mary University of London, School of Economics and Finance.
    14. Georgios Antonios Sarantitis & Theophilos Papadimitriou & Periklis Gogas, 2018. "A Network Analysis of the United Kingdom’s Consumer Price Index," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 173-193, February.
    15. Yunus Emre Ergemen, 2022. "Parametric Estimation of Long Memory in Factor Models," CREATES Research Papers 2022-10, Department of Economics and Business Economics, Aarhus University.
    16. Franz Ruch & Mehmet Balcilar & Mampho P. Modise & Rangan Gupta, 2015. "Forecasting Core Inflation: The Case of South Africa," Working Papers 201543, University of Pretoria, Department of Economics.
    17. Gonzalo Camba-Mendez & George Kapetanios, 2005. "Statistical Tests of the Rank of a Matrix and Their Applications in Econometric Modelling," Working Papers 541, Queen Mary University of London, School of Economics and Finance.
    18. Rachel Holden, 2006. "Measuring core inflation," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 69, pages 1-7, December.
    19. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    20. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    21. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    22. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.

  113. Andrew P. Blake & George Kapetanios, 2003. "Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 253-267, May.

    Cited by:

    1. Georgios Chortareas & George Kapetanios, 2006. "The yen real exchange rate may be stationary after all: evidence from non-linear unit root tests," Bank of England working papers 311, Bank of England.
    2. Lavan Mahadeva and Paul Robinson, 2004. "Unit Root Testing in a Central Bank," Handbooks, Centre for Central Banking Studies, Bank of England, number 22, April.
    3. Dimitris Christopoulos, 2006. "Does a non-linear mean reverting process characterize real GDP movements?," Empirical Economics, Springer, vol. 31(3), pages 601-611, September.
    4. Lee Jinu, 2019. "A Neural Network Method for Nonlinear Time Series Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-18, January.

  114. George Kapetanios, 2003. "Threshold models for trended time series," Empirical Economics, Springer, vol. 28(4), pages 687-707, November.
    See citations under working paper version above.
  115. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2003. "Testing for a unit root in the nonlinear STAR framework," Journal of Econometrics, Elsevier, vol. 112(2), pages 359-379, February.

    Cited by:

    1. JamesR. Lothian & MarkP. Taylor, 2008. "Real Exchange Rates Over the Past Two Centuries: How Important is the Harrod-Balassa-Samuelson Effect?," Economic Journal, Royal Economic Society, vol. 118(532), pages 1742-1763, October.
    2. Robinson Kruse & Michael Frömmel & Lukas Menkhoff & Philipp Sibbertsen, 2009. "What do we know about real exchange rate non-linearities?," CREATES Research Papers 2009-50, Department of Economics and Business Economics, Aarhus University.
    3. Yang Fuyu & Leon-Gonzalez Roberto, 2010. "Bayesian Estimation and Model Selection in the Generalized Stochastic Unit Root Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-38, September.
    4. Mario Cerrato & Christian de Peretti & Rolf Larsson & Nicholas Sarantis, 2011. "A nonlinear panel unit root test under cross section dependence," Working Papers 2011_08, Business School - Economics, University of Glasgow.
    5. Pei-Long Shen & Chih-Wei Su & Hsu-Ling Chang, 2013. "Are real GDP levels nonstationary across Central and Eastern European countries?," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 13(1), pages 99-108, July.
    6. Mohsen Bahmani‐Oskooee & Tsangyao Chang & Farhang Niroomand & Omid Ranjbar, 2020. "Fourier nonlinear quantile unit root test and PPP in Africa," Bulletin of Economic Research, Wiley Blackwell, vol. 72(4), pages 451-481, October.
    7. Månsson, Kristofer & Sjölander, Pär, 2014. "Testing for nonlinear panel unit roots under cross-sectional dependency — With an application to the PPP hypothesis," Economic Modelling, Elsevier, vol. 38(C), pages 121-132.
    8. Hepsag, Aycan, 2017. "A unit root test based on smooth transitions and nonlinear adjustment," MPRA Paper 81788, University Library of Munich, Germany.
    9. Juan Carlos Cuestas & Luís A. Gil-Alana, 2009. "Further evidence on the PPP analysis of the Australian dollar: non-linearities, fractional integration and structural changes," NBS Discussion Papers in Economics 2009/3, Economics, Nottingham Business School, Nottingham Trent University.
    10. Kapetanios, George & Shin, Yongcheol, 2008. "GLS detrending-based unit root tests in nonlinear STAR and SETAR models," Economics Letters, Elsevier, vol. 100(3), pages 377-380, September.
    11. Melike E. Bildirici, 2020. "Environmental pollution, hydropower and nuclear energy generation before and after catastrophe: Bathtub‐Weibull curve and MS‐VECM methods," Natural Resources Forum, Blackwell Publishing, vol. 44(4), pages 289-310, November.
    12. Mehmet Erdoğmuş, 2023. "Do Shocks Permanently Affect Ecological Balance Per Capita in Brazil, South Africa, and New Zealand?," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 147-160, December.
    13. Memduh Alper DEMÄ°R, 2021. "External debt sustainability in the transition economies of southeast Europe: an application by wavelet-based unit root tests," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 12, pages 173-190, June.
    14. Zeynel Abidin Ozdemir & Mehmet Balcilar & Aysit Tansel, 2014. "Are Labor Force Participation Rates Really Non-Stationary? Evidence from Three OECD Countries," Working Papers 15-25, Eastern Mediterranean University, Department of Economics.
    15. Nikolaou, Kleopatra, 2008. "The behaviour of the real exchange rate: Evidence from regression quantiles," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 664-679, May.
    16. Mubariz Hasanov, 2012. "Re-examining Purchasing Power Parity for the Australian Real Exchange Rate," Hacettepe University Department of Economics Working Papers 20124, Hacettepe University, Department of Economics.
    17. Beckmann, Joscha & Czudaj, Robert, 2013. "Is there a homogeneous causality pattern between oil prices and currencies of oil importers and exporters?," Energy Economics, Elsevier, vol. 40(C), pages 665-678.
    18. David I. Harvey & Stephen J. Leybourne & Emily J. Whitehouse, 2017. "Testing for a unit root against ESTAR stationarity," Discussion Papers 17/02, University of Nottingham, Granger Centre for Time Series Econometrics.
    19. Fumitaka Furuoka & Kiew Ling Pui & Chinyere Ezeoke & Ray I. Jacob & Olaoluwa S. Yaya, 2024. "Growth Slowdowns And Middle-Income Trap: Evidence From New Unit Root Framework," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 69(01), pages 461-477, March.
    20. Cheng, Shu-Ching & Wu, Tsung-pao & Lee, Kuei-Chiu & Chang, Tsangyao, 2014. "Flexible Fourier unit root test of unemployment for PIIGS countries," Economic Modelling, Elsevier, vol. 36(C), pages 142-148.
    21. Burak GÜRIŞ & İpek M. YURTTAGÜLER & Muhammed TIRAŞOĞLU, 2017. "Unemployment convergence analysis for Nordic countries: Evidence from linear and nonlinear unit root tests," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(1(610), S), pages 45-56, Spring.
    22. Cheng, Ka Ming, 2022. "Doubts on natural rate of unemployment: Evidence and policy implications," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 230-239.
    23. Dilem Yıldırım, 2016. "Empirical Investigation of Purchasing Power Parity for Turkey: Evidence from Recent Nonlinear Unit Root Tests," ERC Working Papers 1604, ERC - Economic Research Center, Middle East Technical University, revised Apr 2016.
    24. Baillie, Richard T. & Kapetanios, George, 2007. "Testing for Neglected Nonlinearity in Long-Memory Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 447-461, October.
    25. Emirmahmutoglu, Furkan & Omay, Tolga, 2014. "Reexamining the PPP hypothesis: A nonlinear asymmetric heterogeneous panel unit root test," Economic Modelling, Elsevier, vol. 40(C), pages 184-190.
    26. Yang, Yang & Zhao, Zhao, 2020. "Quantile nonlinear unit root test with covariates and an application to the PPP hypothesis," Economic Modelling, Elsevier, vol. 93(C), pages 728-736.
    27. Cheng, Ka Ming & Durmaz, Nazif & Kim, Hyeongwoo & Stern, Michael L., 2012. "Hysteresis vs. natural rate of US unemployment," Economic Modelling, Elsevier, vol. 29(2), pages 428-434.
    28. Mohsen Bahmani-Oskooee & Ali M. Kutan & Su Zhou, 2009. "A century of PPP: supportive results from nonlinear unit root tests," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 11(1), pages 19-27.
    29. Sophocles N. Brissimis & Petros M. Migiakis, 2016. "Inflation persistence, learning dynamics and the rationality of inflation expectations," Empirical Economics, Springer, vol. 51(3), pages 963-979, November.
    30. Dergiades, Theologos & Milas, Costas & Panagiotidis, Theodore, 2013. "Tweets, Google trends and sovereign spreads in the GIIPS," LSE Research Online Documents on Economics 54405, London School of Economics and Political Science, LSE Library.
    31. Frederick Wallace, 2008. "Nonlinear unit root tests of PPP using long-horizon data," Economics Bulletin, AccessEcon, vol. 6(33), pages 1-8.
    32. Mihaela Simionescu & Nicolas Schneider & Beata Gavurova, 2024. "A Bayesian vector-autoregressive application with time-varying parameters on the monetary shocks–production network nexus," Journal of Applied Economics, Taylor & Francis Journals, vol. 27(1), pages 2395114-239, December.
    33. Nazlioglu, Saban & Gormus, N. Alper & Soytas, Uğur, 2016. "Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis," Energy Economics, Elsevier, vol. 60(C), pages 168-175.
    34. Juan Carlos Cuestas & Paulo José Regis, 2010. "Purchasing power parity in OECD countries: nonlinear unit root tests revisited," NBS Discussion Papers in Economics 2010/3, Economics, Nottingham Business School, Nottingham Trent University.
    35. Xin Shen & Mark J. Holmes, 2014. "Do Asia-Pacific stock prices follow a random walk? A regime-switching perspective," Applied Economics Letters, Taylor & Francis Journals, vol. 21(3), pages 189-195, February.
    36. Venus Khim-Sen Liew, 2003. "The Validity of PPP Revisited: An Application of Non-linear Unit Root Test," International Finance 0308001, University Library of Munich, Germany.
    37. Herrerias, M.J. & Ordoñez, J., 2012. "New evidence on the role of regional clusters and convergence in China (1952–2008)," China Economic Review, Elsevier, vol. 23(4), pages 1120-1133.
    38. Dimitris K. Christopoulos & Miguel A. León‐Ledesma, 2007. "A Long‐Run Non‐Linear Approach to the Fisher Effect," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2‐3), pages 543-559, March.
    39. Beckmann, Joscha & Czudaj, Robert, 2013. "Oil prices and effective dollar exchange rates," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 621-636.
    40. Chi-Wei Su & Hui Yu & Hsu-Ling Chang & Xiao-Lin Li, 2017. "How does inflation determine inflation uncertainty? A Chinese perspective," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1417-1434, May.
    41. Mohsen Bahmani-Oskooee & Tsangyao Chang & Kuei-Chiu Lee, 2016. "Panel asymmetric nonlinear unit root test and PPP in Africa," Applied Economics Letters, Taylor & Francis Journals, vol. 23(8), pages 554-558, May.
    42. Theodoros Arvanitopoulos & Vassilis Monastiriotis & Theodore Panagiotidis, 2021. "Drivers of convergence: The role of first- and second-nature geography," Urban Studies, Urban Studies Journal Limited, vol. 58(14), pages 2880-2900, November.
    43. Sollis, Robert, 2011. "Testing the unit root hypothesis against TAR nonlinearity using STAR-based tests," Economics Letters, Elsevier, vol. 112(1), pages 19-22, July.
    44. Andros Gregoriou & Alexandros Kontonikas & Alberto Montagnoli, 2014. "Aggregate and regional house price to earnings ratio dynamics in the UK," Urban Studies, Urban Studies Journal Limited, vol. 51(13), pages 2916-2927, October.
    45. Trunin, Pavel (Трунин, Павел) & Bozhechkova, Alexandra (Божечкова, Александра), 2015. "Analysis of Factors Affecting the Dynamics of the Real Ruble Exchange Rate [Анализ Факторов Динамики Реального Валютного Курса Рубля]," Published Papers mak13, Russian Presidential Academy of National Economy and Public Administration.
    46. Wahab, Bashir A. & Adewuyi, Adeolu O., 2021. "Analysis of major properties of metal prices using new methods: Structural breaks, non-linearity, stationarity and bubbles," Resources Policy, Elsevier, vol. 74(C).
    47. Phiri, Andrew, 2018. "How sustainable are fiscal budgets in the Kingdom of Swaziland?," MPRA Paper 85149, University Library of Munich, Germany.
    48. Kurmas Akdogan, 2016. "Unemployment Hysteresis and Structural Change in Europe," Working Papers 1618, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    49. Ikeno, Hidehiro, 2014. "Long-run analysis on convergence of Japanese local price levels: A pairwise approach," Economic Modelling, Elsevier, vol. 42(C), pages 390-397.
    50. McMillan, David G., 2009. "The confusing time-series behaviour of real exchange rates: Are asymmetries important?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(4), pages 692-711, October.
    51. Yamin Ahmad & Adam Check & Ming Chien Lo, 2024. "Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2139-2173, June.
    52. Mario Cerrato & Christian De Peretti & Chris Stewart, 2013. "Is The Consumption–Income Ratio Stationary? Evidence From Linear And Non-Linear Panel Unit Root Tests For Oecd And Non-Oecd Countries," Manchester School, University of Manchester, vol. 81(1), pages 102-120, January.
    53. Yilanci, Veli & Aydin, Mücahit & Aydin, Mehmet, 2019. "Residual Augmented Fourier ADF Unit Root Test," MPRA Paper 96797, University Library of Munich, Germany.
    54. Solarin, Sakiru Adebola & Lean, Hooi Hooi, 2016. "Are fluctuations in oil consumption permanent or transitory? Evidence from linear and nonlinear unit root tests," Energy Policy, Elsevier, vol. 88(C), pages 262-270.
    55. Öğünç, Fethi & Akdoğan, Kurmaş & Başer, Selen & Chadwick, Meltem Gülenay & Ertuğ, Dilara & Hülagü, Timur & Kösem, Sevim & Özmen, Mustafa Utku & Tekatlı, Necati, 2013. "Short-term inflation forecasting models for Turkey and a forecast combination analysis," Economic Modelling, Elsevier, vol. 33(C), pages 312-325.
    56. De Villeris, David & Apopo, Natalya & Phiri, Andrew, 2018. "Unobserved structural shifts and asymmetries in the random walk model for stock returns in African frontier markets," MPRA Paper 87963, University Library of Munich, Germany.
    57. Luis A Gil-Alana & Christophe André & Rangan Gupta & Tsangyao Chang & Omid Ranjbar, 2015. "The Feldstein-Horioka Puzzle in South Africa: A Fractional Cointegration Approach," Working Papers 201501, University of Pretoria, Department of Economics.
    58. Bastianin, Andrea & Galeotti, Marzio & Polo, Michele, 2019. "Convergence of European natural gas prices," Energy Economics, Elsevier, vol. 81(C), pages 793-811.
    59. Tolga Omay & Aysegul Corakci & Esra Hasdemir, 2021. "High Persistence and Nonlinear Behavior in Financial Variables: A More Powerful Unit Root Testing in the ESTAR Framework," Mathematics, MDPI, vol. 9(20), pages 1-21, October.
    60. Muhammad Shahbaz & Vassilios Papavassiliou & Amine Lahiani & David Roubaud, 2023. "Are we moving towards decarbonisation of the global economy? Lessons from the distant past to the present," Post-Print hal-03573208, HAL.
    61. Ivan Paya & David Peel, 2005. "The process followed by PPP data. On the properties of linearity tests," Applied Economics, Taylor & Francis Journals, vol. 37(21), pages 2515-2522.
    62. Kuswanto, Heri & Sibbertsen, Philipp, 2009. "Testing for Long Memory Against ESTAR Nonlinearities," Hannover Economic Papers (HEP) dp-427, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    63. Jiang Wu & Jianzhong Zhou & Lu Chen & Lei Ye, 2015. "Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5091-5108, November.
    64. Mohsen Bahmani-Oskooee & Abera Gelan, 2006. "Testing the PPP in the non-linear STAR Framework: Evidence from Africa," Economics Bulletin, AccessEcon, vol. 6(17), pages 1-15.
    65. Tolga Omay & Furkan Emirmahmutoglu & Mubariz Hasanov, 2018. "Structural break, nonlinearity and asymmetry: a re-examination of PPP proposition," Applied Economics, Taylor & Francis Journals, vol. 50(12), pages 1289-1308, March.
    66. Araç, Ayşen & Yalta, A. Yasemin, 2015. "Testing the expectations hypothesis for the Eurozone: A nonlinear cointegration analysis," Finance Research Letters, Elsevier, vol. 15(C), pages 41-48.
    67. Tsangyao Chang & Yu-Shao Liu & Chi-Wei Su, 2012. "Purchasing power parity with nonlinear and asymmetric smooth adjustment for the Middle Eastern countries," Applied Economics Letters, Taylor & Francis Journals, vol. 19(5), pages 487-491, March.
    68. Troster, Victor & Shahbaz, Muhammad & Uddin, Gazi Salah, 2018. "Renewable Energy, Oil Prices, and Economic Activity: A Granger-causality in Quantiles Analysis," MPRA Paper 84194, University Library of Munich, Germany, revised 19 Jan 2018.
    69. Winkelried, Diego, 2021. "Unit roots in real primary commodity prices? A meta-analysis of the Grilli and Yang data set," Journal of Commodity Markets, Elsevier, vol. 23(C).
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    72. Luis A.Gil-Alana & Shinhye Chang & Mehmet Balcilar & Goodness C. Aye & Rangan Gupta, 2014. "Persistence in Precious Metal Prices: A Fractional Integration Approach with Structural Breaks," Working Papers 201458, University of Pretoria, Department of Economics.
    73. Lorenzo Trapani, 2018. "Testing for strict stationarity in a random coefficient autoregressive model," Discussion Papers 18/02, University of Nottingham, Granger Centre for Time Series Econometrics.
    74. Christou, Christina & Gupta, Rangan & Nyakabawo, Wendy & Wohar, Mark E., 2018. "Do house prices hedge inflation in the US? A quantile cointegration approach," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 15-26.
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    78. Wu, Jyh-Lin & Lee, Hsiu-Yun, 2009. "A revisit to the non-linear mean reversion of real exchange rates: Evidence from a series-specific non-linear panel unit-root test," Journal of Macroeconomics, Elsevier, vol. 31(4), pages 591-601, December.
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    714. Tolga Omay & Ayşegül Çorakcı & Furkan Emirmahmutoglu, 2017. "Real interest rates: nonlinearity and structural breaks," Empirical Economics, Springer, vol. 52(1), pages 283-307, February.
    715. Ebru Doğan, 2024. "Electricity Capacity Convergence in G20 Countries: New Findings from New Tests," Sustainability, MDPI, vol. 16(19), pages 1-15, September.
    716. Wang, Xiao-Qing & Su, Chi-Wei & Lobonţ, Oana-Ramona & Li, Hao & Nicoleta-Claudia, Moldovan, 2022. "Is China's carbon trading market efficient? Evidence from emissions trading scheme pilots," Energy, Elsevier, vol. 245(C).
    717. George Kapetanios & Yongcheol Shin, 2003. "Testing for Nonstationary Long Memory against Nonlinear Ergodic Models," Working Papers 500, Queen Mary University of London, School of Economics and Finance.
    718. Donauer, Stefanie & Heinen, Florian & Sibbertsen, Philipp, 2010. "Identification problems in ESTAR models and a new model," Hannover Economic Papers (HEP) dp-444, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    719. Lopes, Artur Silva & Zsurkis, Gabriel Florin, 2017. "Are linear models really unuseful to describe business cycle data?," Economics Discussion Papers 2017-5, Kiel Institute for the World Economy (IfW Kiel).
    720. Nazlioglu, Saban & Kucukkaplan, Ilhan & Kilic, Emre & Altuntas, Mehmet, 2022. "Financial market integration of emerging markets: Heavy tails, structural shifts, nonlinearity, and asymmetric persistence," Research in International Business and Finance, Elsevier, vol. 62(C).
    721. Feng, Xiaobing & Hu, Haibo & Wang, Xiaofan, 2010. "The evolutionary synchronization of the exchange rate system in ASEAN+6," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5785-5793.
    722. Sakiru Adebola SOLARIN, 2017. "The Stationarity of Consumption-Income Ratios: Nonlinear Evidence in ASEAN Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 109-123, June.
    723. Steve Cook, 2008. "Non-linear unit root testing in the presence of heavy-tailed innovation processes," Economics Bulletin, AccessEcon, vol. 3(38), pages 1-10.
    724. Gormus, Alper & Nazlioglu, Saban & Soytas, Ugur, 2018. "High-yield bond and energy markets," Energy Economics, Elsevier, vol. 69(C), pages 101-110.
    725. Kim, Bong-Han & Kim, Hong-Kee & Oh, Keun-Yeob, 2009. "The purchasing power parity of Southeast Asian currencies: A time-varying coefficient approach," Economic Modelling, Elsevier, vol. 26(1), pages 96-106, January.
    726. Mohsen Bahmani-Oskooee & Tsangyao Chang & Shu-Ching Cheng & Tsung-Pao Wu, 2015. "Revisiting purchasing power parity in major oil-exporting countries," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 8(1-2), pages 108-116, July.
    727. Shyh-Wei Chen & Chi-Sheng Hsu & Cyun-Jhen Pen, 2016. "Are Inflation Rates Mean-reverting Processes? Evidence from Six Asian Countries," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 12(1), pages 119-155, February.
    728. Jing Li & Junsoo Lee, 2010. "ADL tests for threshold cointegration," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(4), pages 241-254, July.
    729. Shahbaz, Muhammad & Omay, Tolga & Roubaud, David, 2019. "Sharp and Smooth Breaks in Unit Root Testing of Renewable Energy Consumption: The Way Forward," MPRA Paper 92176, University Library of Munich, Germany, revised 11 Feb 2019.
    730. Andy Snell & George Kapetanios & Yongcheol Shin, 2004. "Testing for nonlinear cointegration between stock prices and dividends," Money Macro and Finance (MMF) Research Group Conference 2003 90, Money Macro and Finance Research Group.
    731. Liu, Yan & Chang, Hsu-Ling & Su, Chi-Wei, 2013. "Do real interest rates converge across East Asian countries based on China?," Economic Modelling, Elsevier, vol. 31(C), pages 467-473.
    732. Arize, Augustine C. & Malindretos, John, 2012. "Nonstationarity and nonlinearity in inflation rate: Some further evidence," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 224-234.
    733. Adewuyi, Adeolu O. & Ogebe, Joseph O., 2019. "The validity of uncovered interest parity: Evidence from african members and non-member of the organisation of petroleum exporting countries (OPEC)," Economic Modelling, Elsevier, vol. 82(C), pages 229-249.
    734. Ogali, Oscar I.O. & Okoro, Emeka E. & Olafuyi, Saburi G., 2023. "Assessing consensus on nexus between natural gas consumption and economic growth," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    735. Ceylan, Reşat & Abiyev, Vasif, 2016. "An examination of convergence hypothesis for EU-15 countries," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 96-105.
    736. Apergis, Nicholas & Payne, James E., 2017. "Per capita carbon dioxide emissions across U.S. states by sector and fossil fuel source: Evidence from club convergence tests," Energy Economics, Elsevier, vol. 63(C), pages 365-372.
    737. Noriega Antonio E. & Rodríguez-Pérez Cid Alonso, 2011. "Stationarity, structural breaks, and economic growth in Mexico: 1895-2008," Working Papers 2011-11, Banco de México.
    738. Njindan Iyke, Bernard, 2015. "Asymmetries, Structural Breaks, and Nonlinear Persistence: Evidence and Implications for Uncovering the Energy-Growth Nexus in Selected African Countries," MPRA Paper 67163, University Library of Munich, Germany.
    739. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    740. Mohsen Bahmani‐Oskooee & Scott W. Hegerty, 2009. "Purchasing Power Parity In Less‐Developed And Transition Economies: A Review Paper," Journal of Economic Surveys, Wiley Blackwell, vol. 23(4), pages 617-658, September.
    741. Silva Lopes, Artur, 2020. "Revisiting income convergence with DF-Fourier tests: old evidence with a new test," MPRA Paper 102208, University Library of Munich, Germany.
    742. Chi Lau & Ka Fung & Lee Pugalis, 2014. "Is health care expenditure across Europe converging? Findings from the application of a nonlinear panel unit root test," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 4(2), pages 137-156, December.
    743. Junttila, Juha & Korhonen, Marko, 2011. "Nonlinearity and time-variation in the monetary model of exchange rates," Journal of Macroeconomics, Elsevier, vol. 33(2), pages 288-302, June.
    744. Awaworyi Churchill, Sefa & Inekwe, John & Ivanovski, Kris & Smyth, Russell, 2019. "Dynamics of oil price, precious metal prices and the exchange rate in the long-run," Energy Economics, Elsevier, vol. 84(C).
    745. Ayşegül Çorakcı & Furkan Emirmahmutoglu & Tolga Omay, 2017. "Re-examining the real interest rate parity hypothesis (RIPH) using panel unit root tests with asymmetry and cross-section dependence," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 91-120, February.
    746. Lior Gallo, 2023. "Electricity Intensity Convergence in the OECD Countries," Bank of Israel Working Papers 2023.10, Bank of Israel.
    747. Marko Korhonen & Mikko Puhakka, 2015. "Revisiting unit roots in divorce rates," Applied Economics Letters, Taylor & Francis Journals, vol. 22(8), pages 628-631, May.
    748. Hasanov, Fakhri J. & Shannak, Sa'd, 2020. "Electricity incentives for agriculture in Saudi Arabia. Is that relevant to remove them?," Energy Policy, Elsevier, vol. 144(C).
    749. Iqbal, Javed & Rehman, Muhammad & Ur-Rehman, Hafeez, 2011. "Nonlinearity In Inflation, A Case of Pakistan," MPRA Paper 35858, University Library of Munich, Germany.
    750. Beckmann, Joscha & Belke, Ansgar & Czudaj, Robert, 2014. "Regime-dependent adjustment in energy spot and futures markets," Economic Modelling, Elsevier, vol. 40(C), pages 400-409.

  116. Kapetanios, George, 2003. "A note on an iterative least-squares estimation method for ARMA and VARMA models," Economics Letters, Elsevier, vol. 79(3), pages 305-312, June.
    See citations under working paper version above.
  117. Andrew P. Blake & George Kapetanios, 2003. "A radial basis function artificial neural network test for neglected nonlinearity," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 357-373, December.

    Cited by:

    1. Psaradakis, Zacharias & Vávra, Marián, 2014. "On testing for nonlinearity in multivariate time series," Economics Letters, Elsevier, vol. 125(1), pages 1-4.
    2. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    3. Seung Hyun Hong & Peter C. B. Phillips, 2005. "Testing Linearity in Cointegrating Relations with an Application to Purchasing Power Parity," Cowles Foundation Discussion Papers 1541, Cowles Foundation for Research in Economics, Yale University.
    4. KANAZAWA, Nobuyuki & 金澤, 伸幸, 2018. "Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks," Discussion paper series HIAS-E-64, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    5. Marian Vavra, 2013. "Testing for linear and Markov switching DSGE models," Working and Discussion Papers WP 3/2013, Research Department, National Bank of Slovakia.
    6. Marian Vavra, 2012. "Robustness of Power Properties of Non-linearity Tests," Birkbeck Working Papers in Economics and Finance 1205, Birkbeck, Department of Economics, Mathematics & Statistics.
    7. Konstantinos N. Konstantakis & Panagiotis T. Cheilas & Ioannis G. Melissaropoulos & Panos Xidonas & Panayotis G. Michaelides, 2023. "Supply chains and fake news: a novel input–output neural network approach for the US food sector," Annals of Operations Research, Springer, vol. 327(2), pages 779-794, August.

  118. Kapetanios George, 2003. "Bootstrap Neural Network Cointegration Tests Against Nonlinear Alternative Hypotheses," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-16, July.

    Cited by:

    1. Seung Hyun Hong & Peter C. B. Phillips, 2005. "Testing Linearity in Cointegrating Relations with an Application to Purchasing Power Parity," Cowles Foundation Discussion Papers 1541, Cowles Foundation for Research in Economics, Yale University.

  119. Chortareas, Georgios E. & Kapetanios, George & Shin, Yongcheol, 2002. "Nonlinear mean reversion in real exchange rates," Economics Letters, Elsevier, vol. 77(3), pages 411-417, November.

    Cited by:

    1. King, Alan & Ramlogan-Dobson, Carlyn, 2011. "Nonlinear time-series convergence: The role of structural breaks," Economics Letters, Elsevier, vol. 110(3), pages 238-240, March.
    2. Michael Frömmel & Darko B. Vukovic & Jinyuan Wu, 2022. "The Dollar Exchange Rate, Adjustment to the Purchasing Power Parity, and the Interest Rate Differential," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    3. Westerhoff Frank H. & Reitz Stefan, 2003. "Nonlinearities and Cyclical Behavior: The Role of Chartists and Fundamentalists," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(4), pages 1-15, December.
    4. Kanas, Angelos & Genius, Margarita, 2005. "Regime (non)stationarity in the US/UK real exchange rate," Economics Letters, Elsevier, vol. 87(3), pages 407-413, June.
    5. Wu, Jyh-Lin & Lee, Hsiu-Yun, 2009. "A revisit to the non-linear mean reversion of real exchange rates: Evidence from a series-specific non-linear panel unit-root test," Journal of Macroeconomics, Elsevier, vol. 31(4), pages 591-601, December.
    6. George Kapetanios & Yongcheol Shin, 2002. "GLS Detrending for Nonlinear Unit Root Tests," Working Papers 472, Queen Mary University of London, School of Economics and Finance.
    7. Claire Giordano, 2021. "How frequent a BEER? Assessing the impact of data frequency on real exchange rate misalignment estimation," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(3), pages 365-404, July.
    8. Maican, Florin G. & Sweeney, Richard J., 2006. "Real Exchange Rate Adjustment In European Transition Countries," Working Papers in Economics 202, University of Gothenburg, Department of Economics.
    9. Hyeongwoo Kim & Deockhyun Ryu, 2015. "Measuring the Speed of Convergence of Stock Prices: A Nonparametric and Nonlinear Approach," Auburn Economics Working Paper Series auwp2015-08, Department of Economics, Auburn University.
    10. Stavros Degiannakis & Christos Floros & Enrique Salvador & Dimitrios Vougas, 2022. "On the stationarity of futures hedge ratios," Operational Research, Springer, vol. 22(3), pages 2281-2303, July.
    11. Liu, Lin & Chang, Hsu-Ling & Su, Chi-Wei & Jiang, Chun, 2013. "Real interest rate parity in East Asian countries based on China with flexible Fourier stationary test," Japan and the World Economy, Elsevier, vol. 25, pages 52-58.
    12. Hasan, Mohammad S., 2004. "Univariate time series behaviour of the real exchange rate: evidence from colonial India," Economics Letters, Elsevier, vol. 84(1), pages 75-80, July.
    13. Atanu Ghoshray & Madhavi Pundit, 2021. "Economic growth in China and its impact on international commodity prices," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2776-2789, April.
    14. Georgios Chortareas & George Kapetanios, 2006. "The yen real exchange rate may be stationary after all: evidence from non-linear unit root tests," Bank of England working papers 311, Bank of England.
    15. Mubariz Hasanov, 2014. "Testing for a unit root in the presence of a nonlinear trend: The case of Australian Reel Exchange Rate," Econometrics Letters, Bilimsel Mektuplar Organizasyonu (Scientific letters), vol. 1(1), pages 10-17.
    16. Ahmad Zubaidi Baharumshah & Venus Khim‐Sen Liew & Chan Tze Haw, 2009. "The Real Interest Rate Differential: International Evidence Based On Non‐Linear Unit Root Tests," Bulletin of Economic Research, Wiley Blackwell, vol. 61(1), pages 83-94, January.
    17. Georgios Chortareas & George Kapetanios & Merih Uctum, 2003. "A Nonlinear Approach to Public Finance Sustainability in Latin America," Working Papers 486, Queen Mary University of London, School of Economics and Finance.
    18. Yilmaz Akdi & Zeynel Abidin Ozdemir & Hasan Olgun, 2009. "Testing the PPP hypothesis for G-7 countries," Applied Economics Letters, Taylor & Francis Journals, vol. 16(1), pages 99-101.
    19. Hsu-Ling Chang & De-Chih Liu & Chi-Wei Su, 2012. "Purchasing power parity with flexible Fourier stationary test for Central and Eastern European countries," Applied Economics, Taylor & Francis Journals, vol. 44(32), pages 4249-4256, November.
    20. George Kapetanios, 2002. "Testing for Neglected Nonlinearity in Long Memory Models," Working Papers 474, Queen Mary University of London, School of Economics and Finance.
    21. Jiang, Chun & Li, Xiao-Lin & Chang, Hsu-Ling & Su, Chi-Wei, 2013. "Uncovered interest parity and risk premium convergence in Central and Eastern European countries," Economic Modelling, Elsevier, vol. 33(C), pages 204-208.
    22. Haluk Erlat, 2004. "Unit roots or nonlinear stationarity in Turkish real exchange rates," Applied Economics Letters, Taylor & Francis Journals, vol. 11(10), pages 645-650.
    23. King, Alan & Ramlogan-Dobson, Carlyn, 2015. "International income convergence: Is Latin America actually different?," Economic Modelling, Elsevier, vol. 49(C), pages 212-222.
    24. Georgios Chortareas & George Kapetanios & Merih Uctum, 2008. "Nonlinear Alternatives to Unit Root Tests and Public Finances Sustainability: Some Evidence from Latin American and Caribbean Countries," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(5), pages 645-663, October.
    25. Giraitis, L. & Kapetanios, G. & Yates, T., 2014. "Inference on stochastic time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 179(1), pages 46-65.
    26. Ahmad, Ahmad Hassan & Aworinde, Olalekan Bashir, 2016. "The role of structural breaks, nonlinearity and asymmetric adjustments in African bilateral real exchange rates," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 144-159.
    27. Su, Chi-Wei & Chang, Hsu-Ling & Chang, Tsangyao & Yin, Kedong, 2014. "Monetary convergence in East Asian countries relative to China," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 228-237.
    28. Angelos Kanas, 2009. "Real exchange rate, stationarity, and economic fundamentals," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 33(4), pages 393-409, October.
    29. King, Alan & Ramlogan-Dobson, Carlyn, 2015. "Is Africa Actually Developing?," World Development, Elsevier, vol. 66(C), pages 598-613.
    30. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    31. Hyeongwoo Kim & Liliana Stern & Michael Stern, 2009. "Nonlinear mean reversion in the G7 stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 19(5), pages 347-355.
    32. Alan King & Carlyn Ramlogan-Dobson, 2016. "Is there club convergence in Latin America?," Empirical Economics, Springer, vol. 51(3), pages 1011-1031, November.
    33. Kanas, Angelos, 2006. "Purchasing Power Parity and Markov Regime Switching," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(6), pages 1669-1687, September.
    34. Ho, Tsung-wu, 2005. "Investigating the threshold effects of inflation on PPP," Economic Modelling, Elsevier, vol. 22(5), pages 926-948, September.
    35. Jean-François Goux, 2008. "Ruptures épaisses et stationnarité en tendance : le cas du taux de change euro-dollar," Post-Print halshs-00333576, HAL.
    36. Tsangyao Chang & Hsu-Ling Chang & Hsiao-Ping Chu & Chi-Wei Su, 2006. "Does PPP hold in African countries? Further evidence based on a highly dynamic non-linear (logistic) unit root test," Applied Economics, Taylor & Francis Journals, vol. 38(20), pages 2453-2459.
    37. Maria Eleftheriou & Nikolas A. Müller-Plantenberg, 2018. "The Purchasing Power Parity Fallacy: Time to Reconsider the PPP Hypothesis," Open Economies Review, Springer, vol. 29(3), pages 481-515, July.
    38. Christina Anderl & Guglielmo Maria Caporale, 2021. "Nonlinearities and asymmetric adjustment to PPP in an exchange rate model with inflation expectations," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 49(6), pages 937-959, August.
    39. Christina Anderl & Guglielmo Maria Caporale, 2021. "Nonlinearities and Asymmetric Adjustment to PPP in an Exchange Rate Model with Inflation Expectations," CESifo Working Paper Series 8921, CESifo.
    40. Meng, Xiangcai & Huang, Chia-Hsing, 2016. "Nonlinear models for the sources of real effective exchange rate fluctuations: Evidence from the Republic of Korea," Japan and the World Economy, Elsevier, vol. 40(C), pages 21-30.
    41. Kian-Ping Lim & Venus Khim-Sen Liew, 2003. "Testing for Non-Linearity in ASEAN Financial Markets," Finance 0308002, University Library of Munich, Germany.
    42. Paresh Kumar Narayan, 2006. "Are bilateral real exchange rates stationary? Evidence from Lagrange multiplier unit root tests for India," Applied Economics, Taylor & Francis Journals, vol. 38(1), pages 63-70.
    43. Jiang, Chun & Jian, Na & Liu, Tie-Ying & Su, Chi-Wei, 2016. "Purchasing power parity and real exchange rate in Central Eastern European countries," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 349-358.
    44. Ceylan, Reşat & Abiyev, Vasif, 2016. "An examination of convergence hypothesis for EU-15 countries," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 96-105.

  120. Kapetanios, George, 2001. "Incorporating lag order selection uncertainty in parameter inference for AR models," Economics Letters, Elsevier, vol. 72(2), pages 137-144, August.
    See citations under working paper version above.
  121. Gonzalo Camba-Mendez & George Kapetanios & Richard J. Smith & Martin R. Weale, 2001. "An automatic leading indicator of economic activity: forecasting GDP growth for European countries," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-37.
    See citations under working paper version above.
  122. George Kapetanios, 2001. "Model Selection in Threshold Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(6), pages 733-754, November.
    See citations under working paper version above.
  123. Kapetanios, George, 2000. "Small sample properties of the conditional least squares estimator in SETAR models," Economics Letters, Elsevier, vol. 69(3), pages 267-276, December.

    Cited by:

    1. Norman, Stephen, 2008. "Systematic small sample bias in two regime SETAR model estimation," Economics Letters, Elsevier, vol. 99(1), pages 134-138, April.
    2. Galvao Ana Beatriz & Marcellino Massimiliano, 2014. "The effects of the monetary policy stance on the transmission mechanism," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 217-236, May.
    3. George Kapetanios & Elias Tzavalis, 2005. "Nonlinear Modelling of Autoregressive Structural Breaks in a US Diffusion Index Dataset," Working Papers 537, Queen Mary University of London, School of Economics and Finance.
    4. Andreas A. Andrikopoulos & Dimitrios C. Gkountanis, 2011. "Issues and Models in Applied Econometrics: A partial survey," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 9(2), pages 107-165.
    5. Sinem Hacıoğlu Hoke & George Kapetanios, 2021. "Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 125-150, January.
    6. Man-Wai Ng & Wai-Sum Chan, 2004. "Robustness of alternative non-linearity tests for SETAR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 215-231.
    7. Massacci, Daniele, 2013. "A variable addition test for exogeneity in structural threshold models," Economics Letters, Elsevier, vol. 120(1), pages 5-9.
    8. Coakley, Jerry & Fuertes, Ana-Maria, 2006. "Testing for sign and amplitude asymmetries using threshold autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 30(4), pages 623-654, April.
    9. Zhang, Li-Xin & Chan, Wai-Sum & Cheung, Siu-Hung & Hung, King-Chi, 2009. "A note on the consistency of a robust estimator for threshold autoregressive processes," Statistics & Probability Letters, Elsevier, vol. 79(6), pages 807-813, March.
    10. George Kapetanios & Elias Tzavalis, 2006. "Stochastic Volatility Driven by Large Shocks," Working Papers 568, Queen Mary University of London, School of Economics and Finance.
    11. Marcellino, Massimiliano & Galvão, Ana Beatriz, 2010. "Endogenous Monetary Policy Regimes and the Great Moderation," CEPR Discussion Papers 7827, C.E.P.R. Discussion Papers.
    12. George Kapetanios, 2004. "Testing for Exogeneity in Nonlinear Threshold Models," Working Papers 515, Queen Mary University of London, School of Economics and Finance.
    13. Massacci, Daniele, 2017. "Least squares estimation of large dimensional threshold factor models," Journal of Econometrics, Elsevier, vol. 197(1), pages 101-129.
    14. George Kapetanios, 2004. "The Impact of Large Structural Shocks on Economic Relationships: Evidence from Oil Price Shocks," Working Papers 524, Queen Mary University of London, School of Economics and Finance.
    15. Baillie, Richard T. & Kapetanios, George, 2008. "Nonlinear models for strongly dependent processes with financial applications," Journal of Econometrics, Elsevier, vol. 147(1), pages 60-71, November.
    16. Marian Vavra, 2012. "A Note on the Finite Sample Properties of the CLS Method of TAR Models," Birkbeck Working Papers in Economics and Finance 1206, Birkbeck, Department of Economics, Mathematics & Statistics.
    17. Marian Vavra, 2023. "Bias-Correction in Time Series Quantile Regression Models," Working and Discussion Papers WP 3/2023, Research Department, National Bank of Slovakia.
    18. Kapetanios, G. & Tzavalis, E., 2010. "Modeling structural breaks in economic relationships using large shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 417-436, March.
    19. George Kapetanios, 2003. "Using Extraneous Information and GMM to Estimate Threshold Parameters in TAR Models," Working Papers 494, Queen Mary University of London, School of Economics and Finance.
    20. Paolo Giordani, 2006. "A cautionary note on outlier robust estimation of threshold models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 37-47.
    21. Novella Maugeri, 2014. "Some Pitfalls in Smooth Transition Models Estimation: A Monte Carlo Study," Computational Economics, Springer;Society for Computational Economics, vol. 44(3), pages 339-378, October.
    22. Ahmad Jameel Khadaroo, 2016. "Current Account Deficit in Mauritius: Risks and Prospects," South African Journal of Economics, Economic Society of South Africa, vol. 84(1), pages 109-128, March.
    23. Anna Staszewska-Bystrova & Peter Winker, 2016. "Improved bootstrap prediction intervals for SETAR models," Statistical Papers, Springer, vol. 57(1), pages 89-98, March.

  124. Blake, Andrew P. & Kapetanios, George, 2000. "A radial basis function artificial neural network test for ARCH," Economics Letters, Elsevier, vol. 69(1), pages 15-23, October.
    See citations under working paper version above.

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