IDEAS home Printed from https://ideas.repec.org/e/c/pin18.html
   My authors  Follow this author

Atsushi Inoue

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. Yasuo Hirose & Atsushi Inoue, 2014. "The zero lower bound and parameter bias in an estimated DSGE model," Vanderbilt University Department of Economics Working Papers 14-00009, Vanderbilt University Department of Economics.

    Mentioned in:

    1. The zero lower bound and parameter bias in an estimated DSGE model
      by Christian Zimmermann in NEP-DGE blog on 2015-05-06 01:00:23

Working papers

  1. Atsushi Inoue & Tong Li & Qi Xu, 2021. "Two Sample Unconditional Quantile Effect," Papers 2105.09445, arXiv.org.

    Cited by:

    1. Julián Martínez-Iriarte & Gabriel Montes-Rojas & Yixiao Sun, 2022. "Location-Scale and Compensated Effects in Unconditional Quantile Regressions," Working Papers 127, Red Nacional de Investigadores en Economía (RedNIE).
    2. Montes Rojas Gabriel & Alejo Javier & Galvao Antonio & Martínez-Iriarte Julián, 2023. "Unconditional Quantile Partial Effects via Conditional Quantile Regression," Asociación Argentina de Economía Política: Working Papers 4674, Asociación Argentina de Economía Política.
    3. Martinez-Iriarte, Julian & Sun, Yixiao, 2024. "Identification and estimation of unconditional policy effects of an endogenous binary treatment: An unconditional MTE approach," Journal of Econometrics, Elsevier, vol. 244(1).
    4. Julian Martinez-Iriarte & Gabriel Montes-Rojas & Yixiao Sun, 2022. "Unconditional Effects of General Policy Interventions," Papers 2201.02292, arXiv.org, revised Jul 2023.

  2. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.

    Cited by:

    1. Andres–Escayola, Erik & Berganza, Juan Carlos & Campos, Rodolfo G. & Molina, Luis, 2023. "A BVAR toolkit to assess macrofinancial risks in Brazil and Mexico," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(1).
    2. Lutz Kilian, 2019. "Facts and Fiction in Oil Market Modeling," CESifo Working Paper Series 7902, CESifo.
    3. Bruna, Karel & Van Tran, Quang, 2023. "Asymmetric effects of oil price shocks on EUR/USD exchange rate and structural shock decomposition in a BVAR model with sign restriction," Energy Economics, Elsevier, vol. 128(C).
    4. Martin Stuermer, 2022. "Non-renewable resource extraction over the long term: empirical evidence from global copper production," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(3), pages 617-625, December.
    5. Katarzyna Budnik & Gerhard Rünstler, 2023. "Identifying structural VARs from sparse narrative instruments: Dynamic effects of US macroprudential policies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 186-201, March.
    6. Khalil, Makram & Weber, Marc-Daniel, 2022. "Chinese supply chain shocks," Discussion Papers 44/2022, Deutsche Bundesbank.
    7. Jochen Güntner & Magnus Reif & Maik Wolters, 2024. "Sudden stop: Supply and demand shocks in the German natural gas market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1282-1300, November.
    8. Luca Baldo & Elisa Bonifacio & Marco Brandi & Michelina Lo Russo & Gianluca Maddaloni & Andrea Nobili & Giorgia Rocco & Gabriele Sene & Massimo Valentini, 2021. "Inside the black box: tools for understanding cash circulation," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 7, Bank of Italy, Directorate General for Markets and Payment System.
    9. Boer, Lukas & Pescatori, Andrea & Stuermer, Martin, 2021. "Energy Transition Metals," MPRA Paper 110364, University Library of Munich, Germany.
    10. Szafranek, Karol & Szafrański, Grzegorz & Leszczyńska-Paczesna, Agnieszka, 2024. "Inflation returns. Revisiting the role of external and domestic shocks with Bayesian structural VAR," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 789-810.
    11. Breitenlechner, Max & Georgiadis, Georgios & Schumann, Ben, 2022. "What goes around comes around: How large are spillbacks from US monetary policy?," Journal of Monetary Economics, Elsevier, vol. 131(C), pages 45-60.
    12. Kilian, Lutz, 2020. "Understanding the Estimation of Oil Demand and Oil Supply Elasticities," CEPR Discussion Papers 15244, C.E.P.R. Discussion Papers.
    13. Inoue, Atsushi & Kilian, Lutz, 2020. "Joint Bayesian inference about impulse responses in VAR models," CFS Working Paper Series 650, Center for Financial Studies (CFS).
    14. Paul Carrillo‐Maldonado, 2023. "Partial identification for growth regimes: The case of Latin American countries," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 557-583, July.
    15. Ronicle, David, 2022. "Turning in the widening gyre: monetary and fiscal policy in interwar Britain," Bank of England working papers 968, Bank of England.
    16. Carrillo-Maldonado, Paul & Díaz-Cassou, Javier, 2023. "An anatomy of external shocks in the Andean region," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    17. Diegel, Max & Nautz, Dieter, 2021. "Long-term inflation expectations and the transmission of monetary policy shocks: Evidence from a SVAR analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 130(C).
    18. Herwartz, Helmut & Wang, Shu, 2023. "Point estimation in sign-restricted SVARs based on independence criteria with an application to rational bubbles," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).

  3. Atsushi Inoue & Lutz Kilian, 2020. "Joint Bayesian Inference about Impulse Responses in VAR Models," Working Papers 2022, Federal Reserve Bank of Dallas.

    Cited by:

    1. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2023. "Identification and Inference under Narrative Restrictions," RBA Research Discussion Papers rdp2023-07, Reserve Bank of Australia.
    2. Lutz Kilian & Xiaoqing Zhou, 2019. "Oil Prices, Exchange Rates and Interest Rates," Working Papers 1914, Federal Reserve Bank of Dallas.
    3. Lutz Kilian & Xiaoqing Zhou, 2021. "The Impact of Rising Oil Prices on U.S. Inflation and Inflation Expectations in 2020-23," CESifo Working Paper Series 9455, CESifo.
    4. Finck, David & Tillmann, Peter, 2022. "The macroeconomic effects of global supply chain disruptions," BOFIT Discussion Papers 14/2022, Bank of Finland Institute for Emerging Economies (BOFIT).
    5. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.
    6. Gao, Jiti & Peng, Bin & Wu, Wei Biao & Yan, Yayi, 2024. "Time-varying multivariate causal processes," Journal of Econometrics, Elsevier, vol. 240(1).
    7. Kilian, Lutz & Zhou, Xiaoqing, 2023. "A broader perspective on the inflationary effects of energy price shocks," Energy Economics, Elsevier, vol. 125(C).
    8. Kilian, Lutz & Nomikos, Nikos K. & Zhou, Xiaoqing, 2020. "A quantitative model of the oil tanker market in the Arabian Gulf," CFS Working Paper Series 648, Center for Financial Studies (CFS).
    9. Berger, Tino & Richter, Julia & Wong, Benjamin, 2021. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," University of Göttingen Working Papers in Economics 415, University of Goettingen, Department of Economics.
    10. Jochen Güntner & Magnus Reif & Maik Wolters, 2024. "Sudden stop: Supply and demand shocks in the German natural gas market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1282-1300, November.
    11. Kilian, Lutz & Zhou, Xiaoqing, 2023. "Oil Price Shocks and Inflation," CEPR Discussion Papers 18416, C.E.P.R. Discussion Papers.
    12. Lucrezia Reichlin & Giovanni Ricco & Matthieu Tarbé, 2021. "Monetary-Fiscal Crosswinds in the European Monetary Union," SciencePo Working papers Main hal-03474950, HAL.
    13. Kilian, Lutz, 2023. "How to Construct Monthly VAR Proxies Based on Daily Futures Market Surprises," CEPR Discussion Papers 18348, C.E.P.R. Discussion Papers.
    14. Szafranek, Karol & Szafrański, Grzegorz & Leszczyńska-Paczesna, Agnieszka, 2024. "Inflation returns. Revisiting the role of external and domestic shocks with Bayesian structural VAR," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 789-810.
    15. Gründler, Daniel, 2024. "Does the inflation pass-through of gasoline price shocks depend on the level of inflation?," Economics Letters, Elsevier, vol. 243(C).
    16. Paul Carrillo‐Maldonado, 2023. "Partial identification for growth regimes: The case of Latin American countries," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 557-583, July.
    17. Kilian, Lutz, 2024. "How to construct monthly VAR proxies based on daily surprises in futures markets," Journal of Economic Dynamics and Control, Elsevier, vol. 168(C).
    18. Mohamad B. Karaki & Andrios Neaimeh, 2024. "Do higher global oil and wheat prices matter for the wheat flour price in Lebanon?," Agricultural Economics, International Association of Agricultural Economists, vol. 55(4), pages 559-571, July.
    19. Benk, Szilard & Gillman, Max, 2023. "Identifying money and inflation expectation shocks to real oil prices," Energy Economics, Elsevier, vol. 126(C).
    20. Diab, Sara & Karaki, Mohamad B., 2023. "Do increases in gasoline prices cause higher food prices?," Energy Economics, Elsevier, vol. 127(PB).
    21. Diegel, Max & Nautz, Dieter, 2021. "Long-term inflation expectations and the transmission of monetary policy shocks: Evidence from a SVAR analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 130(C).
    22. Ding, Shusheng & Wang, Anqi & Cui, Tianxiang & Du, Anna Min & Zhou, Xinmiao, 2024. "Commodity market stability and sustainable development: The effect of public health policies," Research in International Business and Finance, Elsevier, vol. 70(PB).
    23. Chen, Zhengyang & Valcarcel, Victor J., 2025. "Modeling inflation expectations in forward-looking interest rate and money growth rules," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 170, pages 1-21.
    24. Finck, David & Tillmann, Peter, 2023. "The macroeconomic effects of global supply chain disruptions," IMFS Working Paper Series 178, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    25. Bruns, Martin, 2021. "Proxy Vector Autoregressions in a Data-rich Environment," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    26. Lukas Berend & Jan Pruser, 2024. "The Transmission of Monetary Policy via Common Cycles in the Euro Area," Papers 2410.05741, arXiv.org, revised Nov 2024.
    27. Lutz Kilian, 2025. "Impulse Response Diagnostics for Priors on Parameters in Structural Vector Autoregressions," Working Papers 2507, Federal Reserve Bank of Dallas.
    28. Max Breitenlechner & Martin Geiger & Daniel Gründler & Johann Scharler, 2024. "Sequencing the COVID‐19 Recession in the USA: What Were the Macroeconomic Drivers?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 119-136, February.

  4. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2019. "Confidence Intervals for Bias and Size Distortion in IV and Local Projections–IV Models," Working Papers 1077, Barcelona School of Economics.

    Cited by:

    1. Barbara Rossi, 2018. "Identifying and estimating the effects of unconventional monetary policy in the data: How to do It and what have we learned?," Economics Working Papers 1641, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2020.

  5. Atsushi Inoue & Barbara Rossi, 2019. "A New Approach to Measuring Economic Policy Shocks, with an Application to Conventional and Unconventional Monetary Policy," Working Papers 1082, Barcelona School of Economics.

    Cited by:

    1. Yoosoon Chang & Ana Maria Herrera & Elena Pesavento, 2023. "Oil Prices Uncertainty, Endogenous Regime Switching, and Inflation Anchoring," CAEPR Working Papers 2023-002 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    2. Yuriy Kitsul & Oleg Sokolinskiy & Jonathan H. Wright, 2022. "Market Effects of Central Bank Credit Markets Support Programs in Europe," International Finance Discussion Papers 1357, Board of Governors of the Federal Reserve System (U.S.).
    3. Jamie Cross & Lennart Hoogerheide & Paul Labonne & Herman K. van Dijk, 2023. "Bayesian Mode Inference for Discrete Distributions in Economics and Finance," Tinbergen Institute Discussion Papers 23-038/III, Tinbergen Institute.
    4. Rasmus Fatum & Naoko Hara & Yohei Yamamoto, 2019. "Negative Interest Rate Policy and the Influence of Macroeconomic News on Yields," Globalization Institute Working Papers 354, Federal Reserve Bank of Dallas.
    5. Luisa Corrado & Daniela Fantozzi & Simona Giglioli, 2022. "Real-time ineuqalities and policies during the pandemic in the US," Temi di discussione (Economic working papers) 1396, Bank of Italy, Economic Research and International Relations Area.
    6. Mathias Krogh & Giovanni Pellegrino, "undated". "Real Activity and Uncertainty Shocks: The Long and the Short of It," "Marco Fanno" Working Papers 0310, Dipartimento di Scienze Economiche "Marco Fanno".
    7. Lance A. Fisher & Hyeon-seung Huh, 2022. "Systematic Monetary Policy in a SVAR for Australia," Working papers 2022rwp-194, Yonsei University, Yonsei Economics Research Institute.
    8. Thorsten V. Koeppl & Jeremy M. Kronick & James McNeil, 2024. "Using functional shocks to assess conventional and unconventional monetary policy in Canada," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 57(4), pages 1314-1336, November.
    9. Yoosoon Chang & Fabio Gómez-Rodríguez & Christian Matthes, 2023. "The Influence of Fiscal and Monetary Policies on the Shape of the Yield Curve," CAMA Working Papers 2023-65, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Yoosoon Chang & Yongok Choi & Chang Sik Kim & J. Isaac Miller & Joon Y. Park, 2024. "Common Trends and Country Specific Heterogeneities in Long-Run World Energy Consumption," CAMA Working Papers 2024-04, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. Hilde C. Bjornland & Yoosoon Chang & Jamie L. Cross, 2024. "Oil and the Stock Market Revisited: A Mixed Functional VAR Approach," CAEPR Working Papers 2023-005 Classification-1, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    12. Kota Ikkatai & Takuji Kawamoto & Kenichi Sakura, 2024. "Japan's Unconventional Monetary Policy and the Exchange Rate Dynamics," Bank of Japan Working Paper Series 24-E-23, Bank of Japan.
    13. Atsushi Inoue & Barbara Rossi, 2019. "A New Approach to Measuring Economic Policy Shocks, with an Application to Conventional and Unconventional Monetary Policy," Working Papers 1082, Barcelona School of Economics.
    14. De Santis, Roberto A., 2020. "Impact of the Asset Purchase Programme on euro area government bond yields using market news," Economic Modelling, Elsevier, vol. 86(C), pages 192-209.
    15. Christina Anderl & Guglielmo Maria Caporale, 2024. "Expectations and Speculation in the Natural Gas Markets," CESifo Working Paper Series 11341, CESifo.
    16. Yoosoon Chang & Soyoung Kim & Joon Y. Park, 2025. "How Do Macroaggregates and Income Distribution Interact Dynamically? A Novel Structural Mixed Autoregression with Aggregate and Functional Variables," Working Papers No 01/2025, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    17. Christina Anderl & Guglielmo Maria Caporale, 2023. "Functional Shocks to Inflation Expectations and Real Interest Rates and Their Macroeconomic Effects," CESifo Working Paper Series 10656, CESifo.
    18. Martin, Feldkircher & Thomas, Gruber & Florian, Huber, 2019. "International effects of a compression of euro area yield curves," Working Papers in Economics 2019-1, University of Salzburg.
    19. Stavrakeva, Vania & Tang, Jenny, 2019. "The Dollar During the Great Recession: US Monetary Policy Signaling and The Flight To Safety," CEPR Discussion Papers 14034, C.E.P.R. Discussion Papers.
    20. Ca' Zorzi, Michele & Dedola, Luca & Georgiadis, Georgios & Jarociński, Marek & Stracca, Livio & Strasser, Georg, 2020. "Monetary policy and its transmission in a globalised world," Working Paper Series 2407, European Central Bank.
    21. Robert Adamek & Stephan Smeekes & Ines Wilms, 2024. "Local projection inference in high dimensions," The Econometrics Journal, Royal Economic Society, vol. 27(3), pages 323-342.
    22. Jarociński, Marek, 2021. "Estimating the Fed’s Unconventional Policy Shocks," Working Paper Series 20210, European Central Bank.
    23. Leonardo Nogueira Ferreira, 2023. "Monetary Policy Surprises, Financial Conditions, and the String Theory Revisited," Working Papers Series 573, Central Bank of Brazil, Research Department.
    24. Zoë Venter, 2020. "The Interaction Between Conventional Monetary Policy and Financial Stability: Chile, Colombia, Japan, Portugal and the UK," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 62(3), pages 521-554, September.
    25. Brubakk, Leif & ter Ellen, Saskia & Robstad, Ørjan & Xu, Hong, 2022. "The macroeconomic effects of forward communication," Journal of International Money and Finance, Elsevier, vol. 120(C).
    26. Endong Wang, 2024. "Structural counterfactual analysis in macroeconomics: theory and inference," Papers 2409.09577, arXiv.org.
    27. Eo, Yunjong & Kang, Kyu Ho, 2020. "The effects of conventional and unconventional monetary policy on forecasting the yield curve," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    28. Chunya Bu & John Rogers & Wenbin Wu, 2019. "A Unified Measure of Fed Monetary Policy Shocks," Finance and Economics Discussion Series 2019-043, Board of Governors of the Federal Reserve System (U.S.).
    29. Alisdair McKay & Christian K. Wolf, 2023. "What Can Time‐Series Regressions Tell Us About Policy Counterfactuals?," Econometrica, Econometric Society, vol. 91(5), pages 1695-1725, September.
    30. Robert Kirkby & Huong Ngoc Vu, 2024. "Impacts of Monetary Policy Shocks on Inflation and Output in New Zealand," The Economic Record, The Economic Society of Australia, vol. 100(329), pages 160-187, June.
    31. Christina Anderl & Guglielmo Maria Caporale, 2024. "Functional Oil Price Expectations Shocks and Inflation," CESifo Working Paper Series 10998, CESifo.
    32. Ortega, Eva & Osbat, Chiara, 2020. "Exchange rate pass-through in the euro area and EU countries," Occasional Paper Series 241, European Central Bank.
    33. Rüth, Sebastian K., 2020. "Shifts in monetary policy and exchange rate dynamics: Is Dornbusch's overshooting hypothesis intact, after all?," Journal of International Economics, Elsevier, vol. 126(C).
    34. Shixuan Wang & Rangan Gupta & Matteo Bonato & Oguzhan Cepni, 2022. "The Effects of Conventional and Unconventional Monetary Policy Shocks on US REITs Moments: Evidence from VARs with Functional Shocks," Working Papers 202219, University of Pretoria, Department of Economics.
    35. 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).
    36. Jamie L. Cross & Aubrey Poon & Wenying Yao & Dan Zhu, 2024. "A Constrained Dynamic Nelson-Siegel Model for Monetary Policy Analysis," Working Papers No 06/2024, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    37. Daniel J. Lewis, 2019. "Announcement-Specific Decompositions of Unconventional Monetary Policy Shocks and Their Macroeconomic Effects," Staff Reports 891, Federal Reserve Bank of New York.
    38. Thi Bich Ngoc Tran & Hoang Cam Huong Pham, 2020. "The Spillover Effects of the US Unconventional Monetary Policy: New Evidence from Asian Developing Countries," JRFM, MDPI, vol. 13(8), pages 1-26, July.
    39. Fisher, Lance A. & Huh, Hyeon-seung, 2023. "Systematic monetary policy in a SVAR for Australia," Economic Modelling, Elsevier, vol. 128(C).
    40. Florian Huber & Massimiliano Marcellino & Tommaso Tornese, 2024. "The Distributional Effects of Economic Uncertainty," Papers 2411.12655, arXiv.org.
    41. Oliver Holtemöller & Alexander Kriwoluzky & Boreum Kwak, 2020. "Exchange Rates and the Information Channel of Monetary Policy," Discussion Papers of DIW Berlin 1906, DIW Berlin, German Institute for Economic Research.

  6. Atsushi Inoue & Barbara Rossi, 2019. "The Effects of Conventional and Unconventional Monetary Policy on Exchange Rates," Working Papers 1078, Barcelona School of Economics.

    Cited by:

    1. Bhattarai, Saroj & Chatterjee, Arpita & Park, Woong Yong, 2018. "Effects of US Quantitative Easing on Emerging Market Economies," ADBI Working Papers 803, Asian Development Bank Institute.
    2. Benjamin K. Johannsen & Elmar Mertens, 2016. "A Time Series Model of Interest Rates With the Effective Lower Bound," Finance and Economics Discussion Series 2016-033, Board of Governors of the Federal Reserve System (U.S.).
    3. Refet S. Gürkaynak & Burcin Kisacikoglu & Sang Seok Lee, 2022. "Exchange Rate and Inflation under Weak Monetary Policy: Turkey Verifies Theory," CESifo Working Paper Series 9748, CESifo.
    4. Cañon, Carlos & Gerba, Eddie & Pambira, Alberto & Stoja, Evarist, 2024. "An unconventional FX tail risk story," Journal of International Money and Finance, Elsevier, vol. 148(C).
    5. Julian di Giovanni & Galina Hale, 2020. "Stock Market Spillovers via the Global Production Network: Transmission of U.S. Monetary Policy," Staff Reports 945, Federal Reserve Bank of New York.
    6. Rasmus Fatum & Naoko Hara & Yohei Yamamoto, 2019. "Negative Interest Rate Policy and the Influence of Macroeconomic News on Yields," Globalization Institute Working Papers 354, Federal Reserve Bank of Dallas.
    7. Callum Jones & Mariano Kulish & Daniel M. Rees, 2018. "International Spillovers of Forward Guidance Shocks," IMF Working Papers 2018/114, International Monetary Fund.
    8. Coenen, Günter & Montes-Galdón, Carlos & Saint Guilhem, Arthur & Hutchinson, John & Motto, Roberto, 2022. "Rate forward guidance in an environment of large central bank balance sheets: a Eurosystem stock-taking assessment," Occasional Paper Series 290, European Central Bank.
    9. Holtemöller, Oliver & Kriwoluzky, Alexander & Kwak, Boreum, 2024. "Is there an information channel of monetary policy?," IWH Discussion Papers 17/2020, Halle Institute for Economic Research (IWH), revised 2024.
    10. De Rezende, Rafael B. & Ristiniemi, Annukka, 2018. "A shadow rate without a lower bound constraint," Working Paper Series 355, Sveriges Riksbank (Central Bank of Sweden).
    11. 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.
    12. Philipp Hartman & Frank Smets, 2018. "The European Central Bank’s Monetary Policy during Its First 20 Years," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 49(2 (Fall)), pages 1-146.
    13. Shahriyar Aliev & Evžen Kočenda, 2022. "ECB monetary policy and commodity prices," FFA Working Papers 4.008, Prague University of Economics and Business, revised 21 Jun 2022.
    14. Kota Ikkatai & Takuji Kawamoto & Kenichi Sakura, 2024. "Japan's Unconventional Monetary Policy and the Exchange Rate Dynamics," Bank of Japan Working Paper Series 24-E-23, Bank of Japan.
    15. Gelfer, Sacha & Gibbs, Christopher G., 2023. "Measuring the effects of large-scale asset purchases: The role of international financial markets and the financial accelerator," Journal of International Money and Finance, Elsevier, vol. 131(C).
    16. De Santis, Roberto A., 2020. "Impact of the Asset Purchase Programme on euro area government bond yields using market news," Economic Modelling, Elsevier, vol. 86(C), pages 192-209.
    17. Daisuke Ikeda & Shangshang Li & Sophocles Mavroeidis & Francesco Zanetti, 2020. "Testing the Effectiveness of Unconventional Monetary Policy in Japan and the United States," IMES Discussion Paper Series 20-E-10, Institute for Monetary and Economic Studies, Bank of Japan.
    18. Albagli, Elias & Ceballos, Luis & Claro, Sebastian & Romero, Damian, 2024. "UIP deviations: Insights from event studies," Journal of International Economics, Elsevier, vol. 148(C).
    19. David KRIZEK & Josef BRCAK, 2021. "Support for export as a non-standard Central Bank policy: foreign exchange interventions in the case of the Czech Republic," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 12, pages 191-218, June.
    20. Radeef Chundakkadan & Subash Sasidharan, 2021. "Monetary Policy Announcement and Stock Returns - Evidence From Long-Term Repo Operations in India," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 0(-), pages 1-5.
    21. Luo, Tao & Sun, Huaping & Zhang, Lixia & Bai, Jiancheng, 2024. "Do the dynamics of macroeconomic attention drive the yen/dollar exchange market volatility?," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 597-611.
    22. Silvia Miranda-Agrippino & Tsvetelina Nenova, 2021. "A Tale of Two Global Monetary Policies," Discussion Papers 2117, Centre for Macroeconomics (CFM).
    23. Kerstin Bernoth & Helmut Herwartz & Lasse Trienens, 2024. "Interest Rates, Convenience Yields, and Inflation Expectations: Drivers of US Dollar Exchange Rates," Discussion Papers of DIW Berlin 2100, DIW Berlin, German Institute for Economic Research.
    24. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
    25. Luisa Corrado & Stefano Grassi & Enrico Minnella, 2021. "The Transmission Mechanism of Quantitative Easing: A Markov-Switching FAVAR Approach," CEIS Research Paper 520, Tor Vergata University, CEIS, revised 21 Oct 2021.
    26. Ryuzo Miyao & Tatsuyoshi Okimoto, 2020. "Regime shifts in the effects of Japan’s unconventional monetary policies," Manchester School, University of Manchester, vol. 88(6), pages 749-772, December.
    27. Martin, Feldkircher & Thomas, Gruber & Florian, Huber, 2019. "International effects of a compression of euro area yield curves," Working Papers in Economics 2019-1, University of Salzburg.
    28. Stylianos Asimakopoulos & Marco Lorusso & Francesco Ravazzolo, 2023. "A Bayesian DSGE Approach to Modelling Cryptocurrency"," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 1012-1035, December.
    29. Kerstin Bernoth & Helmut Herwartz & Lasse Trienens, 2023. "The Impacts of Global Risk and US Monetary Policy on US Dollar Exchange Rates and Excess Currency Returns," Discussion Papers of DIW Berlin 2037, DIW Berlin, German Institute for Economic Research.
    30. Pinchetti, Marco & Szczepaniak, Andrzej, 2021. "Global spillovers of the Fed information effect," Bank of England working papers 952, Bank of England.
    31. Maximilian Böck & Martin Feldkircher & Pierre L. Siklos, 2021. "International Effects of Euro Area Forward Guidance," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(5), pages 1066-1110, October.
    32. Fornero, Jorge & Kirchner, Markus & Molina, Carlos, 2024. "Estimating shadow policy rates in a small open economy and the role of foreign factors," Journal of International Money and Finance, Elsevier, vol. 140(C).
    33. Stavrakeva, Vania & Tang, Jenny, 2019. "The Dollar During the Great Recession: US Monetary Policy Signaling and The Flight To Safety," CEPR Discussion Papers 14034, C.E.P.R. Discussion Papers.
    34. Martin Feldkircher & Florian Huber, 2016. "Unconventional US Monetary Policy: New Tools, Same Channels?," Department of Economics Working Papers wuwp222, Vienna University of Economics and Business, Department of Economics.
    35. Dalhaus, Tatjana & Schaumburg, Julia & Sekhposyan, Tatevik, 2021. "Networking the yield curve: implications for monetary policy," Working Paper Series 2532, European Central Bank.
    36. Jean-Guillaume Sahuc & Grégory Levieuge & José Garcia-Revelo, 2024. "Revisiting 15 Years of Unusual Transatlantic Monetary Policies," Working Papers hal-04563708, HAL.
    37. Ca' Zorzi, Michele & Dedola, Luca & Georgiadis, Georgios & Jarociński, Marek & Stracca, Livio & Strasser, Georg, 2020. "Monetary policy and its transmission in a globalised world," Working Paper Series 2407, European Central Bank.
    38. Daniel Gründler & Eric Mayer & Johann Scharler, 2023. "Monetary Policy Announcements, Information Shocks, and Exchange Rate Dynamics," Open Economies Review, Springer, vol. 34(2), pages 341-369, April.
    39. Teti̇k, Metin, 2020. "Testing of leader-follower interaction between fed and emerging countries’ central banks," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    40. Ritsu Yano & Yoshiyuki Nakazono & Kento Tango, 2024. "The Transmission of Monetary Policy Shocks: Evidence from Japan," TUPD Discussion Papers 57, Graduate School of Economics and Management, Tohoku University.
    41. Gábor Dávid Kiss & Mercédesz Mészáros, 2020. "Gravity Among Central Bank Balance Sheets: Monetary Policy Spill-Over on FX Volatility," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 5(1), pages 33-57, June.
    42. Zoë Venter, 2020. "The Interaction Between Conventional Monetary Policy and Financial Stability: Chile, Colombia, Japan, Portugal and the UK," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 62(3), pages 521-554, September.
    43. Brubakk, Leif & ter Ellen, Saskia & Robstad, Ørjan & Xu, Hong, 2022. "The macroeconomic effects of forward communication," Journal of International Money and Finance, Elsevier, vol. 120(C).
    44. Lucélia Vaz & Rodrigo Raad, 2021. "Functional data analysis for brazilian term structure of interest rate," Textos para Discussão Cedeplar-UFMG 638, Cedeplar, Universidade Federal de Minas Gerais.
    45. Kortela, Tomi & Nelimarkka, Jaakko, 2020. "The effects of conventional and unconventional monetary policy: Identification through the yield curve," Bank of Finland Research Discussion Papers 3/2020, Bank of Finland.
    46. Eo, Yunjong & Kang, Kyu Ho, 2020. "The effects of conventional and unconventional monetary policy on forecasting the yield curve," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    47. Chunya Bu & John Rogers & Wenbin Wu, 2019. "A Unified Measure of Fed Monetary Policy Shocks," Finance and Economics Discussion Series 2019-043, Board of Governors of the Federal Reserve System (U.S.).
    48. Yusuke Tanahara & Kento Tango & Yoshiyuki Nakazono, 2023. "Information Effects of Monetary Policy," TUPD Discussion Papers 41, Graduate School of Economics and Management, Tohoku University.
    49. Carlos Esteban Posada, 2023. "Inflation targeting strategy and its credibility," Papers 2301.11207, arXiv.org.
    50. Nagao, Ryoya & Kondo, Yoshihiro & Nakazono, Yoshiyuki, 2021. "The macroeconomic effects of monetary policy: Evidence from Japan," Journal of the Japanese and International Economies, Elsevier, vol. 61(C).
    51. Enzo Rossi & Vincent Wolff, 2020. "Spillovers to exchange rates from monetary and macroeconomic communications events," Working Papers 2020-18, Swiss National Bank.
    52. Schmitt-Grohé, Stephanie & Uribe, Martín, 2022. "The effects of permanent monetary shocks on exchange rates and uncovered interest rate differentials," Journal of International Economics, Elsevier, vol. 135(C).
    53. Prabheesh, K.P. & Padhan, Rakesh & Bhat, Javed Ahmad, 2024. "Do financial markets react to emerging economies’ asset purchase program? Evidence from the COVID-19 pandemic period," Journal of Asian Economics, Elsevier, vol. 90(C).
    54. Chaturvedi, Priya & Kumar, Kuldeep, 2022. "Econometric modelling of exchange rate volatility using mixed-frequency data," MPRA Paper 115222, University Library of Munich, Germany.
    55. Dossani, Asad, 2024. "Monetary policy and currency variance risk premia," Research in International Business and Finance, Elsevier, vol. 69(C).
    56. Ortega, Eva & Osbat, Chiara, 2020. "Exchange rate pass-through in the euro area and EU countries," Occasional Paper Series 241, European Central Bank.
    57. Itamar Caspi & Amit Friedman & Sigal Ribon, 2024. "Shocks and Currents: Monetary Policy and Israel’s Foreign Exchange Market," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 66(3), pages 454-481, September.
    58. Kim, Kyoung-Gon, 2022. "Financial Crisis and the Global Transmission of U.S. Monetary Policy Surprises," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 63(2), pages 104-125, December.
    59. Rüth, Sebastian K., 2020. "Shifts in monetary policy and exchange rate dynamics: Is Dornbusch's overshooting hypothesis intact, after all?," Journal of International Economics, Elsevier, vol. 126(C).
    60. Shixuan Wang & Rangan Gupta & Matteo Bonato & Oguzhan Cepni, 2022. "The Effects of Conventional and Unconventional Monetary Policy Shocks on US REITs Moments: Evidence from VARs with Functional Shocks," Working Papers 202219, University of Pretoria, Department of Economics.
    61. Behera, Harendra & Gunadi, Iman & Rath, Badri Narayan, 2023. "COVID-19 uncertainty, financial markets and monetary policy effects in case of two emerging Asian countries," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 173-189.
    62. Yang, Jinyu & Dong, Dayong & Liang, Chao & Cao, Yang, 2024. "Monetary policy uncertainty and the price bubbles in energy markets," Energy Economics, Elsevier, vol. 133(C).
    63. Meng, Xiangcai & Huang, Chia-Hsing, 2021. "The time-frequency analysis of conventional and unconventional monetary policy: Evidence from Japan," Japan and the World Economy, Elsevier, vol. 59(C).
    64. Karau, Sören, 2024. "Relative monetary policy and exchange rates," Discussion Papers 40/2024, Deutsche Bundesbank.
    65. Ur Rehman, Mobeen & Al Rababa'a, Abdel Razzaq & El-Nader, Ghaith & Alkhataybeh, Ahmad & Vo, Xuan Vinh, 2022. "Modelling the quantile cross-coherence between exchange rates: Does the COVID-19 pandemic change the interlinkage structure?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    66. Daniel J. Lewis, 2019. "Announcement-Specific Decompositions of Unconventional Monetary Policy Shocks and Their Macroeconomic Effects," Staff Reports 891, Federal Reserve Bank of New York.
    67. Thi Bich Ngoc Tran & Hoang Cam Huong Pham, 2020. "The Spillover Effects of the US Unconventional Monetary Policy: New Evidence from Asian Developing Countries," JRFM, MDPI, vol. 13(8), pages 1-26, July.
    68. Wan Wei & Susan Pozo & Evan Lau, 2021. "The effects of conventional and unconventional monetary policy on exchange rate volatility," Cogent Economics & Finance, Taylor & Francis Journals, vol. 9(1), pages 1997425-199, January.
    69. Shang, Fei, 2022. "The effect of uncertainty on the sensitivity of the yield curve to monetary policy surprises," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    70. Mirela Miescu, 2022. "Forward guidance shocks," Working Papers 352591340, Lancaster University Management School, Economics Department.
    71. Oliver Holtemöller & Alexander Kriwoluzky & Boreum Kwak, 2020. "Exchange Rates and the Information Channel of Monetary Policy," Discussion Papers of DIW Berlin 1906, DIW Berlin, German Institute for Economic Research.
    72. Wei, Xiaoyun & Han, Liyan, 2021. "The impact of COVID-19 pandemic on transmission of monetary policy to financial markets," International Review of Financial Analysis, Elsevier, vol. 74(C).

  7. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.

    Cited by:

    1. Li, Dake & Plagborg-Møller, Mikkel & Wolf, Christian K., 2024. "Local projections vs. VARs: Lessons from thousands of DGPs," Journal of Econometrics, Elsevier, vol. 244(2).
    2. Atsushi Inoue & `Oscar Jord`a & Guido M. Kuersteiner, 2023. "Inference for Local Projections," Papers 2306.03073, arXiv.org, revised Aug 2024.
    3. Andre Harrison & Annika Segelhorst, 2024. "Do consumer price indices in oil-producing economies respond differently to oil market shocks? Evidence from Canada," Empirical Economics, Springer, vol. 67(5), pages 2039-2076, November.
    4. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    5. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Inference in Non-stationary High-Dimensional VARs," Papers 2302.01434, arXiv.org, revised Sep 2023.
    6. Ke-Li Xu, 2022. "On Local Projection Based Inference," CAEPR Working Papers 2022-002 Classification-, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    7. Yuanyuan Li & Dietmar Bauer, 2020. "Modeling I(2) Processes Using Vector Autoregressions Where the Lag Length Increases with the Sample Size," Econometrics, MDPI, vol. 8(3), pages 1-28, September.
    8. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    9. Jean-Marie Dufour & Endong Wang, 2024. "Simple robust two-stage estimation and inference for generalized impulse responses and multi-horizon causality," Papers 2409.10820, arXiv.org.
    10. Lutz Kilian & Xiaoqing Zhou, 2020. "The Econometrics of Oil Market VAR Models," Working Papers 2006, Federal Reserve Bank of Dallas.
    11. Olatunji Abdul Shobande & Joseph Onuche Enemona, 2021. "A Multivariate VAR Model for Evaluating Sustainable Finance and Natural Resource Curse in West Africa: Evidence from Nigeria and Ghana," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    12. Dake Li & Mikkel Plagborg-M{o}ller & Christian K. Wolf, 2021. "Local Projections vs. VARs: Lessons From Thousands of DGPs," Papers 2104.00655, arXiv.org, revised Jan 2024.
    13. Òscar Jordà & Alan M. Taylor, 2024. "Local Projections," NBER Working Papers 32822, National Bureau of Economic Research, Inc.
    14. Ke-Li Xu, 2023. "Local Projection Based Inference under General Conditions," CAEPR Working Papers 2023-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

  8. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2018. "Confidence intervals for bias and size distortion in IV and local projections — IV models," Working Papers 1841, Banco de España.

    Cited by:

    1. Daniel J. Lewis & Karel Mertens, 2022. "A Robust Test for Weak Instruments for 2SLS with Multiple Endogenous Regressors," Working Papers 2208, Federal Reserve Bank of Dallas, revised 26 Sep 2024.
    2. Barbara Rossi & Atsushi Inoue & Yiru Wang, 2024. "Has the Phillips curve flattened?," French Stata Users' Group Meetings 2024 22, Stata Users Group.
    3. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2018. "Confidence intervals for bias and size distortion in IV and local projections — IV models," Working Papers 1841, Banco de España.
    4. Rossi, Barbara, 2019. "Identifying and Estimating the Effects of Unconventional Monetary Policy: How to Do It And What Have We Learned?," CEPR Discussion Papers 14064, C.E.P.R. Discussion Papers.
    5. Germano Ruisi, 2019. "Time-Varying Local Projections," Working Papers 891, Queen Mary University of London, School of Economics and Finance.
    6. Barbara Rossi, 2018. "Identifying and estimating the effects of unconventional monetary policy in the data: How to do It and what have we learned?," Economics Working Papers 1641, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2020.
    7. Daniel J. Lewis & Karel Mertens, 2022. "A Robust Test for Weak Instruments with Multiple Endogenous Regressors," Staff Reports 1020, Federal Reserve Bank of New York.
    8. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    9. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "The First-stage F Test with Many Weak Instruments," Papers 2302.14423, arXiv.org, revised Sep 2024.

  9. Pablo Guerron-Quintana & Atsushi Inoue & Lutz Kilian, 2016. "Impulse Response Matching Estimators for DSGE Models," CESifo Working Paper Series 5730, CESifo.

    Cited by:

    1. Povilas Lastauskas & Julius Stakėnas, 2022. "Dancing Alone or Together: The Dynamic Effects of Independent and Common Monetary Policies," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 217-241, Emerald Group Publishing Limited.
    2. Minford, Patrick & Wickens, Michael R. & Xu, Yongdeng, 2017. "Comparing different data descriptors in Indirect Inference tests on DSGE models," CEPR Discussion Papers 11816, C.E.P.R. Discussion Papers.
    3. Atsushi Inoue & Lutz Kilian, 2016. "Joint Confidence Sets for Structural Impulse Responses," CESifo Working Paper Series 5746, CESifo.
    4. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2021. "Uncertainty and Monetary Policy during the Great Recession," Economics Working Papers 2021-05, Department of Economics and Business Economics, Aarhus University.
    5. 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.
    6. Ruge-Murcia, Francisco, 2020. "Estimating nonlinear dynamic equilibrium models by matching impulse responses," Economics Letters, Elsevier, vol. 197(C).
    7. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    8. Meenagh, David & Minford, Patrick & Xu, Yongdeng, 2022. "Targeting moments for calibration compared with indirect inference," Cardiff Economics Working Papers E2022/12, Cardiff University, Cardiff Business School, Economics Section.
    9. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "Testing DSGE Models by indirect inference: a survey of recent findings," Cardiff Economics Working Papers E2018/14, Cardiff University, Cardiff Business School, Economics Section.
    10. Cavicchioli, Maddalena, 2024. "A matrix unified framework for deriving various impulse responses in Markov switching VAR: Evidence from oil and gas markets," The Journal of Economic Asymmetries, Elsevier, vol. 29(C).
    11. Jesus Fernandez-Villaverde & Juan Rubio-Ramírez & Frank Schorfheide, 2015. "Solution and Estimation Methods for DSGE Models," PIER Working Paper Archive 15-042, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2015.
    12. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2020. "Uncertainty and Monetary Policy during Extreme Events," Economics Working Papers 2020-11, Department of Economics and Business Economics, Aarhus University.
    13. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "The small sample properties of Indirect Inference in testing and estimating DSGE models," Cardiff Economics Working Papers E2018/7, Cardiff University, Cardiff Business School, Economics Section.
    14. Mario Martinoli & Alessio Moneta & Gianluca Pallante, 2022. "Calibration and Validation of Macroeconomic Simulation Models by Statistical Causal Search," LEM Papers Series 2022/33, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    15. Ryan Chahrour & Sanjay K. Chugh & Tristan Potter, 2023. "Anticipated productivity and the labor market," Quantitative Economics, Econometric Society, vol. 14(3), pages 897-934, July.
    16. Lucy Minford & David Meenagh, 2020. "Supply-Side Policy and Economic Growth: A Case Study of the UK," Open Economies Review, Springer, vol. 31(1), pages 159-193, February.
    17. Chatterjee, Pratiti, 2024. "Uncertainty shocks, financial frictions, and business cycle asymmetries across countries," European Economic Review, Elsevier, vol. 162(C).
    18. Angelini, Giovanni & Sorge, Marco M., 2021. "Under the same (Chole)sky: DNK models, timing restrictions and recursive identification of monetary policy shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    19. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    20. Inês da Cunha Cabral & João Nicolau, 2022. "Inflation in the G7 and the expected time to reach the reference rate: A nonparametric approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1608-1620, April.
    21. Minford, Patrick & Xu, Yongdeng, 2024. "Indirect Inference- a methodological essay on its role and applications," Cardiff Economics Working Papers E2024/1, Cardiff University, Cardiff Business School, Economics Section.
    22. Massimo Ferrari Minesso & Maria Sole Pagliari, 2022. "DSGE Nash: solving Nash Games in Macro Models With an application to optimal monetary policy under monopolistic commodity pricing," Working papers 884, Banque de France.
    23. Meenagh, David & Minford, Patrick & Xu, Yongdeng, 2023. "Indirect Inference and Small Sample Bias - Some Recent Results," Cardiff Economics Working Papers E2023/15, Cardiff University, Cardiff Business School, Economics Section.
    24. Òscar Jordà & Alan M. Taylor, 2024. "Local Projections," NBER Working Papers 32822, National Bureau of Economic Research, Inc.
    25. Ferrari Minesso, Massimo & Pagliari, Maria Sole, 2022. "DSGE Nash: solving Nash games in macro models," Working Paper Series 2678, European Central Bank.
    26. Daniil Lomonosov, 2023. "Shocks of Business Activity and Specific Shocks to Oil Market in DSGE Model of Russian Economy and Their Influence Under Different Monetary Policy Regimes," Russian Journal of Money and Finance, Bank of Russia, vol. 82(4), pages 44-79, December.
    27. Lin, Boqiang & Xu, Bin, 2018. "Growth of industrial CO2 emissions in Shanghai city: Evidence from a dynamic vector autoregression analysis," Energy, Elsevier, vol. 151(C), pages 167-177.
    28. Zviadadze, Irina, 2018. "Term Structure of Risk in Expected Returns," CEPR Discussion Papers 13414, C.E.P.R. Discussion Papers.
    29. Giulia Piccillo & Poramapa Poonpakdee, 2023. "Ambiguous Business Cycles, Recessions and Uncertainty: A Quantitative Analysis," CESifo Working Paper Series 10646, CESifo.
    30. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.

  10. Atsushi Inoue & Lutz Kilian, 2016. "Joint Confidence Sets for Structural Impulse Responses," CESifo Working Paper Series 5746, CESifo.

    Cited by:

    1. Pablo Guerron-Quintana & Atsushi Inoue & Lutz Kilian, 2016. "Impulse Response Matching Estimators for DSGE Models," CESifo Working Paper Series 5730, CESifo.
    2. Nikola Kutin & Zakaria Moussa & Thomas Vallée, 2018. "Factors behind the Freight Rates in the Liner Shipping Industry," Working Papers halshs-01828633, HAL.
    3. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.
    4. Atsushi Inoue & Òscar Jordà & Guido M. Kuersteiner, 2023. "Significance Bands for Local Projections," Working Paper Series 2023-15, Federal Reserve Bank of San Francisco.
    5. Anna Staszewska-Bystrova & Peter Winker, 2014. "Measuring Forecast Uncertainty of Corporate Bond Spreads by Bonferroni-Type Prediction Bands," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(2), pages 89-104, June.
    6. 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.
    7. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    8. Paul Beaudry & Fabrice Collard & Patrick Feve & Alain Guay & Franck Portier, 2022. "Dynamic Identification in VARs," Working Papers 22-08, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    9. Carsten Trenkler & Enzo Weber, 2020. "Identifying shocks to business cycles with asynchronous propagation," Empirical Economics, Springer, vol. 58(4), pages 1815-1836, April.
    10. Atsushi Inoue & `Oscar Jord`a & Guido M. Kuersteiner, 2023. "Inference for Local Projections," Papers 2306.03073, arXiv.org, revised Aug 2024.
    11. Kholodilin, Konstantin A. & Netsunajev, Aleksei, 2017. "Crimea and punishment: the impact of sanctions on Russian and European economies," Bank of Estonia Working Papers wp2017-5, Bank of Estonia, revised 11 Sep 2017.
    12. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2018. "Calculating joint confidence bands for impulse response functions using highest density regions," Empirical Economics, Springer, vol. 55(4), pages 1389-1411, December.
    13. Daniel Grabowski & Anna Staszewska-Bystrova & Peter Winker, 2020. "Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 5-32, March.
    14. Jonas E. Arias & Juan F. Rubio-Ramirez & Daniel F. Waggoner, 2020. "Uniform Priors for Impulse Responses," Working Papers 22-30, Federal Reserve Bank of Philadelphia.
    15. Inoue, Atsushi & Kilian, Lutz, 2020. "Joint Bayesian inference about impulse responses in VAR models," CFS Working Paper Series 650, Center for Financial Studies (CFS).
    16. Paul Carrillo‐Maldonado, 2023. "Partial identification for growth regimes: The case of Latin American countries," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 557-583, July.
    17. Harrison, Andre & Reed, Robert R., 2023. "Gross capital inflows, the U.S. economy, and the response of the Federal Reserve," Journal of International Money and Finance, Elsevier, vol. 139(C).
    18. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    19. Endong Wang, 2024. "Structural counterfactual analysis in macroeconomics: theory and inference," Papers 2409.09577, arXiv.org.
    20. Bojaj, Martin M. & Muhadinovic, Milica & Bracanovic, Andrej & Mihailovic, Andrej & Radulovic, Mladen & Jolicic, Ivan & Milosevic, Igor & Milacic, Veselin, 2022. "Forecasting macroeconomic effects of stablecoin adoption: A Bayesian approach," Economic Modelling, Elsevier, vol. 109(C).
    21. Blazsek, Szabolcs & Licht, Adrian, 2019. "Co-integration and common trends analysis with score-driven models : an application to the federal funds effective rate and US inflation rate," UC3M Working papers. Economics 28451, Universidad Carlos III de Madrid. Departamento de Economía.
    22. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    23. Lieb, Lenard & Smeekes, Stephan, 2017. "Inference for Impulse Responses under Model Uncertainty," Research Memorandum 022, Maastricht University, Graduate School of Business and Economics (GSBE).
    24. 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.
    25. Demetrescu, Matei & Salish, Nazarii, 2024. "(Structural) VAR models with ignored changes in mean and volatility," International Journal of Forecasting, Elsevier, vol. 40(2), pages 840-854.
    26. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    27. Hafner, Christian M. & Herwartz, Helmut & Wang, Shu, 2023. "Causal inference with (partially) independent shocks and structural signals on the global crude oil market," LIDAM Discussion Papers ISBA 2023004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    28. Bojaj, Martin M. & Djurovic, Gordana & Fabris, Nikola & Milovic, Nikola, 2023. "Top 1% and inequality connectedness in the EMU and WB," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 139-155.

  11. Chun-Huong Kuo & Atsushi Inoue & Barbara Rossi, 2015. "Identifying the Sources of Model Misspecification," Working Papers 821, Barcelona School of Economics.

    Cited by:

    1. F. Canova & F. Ferroni & C. Matthes, 2015. "Approximating time varying structural models with time invariant structures," Working papers 578, Banque de France.
    2. Helena Marques & Gabriel Pino & J. D. Tena, 2018. "Voting with your feet: migration flows and happiness," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 9(2), pages 163-187, June.
    3. Francesca Monti, 2015. "Can a data-rich environment help identify the sources of model misspecification?," Discussion Papers 1505, Centre for Macroeconomics (CFM).
    4. Guido Ascari & Qazi Haque & Leandro M. Magnusson & Sophocles Mavroeidis, 2023. "Empirical evidence on the Euler equation for investment in the US," School of Economics and Public Policy Working Papers 2023-05 Classification-C2, University of Adelaide, School of Economics and Public Policy.
    5. Den Haan, Wouter & Drechsel, Thomas, 2018. "Agnostic Structural Disturbances (ASDs): Detecting and Reducing Misspecification in Empirical Macroeconomic Models," CEPR Discussion Papers 13145, C.E.P.R. Discussion Papers.
    6. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    7. Barbara Rossi & Atsushi Inoue & Yiru Wang, 2024. "Has the Phillips curve flattened?," French Stata Users' Group Meetings 2024 22, Stata Users Group.
    8. Filippo Ferroni & Jonas D. M. Fisher & Leonardo Melosi, 2022. "Usual Shocks in our Usual Models," Working Paper Series WP 2022-39, Federal Reserve Bank of Chicago.
    9. Mertens, Elmar, 2023. "Precision-based sampling for state space models that have no measurement error," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    10. Filippo Ferroni & Stefano Grassi & Miguel A. León-Ledesma, 2017. "Selecting Primal Innovations in DSGE models," Working Paper Series WP-2017-20, Federal Reserve Bank of Chicago.
    11. Filippo Ferroni & Stefano Grassi & Miguel A. Leon-Ledesma, 2015. "Fundamental shock selection in DSGE models," Studies in Economics 1508, School of Economics, University of Kent.
    12. Canova, Fabio & Matthes, Christian, 2019. "Dealing with misspecification in structural macroeconometric models," CEPR Discussion Papers 13511, C.E.P.R. Discussion Papers.
    13. Hatcher, Michael & Minford, Patrick, 2023. "Chameleon models in economics: A note," Cardiff Economics Working Papers E2023/10, Cardiff University, Cardiff Business School, Economics Section.
    14. Loria, Francesca & Matthes, Christian & Wang, Mu-Chun, 2022. "Economic theories and macroeconomic reality," Journal of Monetary Economics, Elsevier, vol. 126(C), pages 105-117.

  12. Emily Anderson & Atsushi Inoue & Barbara Rossi, 2015. "Heterogeneous Consumers and Fiscal Policy Shocks," Working Papers 822, Barcelona School of Economics.

    Cited by:

    1. Bernd Hayo & Matthias Uhl, 2015. "Regional effects of federal tax shocks," Southern Economic Journal, John Wiley & Sons, vol. 82(2), pages 343-360, October.
    2. Pedro Brinca & Miguel H. Ferreira & Francesco Franco & Hans A. Holter & Laurence Malafry, 2017. "Fiscal Consolidation Programs and Income Inequality," CEF.UP Working Papers 1703, Universidade do Porto, Faculdade de Economia do Porto.
    3. Kerim Peren Arin & Juan A. Lacomba & Francisco Lagos & Ana I. Moro-Egido & Marcel Thum, 2021. "Socio-Economic Attitudes in the Era of Social Distancing and Lockdowns," CESifo Working Paper Series 8845, CESifo.
    4. Rüth, Sebastian K. & Simon, Camilla, 2022. "How do income and the debt position of households propagate fiscal stimulus into consumption?," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    5. Thorsten Drautzburg & Pooyan Amir-Ahmadi, 2017. "Identification through Heterogeneity," 2017 Meeting Papers 1087, Society for Economic Dynamics.
    6. Klein, Mathias & Winkler, Roland, 2017. "Austerity, Inequality, and Private Debt Overhang," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168076, Verein für Socialpolitik / German Economic Association.
    7. Napoletano, Mauro & Roventini, Andrea & Gaffard, Jean Luc, 2017. "Time-varying fiscal multipliers in an agent-based model with credit rationing," Economics Discussion Papers 2017-112, Kiel Institute for the World Economy (IfW Kiel).
    8. Piotr Krajewski & Agata Szymanska, 2019. "The effectiveness of fiscal policy within business cycle-Ricardians vs. non-Ricardians approach," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 19(2), pages 195-215.
    9. Rossi, Barbara & Inoue, Atsushi & Anderson, Emily, 2013. "Heterogeneous Consumers and Fiscal Policy Shocks," CEPR Discussion Papers 9631, C.E.P.R. Discussion Papers.
    10. Minsu Chang & Frank Schorfheide, 2024. "On the Effects of Monetary Policy Shocks on Income and Consumption Heterogeneity," PIER Working Paper Archive 24-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    11. Agovino, Massimiliano & Ferrara, Maria, 2015. "Disabilità e povertà: il ruolo delle pensioni di invalidità civile. Un'analisi DSGE per i dati italiani [Disability and poverty: the role of civilian disability pensions. A DSGE analysis for Italia," MPRA Paper 65616, University Library of Munich, Germany.
    12. Surico, Paolo & Cloyne, James, 2013. "Household Debt and the Dynamic Effects of Income Tax Changes," CEPR Discussion Papers 9649, C.E.P.R. Discussion Papers.
    13. Ferrara, Maria & Tirelli, Patrizio, 2017. "Equitable fiscal consolidations," Economic Modelling, Elsevier, vol. 61(C), pages 207-223.
    14. Alica Ida Bonk & Laure Simon, 2022. "From He-Cession to She-Stimulus? The labor market impact of fiscal policy across gender," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 309-334, May.
    15. Per Krusell and Laurence Malafry, 2014. "Fiscal Multipliers in the 21st Century," EUI-RSCAS Working Papers p0416, European University Institute (EUI), Robert Schuman Centre of Advanced Studies (RSCAS).
    16. Christian Bredemeier & Falko Juessen & Roland Winkler, 2020. "Fiscal Policy and Occupational Employment Dynamics," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(6), pages 1527-1563, September.
    17. Massimiliano Agovino & Maria Ferrara, 2017. "Can civilian disability pensions overcome the poverty issue? A DSGE analysis for Italian data," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(4), pages 1469-1491, July.
    18. Eunseong Ma, 2023. "Monetary Policy And Inequality: How Does One Affect The Other?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(2), pages 691-725, May.
    19. Wifag Adnan & Kerim Peren Arin & Aysegul Corakci & Nicola Spagnolo, 2022. "On the heterogeneous effects of tax policy on labor market outcomes," Southern Economic Journal, John Wiley & Sons, vol. 88(3), pages 991-1036, January.
    20. Javier Andrés & José E. Boscá & Javier Ferri & Cristina Fuentes-Albero, 2018. "Households' balance sheets and the effect of fiscal policy," Working Papers 1831, Banco de España.
    21. Kopiec, Paweł, 2024. "The aggregate and distributional effects of fiscal stimuli," Economic Modelling, Elsevier, vol. 134(C).
    22. Puonti, Päivi, 2023. "Effective Fiscal Policy in an Aging Economy: Evidence from a BVAR Analysis," ETLA Working Papers 110, The Research Institute of the Finnish Economy.
    23. Grancini, Stefano, 2021. "Risk Aversion and Fiscal Consolidation Programs," MPRA Paper 105500, University Library of Munich, Germany.
    24. Giorgio Motta & Patrizio Tirelli, 2013. "Limited Asset Market Participation, Income Inequality and Macroeconomic Volatility," Working Papers 261, University of Milano-Bicocca, Department of Economics, revised Dec 2013.
    25. Alice Albonico & Alessia Paccagnini & Patrizio Tirelli, 2018. "Limited Asset Market Participation and the Euro Area Crisis. An Empirical DSGE Model," Working Papers 391, University of Milano-Bicocca, Department of Economics, revised Nov 2018.
    26. Alice Albonico & Alessia Paccagnini & Patrizio Tirelli, 2014. "Estimating a DSGE model with Limited Asset Market Participation for the Euro Area," Working Papers 286, University of Milano-Bicocca, Department of Economics, revised Nov 2014.
    27. Laure Simon, 2023. "Fiscal Stimulus and Skill Accumulation over the Life Cycle," Staff Working Papers 23-9, Bank of Canada.
    28. Heer, Burkhard & Scharrer, Christian, 2018. "The age-specific burdens of short-run fluctuations in government spending," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 45-75.
    29. Christian Bredemeier & Falko Juessen & Roland Winkler, 2023. "Bringing Back the Jobs Lost to Covid‐19: The Role of Fiscal Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(7), pages 1703-1747, October.
    30. Bessho, Shun-ichiro, 2021. "Local fiscal multipliers and population aging in Japan," Japan and the World Economy, Elsevier, vol. 60(C).
    31. Steven Fazzari & James Morley & Irina Panovska, 2014. "State-Dependent Effects of Fiscal Policy," Discussion Papers 2012-27C, School of Economics, The University of New South Wales.
    32. Fonseca, Miguel, 2020. "Fiscal Consolidations: Welfare Effects of the Adjustment Speed," MPRA Paper 98902, University Library of Munich, Germany, revised 02 Mar 2020.
    33. Givens, Gregory, 2019. "Unemployment, Partial Insurance, and the Multiplier Effects of Government Spending," MPRA Paper 96811, University Library of Munich, Germany.
    34. Masud Alam, 2024. "Output, employment, and price effects of U.S. narrative tax changes: a factor-augmented vector autoregression approach," Empirical Economics, Springer, vol. 67(4), pages 1421-1471, October.
    35. Animashaun, Jubril & Wossink, Ada, 2024. "How do households cope during aggregate shocks? Evidence from the 2009–2015 oil crisis in Nigeria," Resources Policy, Elsevier, vol. 95(C).
    36. Patrizio Tirelli & Maria Ferrara, 2020. "Disinflation, Inequality, And Welfare In A Tank Model," Economic Inquiry, Western Economic Association International, vol. 58(3), pages 1297-1313, July.
    37. Henrique S. Basso & Omar Rachedi, 2018. "The young, the old, and the government: demographics and fiscal multipliers," Working Papers 1837, Banco de España.
    38. Daniel R. Carroll, 2014. "Why Do Economists Still Disagree over Government Spending Multipliers?," Economic Commentary, Federal Reserve Bank of Cleveland, issue May.
    39. Samuel Federico Kaplan & Arin Kerim Peren & Polyzos Efstathios & Spagnolo Nicola, 2022. "Stock Market Responses to Monetary Policy Shocks: Universal Firm-Level Evidence," Asociación Argentina de Economía Política: Working Papers 4571, Asociación Argentina de Economía Política.
    40. Maria Ferrara & Patrizio Tirelli, 2014. "Fiscal Consolidations: Can We Reap the Gain and Escape the Pain?," Working Papers 283, University of Milano-Bicocca, Department of Economics, revised Oct 2014.
    41. Masud Alam, 2021. "Output, Employment, and Price Effects of U.S. Narrative Tax Changes: A Factor-Augmented Vector Autoregression Approach," Papers 2106.10844, arXiv.org.
    42. Eunseong Ma, 2019. "The Heterogeneous Responses of Consumption between Poor and Rich to Government Spending Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(7), pages 1999-2028, October.
    43. Freitas, Bruno, 2020. "Labour Share Heterogeneity and Fiscal Consolidation Programs," MPRA Paper 98973, University Library of Munich, Germany.
    44. Luisa Corrado & Edgar Silgado-Gómez, 2018. "Assessing the Effects of Fiscal Policy News under Imperfect Information: Evidence from Aggregate and Individual Data," CEIS Research Paper 447, Tor Vergata University, CEIS, revised 06 Nov 2018.
    45. Morita, Hiroshi, 2020. "Fiscal multipliers in the most aged country: Empirical evidence and theoretical interpretation," Discussion paper series HIAS-E-100, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    46. Massimiliano Agovino & Maria Ferrara, 2022. "Disabilit?: diseguaglianza sociale ed economica. Un?analisi empirica e teorica," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2022(1), pages 11-42.
    47. Aursland, Thor Andreas & Frankovic, Ivan & Kanik, Birol & Saxegaard, Magnus, 2020. "State-dependent fiscal multipliers in NORA - A DSGE model for fiscal policy analysis in Norway," Economic Modelling, Elsevier, vol. 93(C), pages 321-353.
    48. Polyzos, Efstathios, 2022. "Examining the asymmetric impact of macroeconomic policy in the UAE: Evidence from quartile impulse responses and machine learning," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).

  13. Lu Jin & Atsushi Inoue & Barbara Rossi, 2015. "Rolling Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters," Working Papers 768, Barcelona School of Economics.

    Cited by:

    1. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    2. Fernando Fernández-Rodríguez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2015. "“Financial stress transmission in EMU sovereign bond market volatility: a connectedness analysis”," IREA Working Papers 201510, University of Barcelona, Research Institute of Applied Economics, revised Feb 2015.
    3. Cai, Yuxin & Lu, Xinsheng & Ren, Yongping & Qu, Ling, 2019. "Exploring the dynamic relationship between crude oil price and implied volatility indices: A MF-DCCA approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Hirano, Keisuke & Wright, Jonathan H., 2022. "Analyzing cross-validation for forecasting with structural instability," Journal of Econometrics, Elsevier, vol. 226(1), pages 139-154.
    5. Zhang, Jiaming & Xiang, Yitian & Zou, Yang & Guo, Songlin, 2024. "Volatility forecasting of Chinese energy market: Which uncertainty have better performance?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    6. Zhang, Dongna & Dai, Xingyu & Xue, Jianhao, 2024. "Incorporating weather information into commodity portfolio optimization," Finance Research Letters, Elsevier, vol. 66(C).
    7. 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).
    8. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    9. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2020. "Can systemic risk measures predict economic shocks? Evidence from China," China Economic Review, Elsevier, vol. 64(C).
    10. Fu, Tong & Huang, Dasen & Feng, Lingbing & Tang, Xiaoping, 2024. "More is better? The impact of predictor choice on the INE oil futures volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
    11. Tae-Hwy Lee & Ekaterina Seregina & Yaojue Xu, 2023. "Elicitability and Encompassing for Volatility Forecasts by Bregman Functions," Working Papers 202311, University of California at Riverside, Department of Economics.
    12. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    13. Duan, Huayou & Zhao, Chenchen & Wang, Lu & Liu, Guangqiang, 2024. "The relationship between renewable energy attention and volatility: A HAR model with markov time-varying transition probability," Research in International Business and Finance, Elsevier, vol. 71(C).
    14. 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.
    15. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    16. Xiu Xu & Andrija Mihoci & Wolfgang Karl Hardle, 2020. "lCARE -- localizing Conditional AutoRegressive Expectiles," Papers 2009.13215, arXiv.org.
    17. 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.
    18. 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).
    19. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    20. Hengzhen Lu & Qiujin Gao & Ling Xiao & Gurjeet Dhesi, 2024. "Forecasting EUA futures volatility with geopolitical risk: evidence from GARCH-MIDAS models," Review of Managerial Science, Springer, vol. 18(7), pages 1917-1943, July.
    21. Shahriyar Aliev & Evžen Kočenda, 2022. "ECB monetary policy and commodity prices," FFA Working Papers 4.008, Prague University of Economics and Business, revised 21 Jun 2022.
    22. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).
    23. Peng, Huan & Chen, Ruoxun & Mei, Dexiang & Diao, Xiaohua, 2018. "Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 78-85.
    24. Liang, Chao & Ma, Feng & Li, Ziyang & Li, Yan, 2020. "Which types of commodity price information are more useful for predicting US stock market volatility?," Economic Modelling, Elsevier, vol. 93(C), pages 642-650.
    25. Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Optimal Forecast under Structural Breaks," Working Papers 202208, University of California at Riverside, Department of Economics.
    26. 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.
    27. Rachid Guennouni Hassani & Alexis Gilles & Emmanuel Lassalle & Arthur D'enouveaux, 2020. "Predicting Stock Returns with Batched AROW," Papers 2003.03076, arXiv.org, revised Mar 2020.
    28. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
    29. Zhang, Yaojie & Ma, Feng & Wei, Yu, 2019. "Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches," Energy Economics, Elsevier, vol. 81(C), pages 1109-1120.
    30. Fernando Fernández-Rodríguez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2015. "Volatility spillovers in EMU sovereign bond markets," Working Papers 15-03, Asociación Española de Economía y Finanzas Internacionales.
    31. 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.
    32. Yi, Yongsheng & He, Mengxi & Zhang, Yaojie, 2022. "Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    33. 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.
    34. Wang, Ping & Han, Wei & Huang, Chengcheng & Duong, Duy, 2022. "Forecasting realised volatility from search volume and overnight sentiment: Evidence from China," Research in International Business and Finance, Elsevier, vol. 62(C).
    35. Yaojie Zhang & Yudong Wang & Feng Ma & Yu Wei, 2022. "To jump or not to jump: momentum of jumps in crude oil price volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    36. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
    37. Feng, Lingbing & Qi, Jiajun & Lucey, Brian, 2024. "Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy," International Review of Financial Analysis, Elsevier, vol. 94(C).
    38. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    39. Mikihito Nishi, 2024. "Estimating Time-Varying Parameters of Various Smoothness in Linear Models via Kernel Regression," Papers 2406.14046, arXiv.org, revised Mar 2025.
    40. Artur Tarassow, 2017. "Forecasting growth of U.S. aggregate and household-sector M2 after 2000 using economic uncertainty measures," Macroeconomics and Finance Series 201702, University of Hamburg, Department of Socioeconomics.
    41. Oh, Juhyun & Suh, Dong Hee, 2024. "Exploring the import allocation of wood pellets: Insights from price and policy influences under the renewable portfolio standard," Forest Policy and Economics, Elsevier, vol. 161(C).
    42. He, Mengxi & Wang, Yudong & Zeng, Qing & Zhang, Yaojie, 2023. "Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index," Research in International Business and Finance, Elsevier, vol. 65(C).
    43. Xu, Xiu & Mihoci, Andrija & Härdle, Wolfgang Karl, 2018. "lCARE - localizing conditional autoregressive expectiles," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 198-220.
    44. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    45. Peng, Zhen & Dong, Chaohua, 2022. "Augmented cointegrating linear models with possibly strongly correlated stationary and nonstationary regressors," Finance Research Letters, Elsevier, vol. 47(PB).
    46. 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.
    47. Xu Xiaojie, 2018. "Using Local Information to Improve Short-Run Corn Price Forecasts," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 16(1), pages 1-15, January.
    48. Jakob Krause, 2019. "A convergence-speed-dependent data quantity definition and its effect on risk estimation," Journal of Asset Management, Palgrave Macmillan, vol. 20(6), pages 469-475, October.
    49. Deryugina, Elena & Ponomarenko, Alexey & Rozhkova, Anna, 2020. "When are credit gap estimates reliable?," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 221-238.
    50. Sixian Tang & Jackie Li & Leonie Tickle, 2022. "A New Fourier Approach under the Lee-Carter Model for Incorporating Time-Varying Age Patterns of Structural Changes," Risks, MDPI, vol. 10(8), pages 1-24, July.
    51. Algieri, Bernardina & Leccadito, Arturo, 2019. "Ask CARL: Forecasting tail probabilities for energy commodities," Energy Economics, Elsevier, vol. 84(C).
    52. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2020. "How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics," Energy Economics, Elsevier, vol. 90(C).
    53. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
    54. Zhang, Yaojie & Wang, Yudong, 2023. "Forecasting crude oil futures market returns: A principal component analysis combination approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 659-673.
    55. Liang, Chao & Li, Yan & Ma, Feng & Wei, Yu, 2021. "Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information," International Review of Financial Analysis, Elsevier, vol. 75(C).
    56. Feng, Lingbing & Rao, Haicheng & Lucey, Brian & Zhu, Yiying, 2024. "Volatility forecasting on China's oil futures: New evidence from interpretable ensemble boosting trees," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 1595-1615.
    57. Stephen G. Hall & George S. Tavlas & Yongli Wang, 2023. "Forecasting inflation: The use of dynamic factor analysis and nonlinear combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 514-529, April.
    58. Wen, Danyan & Wang, Huihui & Wang, Yudong & Xiao, Jihong, 2024. "Crude oil futures and the short-term price predictability of petroleum products," Energy, Elsevier, vol. 307(C).
    59. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    60. R'egis Houssou & J'er^ome Bovay & Stephan Robert, 2019. "Adaptive Financial Fraud Detection in Imbalanced Data with Time-Varying Poisson Processes," Papers 1912.04308, arXiv.org.
    61. Bae, Juhee, 2024. "Factor-augmented forecasting in big data," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1660-1688.
    62. Chen, Juan & Xiao, Zuoping & Bai, Jiancheng & Guo, Hongling, 2023. "Predicting volatility in natural gas under a cloud of uncertainties," Resources Policy, Elsevier, vol. 82(C).
    63. Rachid Guennouni Hassani & Alexis Gilles & Emmanuel Lassalle & Arthur Dénouveaux, 2020. "Predicting Stock Returns with Batched AROW," Working Papers hal-02496048, HAL.
    64. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    65. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    66. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.
    67. Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
    68. 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".
    69. Ghani, Usman & Zhu, Bo & Ghani, Maria & Khan, Nasir & khan, Raja Danish Akbar, 2023. "Role of oil shocks in US stock market volatility: A new insight from GARCH-MIDAS perspective," Resources Policy, Elsevier, vol. 85(PB).
    70. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    71. Ye, Wuyi & Xia, Wenjing & Wu, Bin & Chen, Pengzhan, 2022. "Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market," International Review of Financial Analysis, Elsevier, vol. 83(C).
    72. Luca Brugnolini & Antonello D’Agostino & Alex Tagliabracci, 2021. "Is Anything Predictable in Market-Based Surprises?," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 7(3), pages 387-410, November.
    73. Zhang, Yaojie & Ma, Feng & Zhu, Bo, 2019. "Intraday momentum and stock return predictability: Evidence from China," Economic Modelling, Elsevier, vol. 76(C), pages 319-329.
    74. Sun, Yuying & Wang, Shouyang & Zhang, Xun, 2018. "How efficient are China's macroeconomic forecasts? Evidences from a new forecasting evaluation approach," Economic Modelling, Elsevier, vol. 68(C), pages 506-513.
    75. Chang, Chih-Hao & Chen, Zih-Bing & Huang, Shih-Feng, 2022. "Forecasting of high-resolution electricity consumption with stochastic climatic covariates via a functional time series approach," Applied Energy, Elsevier, vol. 309(C).
    76. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    77. Wu, Chuanzhen, 2021. "Window effect with Markov-switching GARCH model in cryptocurrency market," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    78. 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.
    79. 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.
    80. Arango-Castillo, Lenin & Orraca, María José & Molina, G. Stefano, 2023. "The global component of headline and core inflation in emerging market economies and its ability to improve forecasting performance," Economic Modelling, Elsevier, vol. 120(C).
    81. Gantungalag Altansukh & Denise R. Osborn, 2022. "Using structural break inference for forecasting time series," Empirical Economics, Springer, vol. 63(1), pages 1-41, July.
    82. Shikha Gupta & Nand Kumar, 2022. "Globalization Versus Slowbalization: A Perspective on the Indian Economy," Journal of South Asian Development, , vol. 17(1), pages 84-107, April.
    83. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    84. Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).
    85. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
    86. 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.
    87. Boot, Tom & Pick, Andreas, 2020. "Does modeling a structural break improve forecast accuracy?," Journal of Econometrics, Elsevier, vol. 215(1), pages 35-59.
    88. Wen, Danyan & Wang, Yudong & Zhang, Yaojie, 2021. "Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism," Economic Modelling, Elsevier, vol. 96(C), pages 209-219.
    89. Zhang, Yaojie & Ma, Feng & Liao, Yin, 2020. "Forecasting global equity market volatilities," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1454-1475.
    90. Damiano B. Silipo & Giovanni Verga & Sviatlana Hlebik, 2023. "Managerial Beliefs and Banking Behavior," Journal of Financial Services Research, Springer;Western Finance Association, vol. 64(3), pages 401-431, December.
    91. Li, Xishu & Zuidwijk, Rob & de Koster, M.B.M, 2023. "Optimal competitive capacity strategies: Evidence from the container shipping market," Omega, Elsevier, vol. 115(C).
    92. 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.
    93. Dent, Kieran & Hacioglu Hoke, Sinem & Panagiotopoulos, Apostolos, 2017. "Solvency and wholesale funding cost interactions at UK banks," Bank of England working papers 681, Bank of England.
    94. Liu, Guangqiang & Guo, Xiaozhu, 2022. "Forecasting stock market volatility using commodity futures volatility information," Resources Policy, Elsevier, vol. 75(C).
    95. Liu, Jing & Ma, Feng & Yang, Ke & Zhang, Yaojie, 2018. "Forecasting the oil futures price volatility: Large jumps and small jumps," Energy Economics, Elsevier, vol. 72(C), pages 321-330.
    96. Christis Katsouris, 2023. "Predictability Tests Robust against Parameter Instability," Papers 2307.15151, arXiv.org.
    97. 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.).
    98. Liang, Chao & Luo, Qin & Li, Yan & Huynh, Luu Duc Toan, 2023. "Global financial stress index and long-term volatility forecast for international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    99. Zhang, Yaojie & Lei, Likun & Wei, Yu, 2020. "Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    100. Prakash, Navendu & Srivastava, Bhavya & Singh, Shveta & Sharma, Seema & Jain, Sonali, 2022. "Effectiveness of social distancing interventions in containing COVID-19 incidence: International evidence using Kalman filter," Economics & Human Biology, Elsevier, vol. 44(C).
    101. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    102. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    103. Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.
    104. Khowaja Kainat & Saef Danial & Sizov Sergej & Härdle Wolfgang Karl, 2024. "Scenario based merger & acquisition forecasting," Management & Marketing, Sciendo, vol. 19(4), pages 579-600.
    105. Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2021. "Realized skewness and the short-term predictability for aggregate stock market volatility," Economic Modelling, Elsevier, vol. 103(C).
    106. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
    107. Hany Guirguis & Vaneesha Boney Dutra & Zoe McGreevy, 2022. "The impact of global economies on US inflation: A test of the Phillips curve," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(3), pages 575-592, July.
    108. Mengxi He & Yudong Wang & Yaojie Zhang, 2023. "The predictability of iron ore futures prices: A product‐material lead–lag effect," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1289-1304, September.
    109. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    110. 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.
    111. Nikolaos Giannellis & Stephen G. Hall & Georgios P. Kouretas & George S. Tavlas, 2024. "Forecasting in turbulent times," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 819-826, July.
    112. 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.
    113. 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.
    114. 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).
    115. He, Mengxi & Wen, Danyan & Xing, Lu & Zhang, Yaojie, 2024. "Industry volatility concentration and the predictability of aggregate stock market volatility," International Review of Economics & Finance, Elsevier, vol. 95(C).
    116. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    117. Subhamitra Patra & Gourishankar S. Hiremath, 2022. "An Entropy Approach to Measure the Dynamic Stock Market Efficiency," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(2), pages 337-377, June.
    118. Chien-Ho Wang & Ming-Hui Ko & Wan-Jiun Chen, 2019. "Effects of Kyoto Protocol on CO 2 Emissions: A Five-Country Rolling Regression Analysis," Sustainability, MDPI, vol. 11(3), pages 1-20, January.
    119. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel & Schweizer, Denis, 2023. "Interactions between investors’ fear and greed sentiment and Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    120. Dong, Dayong & Yue, Sishi & Cao, Jiawei, 2020. "Site visit information content and return predictability: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    121. Zhang, Yue-Jun & Li, Zhao-Chen, 2021. "Forecasting the stock returns of Chinese oil companies: Can investor attention help?," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 531-555.
    122. Xiafei Li & Yu Wei & Xiaodan Chen & Feng Ma & Chao Liang & Wang Chen, 2022. "Which uncertainty is powerful to forecast crude oil market volatility? New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4279-4297, October.
    123. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
    124. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).
    125. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2024. "Improving models and forecasts after equilibrium-mean shifts," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1085-1100.
    126. Li, Xiafei & Guo, Qiang & Liang, Chao & Umar, Muhammad, 2023. "Forecasting gold volatility with geopolitical risk indices," Research in International Business and Finance, Elsevier, vol. 64(C).
    127. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.

  14. Atsushi Inoue & Mototsugu Shintania, 2014. "Quasi-Bayesian Model Selection," Departmental Working Papers 1402, Southern Methodist University, Department of Economics.

    Cited by:

    1. Hirano, Keisuke & Wright, Jonathan H., 2022. "Analyzing cross-validation for forecasting with structural instability," Journal of Econometrics, Elsevier, vol. 226(1), pages 139-154.
    2. Akihisa Shibata & Mototsugu Shintani & Takayuki Tsuruga, 2018. "Current Account Dynamics under Information Rigidity and Imperfect Capital Mobility," Globalization Institute Working Papers 335, Federal Reserve Bank of Dallas.
    3. Yasufumi Gemma & Takushi Kurozumi & Mototsugu Shintani, 2023. "Trend Inflation and Evolving Inflation Dynamics:A Bayesian GMM Analysis," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 506-520, December.
    4. Iwasaki, Yuto & Muto, Ichiro & Shintani, Mototsugu, 2021. "Missing wage inflation? Estimating the natural rate of unemployment in a nonlinear DSGE model," European Economic Review, Elsevier, vol. 132(C).
    5. Jesus Fernandez-Villaverde & Juan Rubio-Ramírez & Frank Schorfheide, 2015. "Solution and Estimation Methods for DSGE Models," PIER Working Paper Archive 15-042, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2015.
    6. Li, Yong & Yu, Jun & Zeng, Tao, 2020. "Deviance information criterion for latent variable models and misspecified models," Journal of Econometrics, Elsevier, vol. 216(2), pages 450-493.
    7. Prosper Dovonon & Firmin Doko Tchatoka & Michael Aguessy, 2019. "Relevant moment selection under mixed identification strength," School of Economics and Public Policy Working Papers 2019-04, University of Adelaide, School of Economics and Public Policy.
    8. Andrey Polbin & Sergey Sinelnikov-Murylev, 2023. "Developing and impulse response matching estimation of the DSGE model for the Russian economy," Research Paper Series, Gaidar Institute for Economic Policy, issue 182P, pages 1-53.
    9. KANO, Takashi, 2023. "Posterior Inferences on Incomplete Structural Models : The Minimal Econometric Interpretation," Discussion paper series HIAS-E-128, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.

  15. 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.

    Cited by:

    1. Fernando Fernández-Rodríguez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2015. "“Financial stress transmission in EMU sovereign bond market volatility: a connectedness analysis”," IREA Working Papers 201510, University of Barcelona, Research Institute of Applied Economics, revised Feb 2015.
    2. Xiu Xu & Andrija Mihoci & Wolfgang Karl Hardle, 2020. "lCARE -- localizing Conditional AutoRegressive Expectiles," Papers 2009.13215, arXiv.org.
    3. Fernando Fernández-Rodríguez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2015. "Volatility spillovers in EMU sovereign bond markets," Working Papers 15-03, Asociación Española de Economía y Finanzas Internacionales.
    4. Xu, Xiu & Mihoci, Andrija & Härdle, Wolfgang Karl, 2018. "lCARE - localizing conditional autoregressive expectiles," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 198-220.
    5. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2020. "How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics," Energy Economics, Elsevier, vol. 90(C).
    6. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    7. 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.

  16. Yasuo Hirose & Atsushi Inoue, 2013. "Zero Lower Bound and Parameter Bias in an Estimated DSGE Model," CAMA Working Papers 2013-60, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Chun-Hung Kuo & Hiroaki Miyamoto, 2016. "Unemployment and Wage Rigidity in Japan: A DSGE Model Perspective," Working Papers EMS_2016_06, Research Institute, International University of Japan.
    2. Charles Ka Yui Leung & Joe Cho Yiu Ng, 2018. "Macro Aspects of Housing," Globalization Institute Working Papers 340, Federal Reserve Bank of Dallas.
    3. Roberta Cardani & Alessia Paccagnini & Stelios D. Bekiros, 2017. "The Effectiveness of Forward Guidance in an Estimated DSGE Model for the Euro Area: the Role of Expectations," Working Papers 201701, School of Economics, University College Dublin.
    4. 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.
    5. Tyler Atkinson & Alexander W. Richter & Nathaniel A. Throckmorton, 2018. "The Zero Lower Bound and Estimation Accuracy," Working Papers 1804, Federal Reserve Bank of Dallas.
    6. Yasuo Hirose & Takeki Sunakawa, 2019. "The Natural Rate of Interest in a Nonlinear DSGE Model," Working Papers e128, Tokyo Center for Economic Research.
    7. Yasuo Hirose & Takeki Sunakawa, 2016. "Parameter Bias in an Estimated DSGE Model," Working Papers halshs-01661908, HAL.
    8. Nicoletta Batini & Alessandro Cantelmo & Giovanni Melina & Stefania Villa, 2020. "How Loose, how tight? A measure of monetary and fiscal stance for the euro area," Temi di discussione (Economic working papers) 1295, Bank of Italy, Economic Research and International Relations Area.
    9. Alfred Duncan & Charles Nolan, 2017. "Disputes, Debt and Equity," Studies in Economics 1716, School of Economics, University of Kent.
    10. Iwasaki, Yuto & Muto, Ichiro & Shintani, Mototsugu, 2021. "Missing wage inflation? Estimating the natural rate of unemployment in a nonlinear DSGE model," European Economic Review, Elsevier, vol. 132(C).
    11. Yasuo Hirose & Atsushi Inoue, 2013. "Zero Lower Bound and Parameter Bias in an Estimated DSGE Model," TERG Discussion Papers 308, Graduate School of Economics and Management, Tohoku University.
    12. Muto, Ichiro & Sudo, Nao & Yoneyama, Shunichi, 2013. "Productivity Slowdown in Japan’s Lost Decades: How Much of It is Attributed to Financial Factors?," Dynare Working Papers 28, CEPREMAP.
    13. Best Gabriela & Kapinos Pavel, 2016. "Monetary policy and news shocks: are Taylor rules forward-looking?," The B.E. Journal of Macroeconomics, De Gruyter, vol. 16(2), pages 335-360, June.
    14. Shirota, Toyoichiro, 2018. "What is the major source of business cycles: Spillovers from land prices, investment shocks, or anything else?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 138-149.
    15. Marcin Bielecki & Michał Brzoza-Brzezina & Marcin Kolasa & Krzysztof Makarski, 2017. "Could the boom-bust in the eurozone periphery have been prevented?," GRAPE Working Papers 17, GRAPE Group for Research in Applied Economics.
    16. Neri, Stefano & Gerali, Andrea, 2019. "Natural rates across the Atlantic," Journal of Macroeconomics, Elsevier, vol. 62(C).
    17. Eijffinger, S.C.W. & Grajales Olarte, A. & Uras, R.B., 2015. "Heterogeneity in Wage Setting Behavior in a New-Keynesian Model," Discussion Paper 2015-024, Tilburg University, Center for Economic Research.
    18. Alexander W. Richter & Nathaniel A. Throckmorton, 2016. "Are nonlinear methods necessary at the zero lower bound?," Working Papers 1606, Federal Reserve Bank of Dallas.
    19. Chen, Xiaoshan & Kirsanova, Tatiana & Leith, Campbell, 2014. "An Empirical Assessment of Optimal Monetary Policy Delegation in the Euro Area," Stirling Economics Discussion Papers 2014-11, University of Stirling, Division of Economics.
    20. Damioli, Giacomo & Gregori, Wildmer Daniel, 2021. "Diplomatic relations and cross-border investments in the European Union," JRC Working Papers in Economics and Finance 2021-02, Joint Research Centre, European Commission.
    21. Alice Albonico & Alessia Paccagnini & Patrizio Tirelli, 2018. "Limited Asset Market Participation and the Euro Area Crisis. An Empirical DSGE Model," Working Papers 391, University of Milano-Bicocca, Department of Economics, revised Nov 2018.
    22. Hasumi, Ryo & Iiboshi, Hirokuni & Nakamura, Daisuke, 2017. "R&D Growth and Business Cycles Measured with an Endogenous Growth DSGE Model," MPRA Paper 85525, University Library of Munich, Germany.
    23. Albertini, Julien & Lan, Hong, 2016. "The importance of time-varying parameters in new Keynesian models with zero lower bound," SFB 649 Discussion Papers 2016-013, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    24. Jeff Fuhrer, 2017. "Japanese and U.S. Inflation Dynamics in the 21st Century," IMES Discussion Paper Series 17-E-05, Institute for Monetary and Economic Studies, Bank of Japan.
    25. Eijffinger, S.C.W. & Grajales Olarte, A. & Uras, R.B., 2015. "Heterogeneity in Wage Setting Behavior in a New-Keynesian Model," Other publications TiSEM ca4cf819-2c5f-4391-82df-6, Tilburg University, School of Economics and Management.
    26. Eijffinger, S.C.W. & Grajales Olarte, A. & Uras, R.B., 2015. "Heterogeneity in Wage Setting Behavior in a New-Keynesian Model," Other publications TiSEM cd9bb586-72f4-47d0-94e0-1, Tilburg University, School of Economics and Management.
    27. Ichiro Muto & Nao Sudo & Shunichi Yoneyama, 2023. "Productivity Slowdown in Japan's Lost Decades: How Much of It Can Be Attributed to Damaged Balance Sheets?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(1), pages 159-207, February.
    28. Giovannini, Massimo & Pfeiffer, Philipp & Ratto, Marco, 2021. "Efficient and robust inference of models with occasionally binding constraints," JRC Working Papers in Economics and Finance 2021-03, Joint Research Centre, European Commission.
    29. Hasumi, Ryo & Iibsoshi, Hirokuni & Nakamura, Daisuke, 2018. "Trends, Cycles and Lost Decades: Decomposition from a DSGE Model with Endogenous Growth," MPRA Paper 85521, University Library of Munich, Germany.
    30. Charles Ka Yui Leung & Edward Chi Ho Tang, 2021. "The Dynamics of the House Price-to-Income Ratio: Theory and Evidence," GRU Working Paper Series GRU_2021_005, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    31. Hills, Timothy S. & Nakata, Taisuke & Schmidt, Sebastian, 2019. "Effective lower bound risk," European Economic Review, Elsevier, vol. 120(C).
    32. Yasuo Hirose & Takeki Sunakawa, 2016. "Parameter Bias in an Estimated DSGE Model:Does Nonlinearity Matter?," UTokyo Price Project Working Paper Series 063, University of Tokyo, Graduate School of Economics.
    33. 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.
    34. MATSUMAE Tatsuyoshi & HASUMI Ryo, 2016. "Impacts of Government Spending on Unemployment: Evidence from a Medium-scale DSGE Model(in Japanese)," ESRI Discussion paper series 329, Economic and Social Research Institute (ESRI).
    35. Hirokuni Iiboshi & Mototsugu Shintani & Kozo Ueda, 2018. "Estimating a Nonlinear New Keynesian Model with a Zero Lower Bound for Japan," Working Papers e120, Tokyo Center for Economic Research.
    36. Eijffinger, Sylvester & Grajales Olarte, Anderson & Uras, Burak, 2020. "Heterogeneity in wage setting behavior in a New-Keynesian Model," Other publications TiSEM 24069cb1-ed64-4367-9a37-b, Tilburg University, School of Economics and Management.
    37. Yasuo Hirose & Takeki Sunakawa, 2019. "Review of Solution and Estimation Methods for Nonlinear Dynamic Stochastic General Equilibrium Models with the Zero Lower Bound," The Japanese Economic Review, Japanese Economic Association, vol. 70(1), pages 51-104, March.
    38. Bianca Barbaro & Giorgio Massari & Patrizio Tirelli, 2022. "Who killed business dynamism in the U.S.?," Working Papers 494, University of Milano-Bicocca, Department of Economics, revised Aug 2022.
    39. Go Kotera & Saisuke Sakai, 2017. "Complementarity between Merit Goods and Private Consumption: Evidence from estimated DSGE model for Japan," KIER Working Papers 978, Kyoto University, Institute of Economic Research.
    40. Calo, Silvia & Gregori, Wildmer Daniel & Petracco Giudici, Marco & Rancan, Michela, 2021. "Has the Comprehensive Assessment made the European financial system more resilient?," JRC Working Papers in Economics and Finance 2021-08, Joint Research Centre, European Commission.
    41. Kang, Hyunju & Park, Jaevin & Suh, Hyunduk, 2020. "The rise of part-time employment in the great recession: Its causes and macroeconomic effects," Journal of Macroeconomics, Elsevier, vol. 66(C).

  17. Xu Han & Atsushi Inoue, 2013. "Tests for Parameter Instability in Dynamic Factor Models," DSSR Discussion Papers 10, Graduate School of Economics and Management, Tohoku University.

    Cited by:

    1. Zhou, Ruichao & Wu, Jianhong, 2023. "Determining the number of change-points in high-dimensional factor models by cross-validation with matrix completion," Economics Letters, Elsevier, vol. 232(C).
    2. Ma, Shujie & Su, Liangjun, 2018. "Estimation of large dimensional factor models with an unknown number of breaks," Journal of Econometrics, Elsevier, vol. 207(1), pages 1-29.
    3. Massacci, Daniele & Kapetanios, George, 2024. "Forecasting in factor augmented regressions under structural change," International Journal of Forecasting, Elsevier, vol. 40(1), pages 62-76.
    4. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    5. Giovanni Caggiano & Efrem Castelnuovo, 2023. "Global financial uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 432-449, April.
    6. Wenting Liao & Jun Ma & Chengsi Zhang, 2023. "Identifying exchange rate effects and spillovers of US monetary policy shocks in the presence of time‐varying instrument relevance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(7), pages 989-1006, November.
    7. Duan, Jiangtao & Bai, Jushan & Han, Xu, 2023. "Quasi-maximum likelihood estimation of break point in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 233(1), pages 209-236.
    8. Han, Chirok & Kim, Dukpa, 2020. "Testing for the null of block zero restrictions in common factor models," Economics Letters, Elsevier, vol. 188(C).
    9. Bai, Jushan & Li, Kunpeng, 2012. "Maximum likelihood estimation and inference for approximate factor models of high dimension," MPRA Paper 42099, University Library of Munich, Germany, revised 19 Oct 2012.
    10. Yamamoto, Yohei & Tanaka, Shinya, 2015. "Testing for factor loading structural change under common breaks," Journal of Econometrics, Elsevier, vol. 189(1), pages 187-206.
    11. Tatsushi Oka & Pierre Perron, 2016. "Testing for Common Breaks in a Multiple Equations System," Papers 1606.00092, arXiv.org, revised Jan 2018.
    12. Su, Liangjun & Wang, Xia, 2017. "On time-varying factor models: Estimation and testing," Journal of Econometrics, Elsevier, vol. 198(1), pages 84-101.
    13. Chen, Liang, 2011. "Detecting big structural breaks in large factor models," UC3M Working papers. Economics we1141, Universidad Carlos III de Madrid. Departamento de Economía.
    14. Li, Degui, 2024. "Estimation of Large Dynamic Covariance Matrices: A Selective Review," Econometrics and Statistics, Elsevier, vol. 29(C), pages 16-30.
    15. Monika Bours & Ansgar Steland, 2021. "Large‐sample approximations and change testing for high‐dimensional covariance matrices of multivariate linear time series and factor models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 610-654, June.
    16. Beyhum, Jad & Striaukas, Jonas, 2024. "Testing for sparse idiosyncratic components in factor-augmented regression models," Journal of Econometrics, Elsevier, vol. 244(1).
    17. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    18. Wang, Lu & Wu, Jianhong, 2022. "Estimation of high-dimensional factor models with multiple structural changes," Economic Modelling, Elsevier, vol. 108(C).
    19. 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.
    20. Chou, Ray Yeutien & Yen, Tso-Jung & Yen, Yu-Min, 2020. "Macroeconomic forecasting using approximate factor models with outliers," International Journal of Forecasting, Elsevier, vol. 36(2), pages 267-291.
    21. Markus Pelger & Ruoxuan Xiong, 2018. "State-Varying Factor Models of Large Dimensions," Papers 1807.02248, arXiv.org, revised Oct 2020.
    22. Byungsoo Kim & Junmo Song & Changryong Baek, 2021. "Robust test for structural instability in dynamic factor models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(4), pages 821-853, August.
    23. Aslanidis, Nektarios & Hartigan, Luke, 2021. "Is the assumption of constant factor loadings too strong in practice?," Economic Modelling, Elsevier, vol. 98(C), pages 100-108.
    24. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    25. Badi H. Baltagi & Chihwa Kao & Fa Wang, 2016. "The Identification and Estimation of a Large Factor Model with Structural Instability," Center for Policy Research Working Papers 194, Center for Policy Research, Maxwell School, Syracuse University.
    26. Bonsoo Koo & Benjamin Wong & Ze-Yu Zhong, 2023. "Disentangling Structural Breaks in High Dimensional Factor Models," CAMA Working Papers 2023-15, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    27. Bai, Jushan & Duan, Jiangtao & Han, Xu, 2024. "The likelihood ratio test for structural changes in factor models," Journal of Econometrics, Elsevier, vol. 238(2).
    28. 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.
    29. Antoine A. Djogbenou, 2020. "Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 344-370, April.
    30. Fu, Zhonghao & Hong, Yongmiao & Wang, Xia, 2023. "Testing for structural changes in large dimensional factor models via discrete Fourier transform," Journal of Econometrics, Elsevier, vol. 233(1), pages 302-331.
    31. Steland, Ansgar, 2020. "Testing and estimating change-points in the covariance matrix of a high-dimensional time series," Journal of Multivariate Analysis, Elsevier, vol. 177(C).
    32. Changryong Baek & Benjamin Leinwand & Kristen A. Lindquist & Seok-Oh Jeong & Joseph Hopfinger & Katheleen M. Gates & Vladas Pipiras, 2023. "Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 636-655, June.
    33. Hartigan, Luke & Morley, James, 2019. "A Factor Model Analysis of the Australian Economy and the Effects of Inflation Targeting," Working Papers 2019-10, University of Sydney, School of Economics, revised Nov 2019.
    34. Massacci, Daniele, 2017. "Least squares estimation of large dimensional threshold factor models," Journal of Econometrics, Elsevier, vol. 197(1), pages 101-129.
    35. Matteo Barigozzi & Lorenzo Trapani, 2017. "Sequential testing for structural stability in approximate factor models," Papers 1708.02786, arXiv.org, revised Mar 2020.
    36. Jianqing Fan & Yuling Yan & Yuheng Zheng, 2024. "When can weak latent factors be statistically inferred?," Papers 2407.03616, arXiv.org, revised Sep 2024.
    37. Ma, Chenchen & Tu, Yundong, 2023. "Shrinkage estimation of multiple threshold factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1876-1892.
    38. Yohei Yamamoto & Naoko Hara, 2022. "Identifying factor‐augmented vector autoregression models via changes in shock variances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 722-745, June.
    39. Chen, Sanpan & Cui, Guowei & Zhang, Jianhua, 2017. "On testing for structural break of coefficients in factor-augmented regression models," Economics Letters, Elsevier, vol. 161(C), pages 141-145.
    40. Luke Hartigan, 2015. "Changes in the Factor Structure of the U.S. Economy: Permanent Breaks or Business Cycle Regimes?," Discussion Papers 2015-17, School of Economics, The University of New South Wales.
    41. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    42. Bai, Jushan & Han, Xu & Shi, Yutang, 2020. "Estimation and inference of change points in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 219(1), pages 66-100.
    43. Chen, Liang, 2015. "Estimating the common break date in large factor models," Economics Letters, Elsevier, vol. 131(C), pages 70-74.
    44. Chen, Liang, 2012. "Identifying observed factors in approximate factor models: estimation and hypothesis testing," MPRA Paper 37514, University Library of Munich, Germany.
    45. Baltagi, Badi H. & Kao, Chihwa & Wang, Fa, 2021. "Estimating and testing high dimensional factor models with multiple structural changes," Journal of Econometrics, Elsevier, vol. 220(2), pages 349-365.
    46. Jaeheon Jung, 2019. "Estimating a Large Covariance Matrix in Time-varying Factor Models," Papers 1910.11965, arXiv.org.
    47. Mehmet Balcilar & Riza Demirer & Festus V. Bekun, 2021. "Flexible Time-Varying Betas in a Novel Mixture Innovation Factor Model with Latent Threshold," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    48. Horváth, Lajos & Rice, Gregory, 2019. "Asymptotics for empirical eigenvalue processes in high-dimensional linear factor models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 138-165.
    49. Ma, Chenchen & Tu, Yundong, 2023. "Group fused Lasso for large factor models with multiple structural breaks," Journal of Econometrics, Elsevier, vol. 233(1), pages 132-154.
    50. Dante Amengual & Luca Repetto, 2014. "Testing a Large Number of Hypotheses in Approximate Factor Models," Working Papers wp2014_1410, CEMFI.
    51. Baek, Changryong & Gates, Katheleen M. & Leinwand, Benjamin & Pipiras, Vladas, 2021. "Two sample tests for high-dimensional autocovariances," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).

  18. Kilian, Lutz & Inoue, Atsushi, 2011. "Inference on Impulse Response Functions in Structural VAR Models," CEPR Discussion Papers 8419, C.E.P.R. Discussion Papers.

    Cited by:

    1. Yayi Yan & Jiti Gao & Bin Peng, 2021. "On Time-Varying VAR Models: Estimation, Testing and Impulse Response Analysis," Papers 2111.00450, arXiv.org.
    2. Lutz Kilian & Xiaoqing Zhou, 2019. "Oil Prices, Exchange Rates and Interest Rates," Working Papers 1914, Federal Reserve Bank of Dallas.
    3. Kilian, Lutz & Fattouh, Bassam & Mahadeva, Lavan, 2012. "The Role of Speculation in Oil Markets: What Have We Learned So Far?," CEPR Discussion Papers 8916, C.E.P.R. Discussion Papers.
    4. Raffaella Giacomini & Toru Kitagawa, 2014. "Inference about Non-Identi?ed SVARs," CeMMAP working papers CWP45/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Konstantinos Chisiridis & Kostas Mouratidis & Theodore Panagiotidis, 2018. "The North-South Divide, the Euro and the World," Working Papers 2018015, The University of Sheffield, Department of Economics.
    6. Fabrice Dabiré, 2022. "Forward guidance and the exchange rate: A theoretical sign restricted VAR analysis," Cahiers de recherche 22-03, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    7. Juan Antolín-Díaz & Juan F. Rubio-Ramírez, 2017. "Narrative Sign Restrictions for SVARs," Working Papers 2017-07, FEDEA.
    8. 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.
    9. Lutz Kilian, 2019. "Facts and Fiction in Oil Market Modeling," CESifo Working Paper Series 7902, CESifo.
    10. Alsamara, Mouyad Kassm & Mrabet, Zouhair & Elafif, Mohamed & Gangopadhyay, Partha, 2017. "The asymmetric effects of oil price on economic growth in Turkey and Saudi Arabia: new evidence from nonlinear ARDL approach," International Journal of Development and Conflict, Gokhale Institute of Politics and Economics, vol. 7(2), pages 97-118.
    11. Atsushi Inoue & Lutz Kilian, 2016. "Joint Confidence Sets for Structural Impulse Responses," CESifo Working Paper Series 5746, CESifo.
    12. Rangan Gupta & Mampho P. Modise, 2013. "Does the Source of Oil Price Shocks Matter for South African Stock Returns? A Structural VAR Approach," Working Papers 201318, University of Pretoria, Department of Economics.
    13. Morita, Hiroshi, 2014. "External shocks and Japanese business cycles: Evidence from a sign-restricted VAR model," Japan and the World Economy, Elsevier, vol. 30(C), pages 59-74.
    14. Thorsten Drautzburg & Pooyan Amir-Ahmadi, 2017. "Identification through Heterogeneity," 2017 Meeting Papers 1087, Society for Economic Dynamics.
    15. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.
    16. Atsushi Inoue & Òscar Jordà & Guido M. Kuersteiner, 2023. "Significance Bands for Local Projections," Working Paper Series 2023-15, Federal Reserve Bank of San Francisco.
    17. 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.
    18. Stephan B. Bruns & Alessio Moneta & David I. Stern, 2019. "Estimating the Economy-Wide Rebound Effect Using Empirically Identified Structural Vector Autoregressions," LEM Papers Series 2019/27, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    19. Hairault, Jean-Olivier & Zhutova, Anastasia, 2018. "The cyclicality of labor-market flows: A multiple-shock approach," European Economic Review, Elsevier, vol. 103(C), pages 150-172.
    20. Raffaella Giacomini & Toru Kitagawa, 2018. "Robust Bayesian inference for set-identified models," CeMMAP working papers CWP61/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Josué Diwambuena & Francesco Ravazzolo, 2022. "What are the drivers of Labor Productivity?," BEMPS - Bozen Economics & Management Paper Series BEMPS86, Faculty of Economics and Management at the Free University of Bozen.
    22. Alessio Volpicella, 2019. "SVARs Identification through Bounds on the Forecast Error Variance," Working Papers 890, Queen Mary University of London, School of Economics and Finance.
    23. Gong, Xiao-Li & Liu, Jian-Min & Xiong, Xiong & Zhang, Wei, 2021. "The dynamic effects of international oil price shocks on economic fluctuation," Resources Policy, Elsevier, vol. 74(C).
    24. Güntner, Jochen & Öhlinger, Peter, 2022. "Oil price shocks and the hedging benefit of airline investments," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    25. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2014. "Confidence Bands for Impulse Responses: Bonferroni versus Wald," Discussion Papers of DIW Berlin 1354, DIW Berlin, German Institute for Economic Research.
    26. David S. Jacks & Martin Stuermer, 2021. "Dry Bulk Shipping and the Evolution of Maritime Transport Costs, 1850-2020," NBER Working Papers 28627, National Bureau of Economic Research, Inc.
    27. Salzmann, Leonard, 2024. "Do Survey Data Help Identify Supply and Demand Shocks in Sign-restricted SVARs?," EconStor Preprints 289576, ZBW - Leibniz Information Centre for Economics.
    28. Thorsten Drautzburg, 2016. "A narrative approach to a fiscal DSGE model," Working Papers 16-11, Federal Reserve Bank of Philadelphia.
    29. Yayi Yan & Jiti Gao & Bin Peng, 2021. "On Time-Varying VAR models: Estimation, Testing and Impulse Response Analysis," Monash Econometrics and Business Statistics Working Papers 17/21, Monash University, Department of Econometrics and Business Statistics.
    30. Amir-Ahmadi, Pooyan & Matthes, Christian & Wang, Mu-Chun, 2017. "Measurement errors and monetary policy: Then and now," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 66-78.
    31. Gehrke, Britta & Yao, Fang, 2013. "Sources of Real Exchange Rate Fluctuations: The Role of Supply Shocks Revisited," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79821, Verein für Socialpolitik / German Economic Association.
    32. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    33. Kilian, Lutz & Zhou, Xiaoqing, 2018. "Structural Interpretation of Vector Autoregressions with Incomplete Information: Revisiting the Role of Oil Supply and Demand S," CEPR Discussion Papers 13068, C.E.P.R. Discussion Papers.
    34. Zheng, Xinwei & Su, Dan, 2017. "Impacts of oil price shocks on Chinese stock market liquidity," International Review of Economics & Finance, Elsevier, vol. 50(C), pages 136-174.
    35. Victor Pontines, 2020. "The real effects of loan-to-value limits: Empirical evidence from Korea," CAMA Working Papers 2020-02, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    36. Bünyamin Fuat Yıldız & Korhan K. Gökmenoğlu & Wing-Keung Wong, 2022. "Analysing Monetary Policy Shocks by Sign and Parametric Restrictions: The Evidence from Russia," Economies, MDPI, vol. 10(10), pages 1-16, September.
    37. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector auto regressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Bank of Finland Research Discussion Papers 14/2018, Bank of Finland.
    38. Pérez, Fernando & Vega, Marco, 2015. "Asymmetric exchange rate pass-through: Evidence from Peru," Working Papers 2015-011, Banco Central de Reserva del Perú.
    39. Sangyup Choi, 2021. "Bank Lending Standards, Loan Demand, and the Macroeconomy: Evidence from the Korean Bank Loan Officer Survey," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-45, December.
    40. Syed Abul Basher & Alfred Haug & Perry Sadorsky, 2017. "The impact of oil-market shocks on stock returns in major oil-exporting countries: A Markov-switching approach," Working Papers 1710, University of Otago, Department of Economics, revised Oct 2017.
    41. Atsushi Inoue & `Oscar Jord`a & Guido M. Kuersteiner, 2023. "Inference for Local Projections," Papers 2306.03073, arXiv.org, revised Aug 2024.
    42. Saidul Islam, 2024. "Investment-specific technology shocks and business cycle: evidence from a sign restriction approach," Indian Economic Review, Springer, vol. 59(1), pages 249-283, June.
    43. Raffaella Giacomini & Toru Kitagawa, 2014. "Inference about Non-Identified SVARs," CeMMAP working papers 45/14, Institute for Fiscal Studies.
    44. Fengler, Matthias & Polivka, Jeannine, 2022. "Structural Volatility Impulse Response Analysis," Economics Working Paper Series 2211, University of St. Gallen, School of Economics and Political Science, revised Nov 2022.
    45. Rubio-Ramírez, Juan Francisco & Caldara, Dario & Arias, Jonas E., 2016. "The Systematic Component of Monetary Policy in SVARs: An Agnostic Identi," CEPR Discussion Papers 11674, C.E.P.R. Discussion Papers.
    46. Alfred Haug & Syed Basher & Perry Sadorsky, 2016. "The impact of oil price shocks on exchange rates: A non-linear smooth-transition approach," EcoMod2016 9226, EcoMod.
    47. Liu, Donghui & Meng, Lingjie & Wang, Yudong, 2020. "Oil price shocks and Chinese economy revisited: New evidence from SVAR model with sign restrictions," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 20-32.
    48. Helmut Herwartz & Martin Plödt, 2016. "Simulation Evidence on Theory-based and Statistical Identification under Volatility Breaks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 94-112, February.
    49. Lutz Kilian & Xiaoqing Zhou, 2019. "Does Drawing Down the U.S. Strategic Petroleum Reserve Help Stabilize Oil Prices?," Working Papers 1916, Federal Reserve Bank of Dallas.
    50. Dany, Geraldine, 2016. "The credit channel during times of financial stress: A time varying VAR analysis," VfS Annual Conference 2016 (Augsburg): Demographic Change 145899, Verein für Socialpolitik / German Economic Association.
    51. Marek A. Dąbrowski & Łukasz Kwiatkowski & Justyna Wróblewska, 2020. "Sources of Real Exchange Rate Variability in Central and Eastern European Countries: Evidence from Structural Bayesian MSH-VAR Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(4), pages 369-412, December.
    52. Herwartz, Helmut & Plödt, Martin, 2014. "Sign restrictions and statistical identification under volatility breaks -- Simulation based evidence and an empirical application to monetary policy analysis," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100326, Verein für Socialpolitik / German Economic Association.
    53. Claudia Foroni & Livio Stracca, 2023. "The shale oil revolution and the global oil supply curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 370-387, April.
    54. Benjamin Poignard & Manabu Asai, 2023. "Estimation of high-dimensional vector autoregression via sparse precision matrix," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 307-326.
    55. Georgiadis, Georgios, 2015. "Examining asymmetries in the transmission of monetary policy in the euro area: Evidence from a mixed cross-section global VAR model," European Economic Review, Elsevier, vol. 75(C), pages 195-215.
    56. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2013. "Comparison of Methods for Constructing Joint Confidence Bands for Impulse Response Functions," MAGKS Papers on Economics 201325, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    57. Andre Harrison & Annika Segelhorst, 2024. "Do consumer price indices in oil-producing economies respond differently to oil market shocks? Evidence from Canada," Empirical Economics, Springer, vol. 67(5), pages 2039-2076, November.
    58. Kilian, Lutz, 2020. "Understanding the Estimation of Oil Demand and Oil Supply Elasticities," CEPR Discussion Papers 15244, C.E.P.R. Discussion Papers.
    59. Czudaj, Robert & Beckmann, Joscha, 2018. "Monetary policy shocks, expectations and information rigidities," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181573, Verein für Socialpolitik / German Economic Association.
    60. Uhrin, Gábor B. & Herwartz, Helmut, 2016. "Monetary policy shocks, set-identifying restrictions, and asset prices: A benchmarking approach for analyzing set-identified models," University of Göttingen Working Papers in Economics 295, University of Goettingen, Department of Economics.
    61. Tiemen M. Woutersen & John Ham, 2013. "Calculating confidence intervals for continuous and discontinuous functions of parameters," CeMMAP working papers 23/13, Institute for Fiscal Studies.
    62. Daniel Grabowski & Anna Staszewska-Bystrova & Peter Winker, 2020. "Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 5-32, March.
    63. Jonas E. Arias & Juan F. Rubio-Ramirez & Daniel F. Waggoner, 2020. "Uniform Priors for Impulse Responses," Working Papers 22-30, Federal Reserve Bank of Philadelphia.
    64. Markku Lanne & Jani Luoto, 2016. "Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression," CREATES Research Papers 2016-04, Department of Economics and Business Economics, Aarhus University.
    65. Alfred A. Haug & Syed Abul Basher, 2019. "Exchange rates of oil exporting countries and global oil price shocks: a nonlinear smooth-transition approach," Applied Economics, Taylor & Francis Journals, vol. 51(48), pages 5282-5296, October.
    66. Hiroshi Morita, 2017. "Effects of Anticipated Fiscal Policy Shock on Macroeconomic Dynamics in Japan," The Japanese Economic Review, Springer, vol. 68(3), pages 364-393, September.
    67. Dirk-Jan van de Ven & Roger Fouquet, 2014. "Historical energy price shocks and their changing effects on the economy," GRI Working Papers 153, Grantham Research Institute on Climate Change and the Environment.
    68. Ferreira, Leonardo N., 2022. "Forward guidance matters: Disentangling monetary policy shocks," Journal of Macroeconomics, Elsevier, vol. 73(C).
    69. Xiaoqing Zhou, 2019. "Refining the Workhorse Oil Market Model," Working Papers 1910, Federal Reserve Bank of Dallas.
    70. Harrison, Andre & Liu, Xiaochun & Stewart, Shamar L., 2024. "Are exchange rates absorbers of global oil shocks? A generalized structural analysis," Journal of International Money and Finance, Elsevier, vol. 146(C).
    71. Ferhat Citak, 2018. "Exchange Rate and Turkish Tourism Trade," International Journal of Economics and Financial Issues, Econjournals, vol. 8(4), pages 206-213.
    72. Inoue, Atsushi & Kilian, Lutz, 2020. "Joint Bayesian inference about impulse responses in VAR models," CFS Working Paper Series 650, Center for Financial Studies (CFS).
    73. Fernando J. Pérez Forero & Marco Vega, 2016. "Asymmetric Exchange Rate Pass-through: Evidence from Nonlinear SVARs," Working Papers 63, Peruvian Economic Association.
    74. Rausser, Gordon & Stuermer, Martin, 2020. "A Dynamic Analysis of Collusive Action: The Case of the World Copper Market, 1882-2016," MPRA Paper 104708, University Library of Munich, Germany.
    75. Richard T. Baillie & Kun Ho Kim, 2016. "Is Robust Inference with OLS Sensible in Time Series Regressions? Investigating Bias and MSE Trade-offs with Feasible GLS and VAR Approaches," Working Paper series 16-04, Rimini Centre for Economic Analysis.
    76. Lucas Hafemann & Paul Rudel & Joerg Schmidt, 2017. "Moving Closer or Drifting Apart: Distributional Effects of Monetary Policy," MAGKS Papers on Economics 201721, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    77. Paul Carrillo‐Maldonado, 2023. "Partial identification for growth regimes: The case of Latin American countries," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 557-583, July.
    78. Eickmeier, Sandra & Metiu, Norbert & Prieto, Esteban, 2016. "Time-varying volatility, financial intermediation and monetary policy," Discussion Papers 46/2016, Deutsche Bundesbank.
    79. Arias, Jonas E. & Caldara, Dario & Rubio-Ramírez, Juan F., 2019. "The systematic component of monetary policy in SVARs: An agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 1-13.
    80. Pooyan Amir‐Ahmadi & Thorsten Drautzburg, 2021. "Identification and inference with ranking restrictions," Quantitative Economics, Econometric Society, vol. 12(1), pages 1-39, January.
    81. Jamie L. Cross & Bao H. Nguyen & Trung Duc Tran, 2021. "The Role of Precautionary and Speculative Demand in the Global Market for Crude Oil," Working Papers No 06/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    82. Marianna Riggi & Fabrizio Venditti, 2015. "The time varying effect of oil price shocks on euro-area exports," Temi di discussione (Economic working papers) 1035, Bank of Italy, Economic Research and International Relations Area.
    83. Kuang, Haoxuan & Qu, Haohao & Deng, Kunxiang & Li, Jun, 2024. "A physics-informed graph learning approach for citywide electric vehicle charging demand prediction and pricing," Applied Energy, Elsevier, vol. 363(C).
    84. Harrison, Andre & Reed, Robert R., 2023. "Gross capital inflows, the U.S. economy, and the response of the Federal Reserve," Journal of International Money and Finance, Elsevier, vol. 139(C).
    85. Alexandru George NEACȘU & Andrei Costin NEACȘU & Georgiana PLEȘA & Georgian Dănuț MIHAI, 2024. "Assessing the impact of energy and macroeconomic shocks on the Romanian economy: a Bayesian VAR approach," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(641), W), pages 109-118, Winter.
    86. Steven Fazzari & James Morley & Irina Panovska, 2014. "State-Dependent Effects of Fiscal Policy," Discussion Papers 2012-27C, School of Economics, The University of New South Wales.
    87. Endong Wang, 2024. "Structural counterfactual analysis in macroeconomics: theory and inference," Papers 2409.09577, arXiv.org.
    88. Francesco Furlanetto & Francesco Ravazzolo & Samad Sarferaz, 2014. "Identification of financial factors in economic fluctuations," KOF Working papers 14-364, KOF Swiss Economic Institute, ETH Zurich.
    89. Arora, Vipin, 2014. "Estimates of the Price Elasticities of Natural Gas Supply and Demand in the United States," MPRA Paper 54232, University Library of Munich, Germany.
    90. Cengiz Tunc & Denis Pelletier, 2013. "Endogenous Life-Cycle Housing Investment and Portfolio Allocation," Working Papers 1345, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    91. Lomonosov, Daniil & Polbin, Andrey & Fokin, Nikita, 2020. "Влияние Шоков Мировой Деловой Активности, Предложения Нефти И Спекулятивных Нефтяных Шоков На Экономику Рф [The impact of global economic activity, oil supply and speculative oil shocks on the Russ," MPRA Paper 106019, University Library of Munich, Germany.
    92. Gehrke, Britta & Yao, Fang, 2014. "Phillips curve shocks and real exchange rate fluctuations: SVAR evidence," FAU Discussion Papers in Economics 11/2014, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    93. Mansur, Alfan, 2019. "Sharia Banking Dynamics and the Macroeconomic Responses: Evidence from Indonesia," MPRA Paper 97883, University Library of Munich, Germany, revised 02 Oct 2019.
    94. Lutz Kilian & Xiaoqing Zhou, 2020. "The Econometrics of Oil Market VAR Models," Working Papers 2006, Federal Reserve Bank of Dallas.
    95. Nicolas Legrand, 2019. "The Empirical Merit Of Structural Explanations Of Commodity Price Volatility: Review And Perspectives," Journal of Economic Surveys, Wiley Blackwell, vol. 33(2), pages 639-664, April.
    96. K. Istrefi & B. Vonnak, 2015. "Delayed Overshooting Puzzle in Structural Vector Autoregression Models," Working papers 576, Banque de France.
    97. Charles Rahal,, 2016. "Housing markets and unconventional monetary policy," Journal of Housing Economics, Elsevier, vol. 32(C), pages 67-80.
    98. Caputo, Rodrigo & Pedersen, Michael, 2020. "The changing nature of the real exchange rate: The role of central bank preferences," Economic Modelling, Elsevier, vol. 90(C), pages 445-464.
    99. Wee Chian Koh, 2017. "How do oil supply and demand shocks affect Asian stock markets?," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 10(1), pages 1-18, January.
    100. Etienne Vaccaro-Grange, 2019. "Quantitative Easing and the Term Premium as a Monetary Policy Instrument," AMSE Working Papers 1932, Aix-Marseille School of Economics, France.
    101. 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.
    102. Lomonosov Daniil, 2021. "Role of the Coronavirus Pandemic and the Collapse of the OPEC+ Deal in the Dynamics of Oil Prices in 2020 [Роль Пандемии Коронавируса И Развала Сделки Опек+ В Динамике Цены На Нефть В 2020 Г]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 8, pages 23-28, August.
    103. Benjamin Wong, 2013. "Inflation Dynamics and The Role of Oil Shocks: How Different Were the 1970s?," CAMA Working Papers 2013-59, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    104. Lutz Kilian & Xiaoqing Zhou, 2018. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks: Comment," CESifo Working Paper Series 7166, CESifo.
    105. Chen, Yong & Liu, Dingming, 2018. "Government spending shocks and the real exchange rate in China: Evidence from a sign-restricted VAR model," Economic Modelling, Elsevier, vol. 68(C), pages 543-554.
    106. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    107. Pascal Towbin & Sebastian Weber, 2016. "Price expectations and the US housing boom," Working Papers 2016-06, Swiss National Bank.
    108. Matthew Greenwood‐Nimmo & Viet Hoang Nguyen & Eliza Wu, 2021. "On the International Spillover Effects of Country‐Specific Financial Sector Bailouts and Sovereign Risk Shocks," The Economic Record, The Economic Society of Australia, vol. 97(317), pages 285-309, June.
    109. Lomonosov Daniil, 2021. "Роль Пандемии Коронавируса И Развала Сделки Опек+ В Динамике Цены На Нефть В 2020 Г," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 8, pages 23-28, August.
    110. Lomonosov, Daniil, 2021. "Роль Коронавирусной Пандемии И Развала Сделки Опек+ В Динамике Цены На Нефть В 2020 Году [The role of the coronavirus pandemic and the collapse of the OPEC + deal in the dynamics of oil prices in 2," MPRA Paper 109319, University Library of Munich, Germany.
    111. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    112. John W. Keating, 2013. "What Do We Learn from Blanchard and Quah Decompositions If Aggregate Demand May Not be Long-Run Neutral?," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201302, University of Kansas, Department of Economics.
    113. Paolo Gorgi & Siem Jan Koopman & Julia Schaumburg, 2021. "Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors," Tinbergen Institute Discussion Papers 21-056/III, Tinbergen Institute.
    114. Gong, Xu & Lin, Boqiang, 2018. "Time-varying effects of oil supply and demand shocks on China's macro-economy," Energy, Elsevier, vol. 149(C), pages 424-437.
    115. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2018. "The impact of oil-market shocks on stock returns in major oil-exporting countries," Journal of International Money and Finance, Elsevier, vol. 86(C), pages 264-280.
    116. Maghyereh, Aktham & Abdoh, Hussein, 2021. "The effect of structural oil shocks on bank systemic risk in the GCC countries," Energy Economics, Elsevier, vol. 103(C).
    117. Oladunni, Sunday, 2019. "External Shocks and Business Cycle Fluctuations in Oil-exporting Small Open Economies: The Case of Nigeria," MPRA Paper 98639, University Library of Munich, Germany.
    118. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.
    119. Herwartz, Helmut & Plödt, Martin, 2016. "The macroeconomic effects of oil price shocks: Evidence from a statistical identification approach," Journal of International Money and Finance, Elsevier, vol. 61(C), pages 30-44.
    120. Morita, Hiroshi, 2015. "State-dependent effects of fiscal policy in Japan: Do rule-of-thumb households increase the effects of fiscal policy?," Journal of Macroeconomics, Elsevier, vol. 43(C), pages 49-61.
    121. Steven M. Fazzari & James Morley & Irina B. Panovska, 2017. "When Do Discretionary Changes in Government Spending or Taxes Have Larger Effects?," Discussion Papers 2017-04, School of Economics, The University of New South Wales.
    122. Tommy Wu & Michael Cheng & Ken Wong, 2017. "Bayesian analysis of Hong Kong's housing price dynamics," Pacific Economic Review, Wiley Blackwell, vol. 22(3), pages 312-331, August.
    123. Daniil Lomonosov & Andrey Polbin & Nikita Fokin, 2021. "The Impact of Global Economic Activity, Oil Supply and Speculative Oil Shocks on the Russian Economy," HSE Economic Journal, National Research University Higher School of Economics, vol. 25(2), pages 227-262.
    124. Donayre, Luiggi & Panovska, Irina, 2016. "State-dependent exchange rate pass-through behavior," Journal of International Money and Finance, Elsevier, vol. 64(C), pages 170-195.
    125. Ham, John C. & Woutersen, Tiemen, 2011. "Calculating Confidence Intervals for Continuous and Discontinuous Functions of Estimated Parameters," IZA Discussion Papers 5816, Institute of Labor Economics (IZA).
    126. Tachibana, Minoru, 2013. "How have inflation-targeting central banks responded to supply shocks?," Economics Letters, Elsevier, vol. 121(1), pages 1-3.
    127. Gafarov, Bulat & Meier, Matthias & Montiel Olea, José Luis, 2018. "Delta-method inference for a class of set-identified SVARs," Journal of Econometrics, Elsevier, vol. 203(2), pages 316-327.
    128. Sangyup Choi, 2018. "Bank Lending Standards, Loan Demand, and the Macroeconomy: Evidence from the Emerging Market Bank Loan Officer Survey," Working papers 2018rwp-126, Yonsei University, Yonsei Economics Research Institute.
    129. Keating, John W., 2013. "What do we learn from Blanchard and Quah decompositions of output if aggregate demand may not be long-run neutral?," Journal of Macroeconomics, Elsevier, vol. 38(PB), pages 203-217.
    130. Sara Boni & Francesco Ravazzolo, 2022. "A Structural Analysis of Unemployment-Generating Supply Shocks with an Application to the US Pharmaceutical Industry," BEMPS - Bozen Economics & Management Paper Series BEMPS94, Faculty of Economics and Management at the Free University of Bozen.
    131. Hiroshi Morita, 2017. "Effects of Anticipated Fiscal Policy Shock on Macroeconomic Dynamics in Japan," The Japanese Economic Review, Japanese Economic Association, vol. 68(3), pages 364-393, September.

  19. Barbara Rossi & Atsushi Inoue, 2011. "Out-of-Sample Forecast Tests Robust to Window Size Choice," Working Papers 11-04, Duke University, Department of Economics.

    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
    3. Tae-Hwy Lee & Weiping Yang, 2012. "Money–Income Granger-Causality in Quantiles," Advances in Econometrics, in: 30th Anniversary Edition, pages 385-409, Emerald Group Publishing Limited.
    4. 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.
    5. Dai, Zhifeng & Zhang, Xiaotong & Li, Tingyu, 2023. "Forecasting stock return volatility in data-rich environment: A new powerful predictor," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    6. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    7. Atsushi Inoue & Barbara Rossi, 2011. "Out-of-sample forecast tests robust to the choice of window size," Working Papers 11-31, Federal Reserve Bank of Philadelphia.
    8. Barbara Rossi, 2012. "Comment on "Taylor Rule Exchange Rate Forecasting during the Financial Crisis"," NBER Chapters, in: NBER International Seminar on Macroeconomics 2012, pages 106-116, National Bureau of Economic Research, Inc.
    9. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.

  20. Rossi, Barbara & Inoue, Atsushi, 2011. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," CEPR Discussion Papers 8542, C.E.P.R. Discussion Papers.

    Cited by:

    1. Bańbura, Marta & Bobeica, Elena, 2020. "Does the Phillips curve help to forecast euro area inflation?," Working Paper Series 2471, European Central Bank.
    2. Zhang, Li & Wang, Lu & Wang, Xunxiao & Zhang, Yaojie & Pan, Zhigang, 2022. "How macro-variables drive crude oil volatility? Perspective from the STL-based iterated combination method," Resources Policy, Elsevier, vol. 77(C).
    3. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    4. 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.
    5. Buncic, Daniel & Gisler, Katja I.M., 2016. "Global equity market volatility spillovers: A broader role for the United States," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1317-1339.
    6. Zhang, Xiaoyun & Guo, Qiang, 2024. "How useful are energy-related uncertainty for oil price volatility forecasting?," Finance Research Letters, Elsevier, vol. 60(C).
    7. Rossi, José Luiz Júnior, 2013. "Liquidity and Exchange Rates," Insper Working Papers wpe_325, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    8. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
    9. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    10. Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
    11. Fotis Papailias & Dimitrios Thomakos, 2015. "Covariance averaging for improved estimation and portfolio allocation," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(1), pages 31-59, February.
    12. Tae-Hwy Lee & Weiping Yang, 2012. "Money–Income Granger-Causality in Quantiles," Advances in Econometrics, in: 30th Anniversary Edition, pages 385-409, Emerald Group Publishing Limited.
    13. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
    14. Duan, Huayou & Zhao, Chenchen & Wang, Lu & Liu, Guangqiang, 2024. "The relationship between renewable energy attention and volatility: A HAR model with markov time-varying transition probability," Research in International Business and Finance, Elsevier, vol. 71(C).
    15. Tatevik Sekhposyan & Barbara Rossi, 2015. "Alternative Tests for Correct Specification of Conditional Predictive Densities," Working Papers 758, Barcelona School of Economics.
    16. 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.
    17. Dai, Zhifeng & Zhang, Xiaotong & Li, Tingyu, 2023. "Forecasting stock return volatility in data-rich environment: A new powerful predictor," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    18. Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng, 2021. "Forecasting stock return volatility using a robust regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1463-1478, December.
    19. Breen, John David & Hu, Liang, 2021. "The predictive content of oil price and volatility: New evidence on exchange rate forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    20. Nicolás Magner & Nicolás Hardy, 2022. "Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle," Mathematics, MDPI, vol. 10(13), pages 1-27, July.
    21. Chen, Shiu-Sheng & Chou, Yu-Hsi, 2023. "Liquidity yield and exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 137(C).
    22. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    23. Nonejad, Nima, 2021. "Predicting equity premium using news-based economic policy uncertainty: Not all uncertainty changes are equally important," International Review of Financial Analysis, Elsevier, vol. 77(C).
    24. Likun Lei & Yaojie Zhang & Yu Wei & Yi Zhang, 2021. "Forecasting the volatility of Chinese stock market: An international volatility index," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1336-1350, January.
    25. Avraham Turgeman & Claudiu Botoc & Marilen Pirtea & Octavian Jude, 0000. "Modelling Intraday Realized Volatility: The Role Of Vix, Oil And Gold," Proceedings of Economics and Finance Conferences 14115804, International Institute of Social and Economic Sciences.
    26. Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
    27. Domenico Ferraro & Ken Rogoff & Barbara Rossi, 2011. "Can oil prices forecast exchange rates?," Economics Working Papers 1461, Department of Economics and Business, Universitat Pompeu Fabra, revised Jan 2015.
    28. Kreye, Tom Jannik & Sibbertsen, Philipp, 2024. "Testing for a Forecast Accuracy Breakdown under Long Memory," Hannover Economic Papers (HEP) dp-729, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    29. Theologos Dergiades & Panos K. Pouliasis, 2021. "Should Stock Returns Predictability be hooked on Long Horizon Regressions?," Discussion Paper Series 2021_03, Department of Economics, University of Macedonia, revised Feb 2021.
    30. Guo, Yangli & He, Feng & Liang, Chao & Ma, Feng, 2022. "Oil price volatility predictability: New evidence from a scaled PCA approach," Energy Economics, Elsevier, vol. 105(C).
    31. Peng, Huan & Chen, Ruoxun & Mei, Dexiang & Diao, Xiaohua, 2018. "Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 78-85.
    32. Valeriy Zakamulin, 2014. "The real-life performance of market timing with moving average and time-series momentum rules," Journal of Asset Management, Palgrave Macmillan, vol. 15(4), pages 261-278, August.
    33. Liang, Chao & Ma, Feng & Li, Ziyang & Li, Yan, 2020. "Which types of commodity price information are more useful for predicting US stock market volatility?," Economic Modelling, Elsevier, vol. 93(C), pages 642-650.
    34. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2015. "Are Indian stock returns predictable?," Working Papers fe_2015_07, Deakin University, Department of Economics.
    35. Norman R. Swanson & Weiqi Xiong & Xiye Yang, 2020. "Predicting interest rates using shrinkage methods, real‐time diffusion indexes, and model combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 587-613, August.
    36. Kolev, Gueorgui I. & Karapandza, Rasa, 2017. "Out-of-sample equity premium predictability and sample split–invariant inference," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 188-201.
    37. Rodrigo Sekkel, 2014. "Balance Sheets of Financial Intermediaries: Do They Forecast Economic Activity?," Staff Working Papers 14-40, Bank of Canada.
    38. Nicolas S. Magner & Nicolás Hardy & Tiago Ferreira & Jaime F. Lavin, 2023. "“Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis," Mathematics, MDPI, vol. 11(7), pages 1-16, March.
    39. Lu, Fei & Ma, Feng & Guo, Qiang, 2023. "Less is more? New evidence from stock market volatility predictability," International Review of Financial Analysis, Elsevier, vol. 89(C).
    40. Hardy, Nicolás & Ferreira, Tiago & Quinteros, Maria J. & Magner, Nicolás S., 2023. "“Watch your tone!”: Forecasting mining industry commodity prices with financial report tone," Resources Policy, Elsevier, vol. 86(PA).
    41. Li, Dongxin & Zhang, Li & Li, Lihong, 2023. "Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model," International Review of Financial Analysis, Elsevier, vol. 88(C).
    42. Zhang, Yaojie & Ma, Feng & Wei, Yu, 2019. "Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches," Energy Economics, Elsevier, vol. 81(C), pages 1109-1120.
    43. Firmin Doko Tchatoka & Qazi Haque, 2023. "On bootstrapping tests of equal forecast accuracy for nested models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1844-1864, November.
    44. Peng Ye & Yong Li & Abu Bakkar Siddik, 2023. "Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm," Energies, MDPI, vol. 16(11), pages 1-39, June.
    45. Wang, Lu & Wu, Jiangbin & Cao, Yang & Hong, Yanran, 2022. "Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?," Energy Economics, Elsevier, vol. 111(C).
    46. Yi, Yongsheng & He, Mengxi & Zhang, Yaojie, 2022. "Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    47. Martin Enilov & Yuan Wang, 2022. "Tourism and economic growth: Multi-country evidence from mixed-frequency Granger causality tests," Tourism Economics, , vol. 28(5), pages 1216-1239, August.
    48. Ferraro, Domenico & Rogoff, Kenneth & Rossi, Barbara, 2015. "Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates," Journal of International Money and Finance, Elsevier, vol. 54(C), pages 116-141.
    49. Wang, Ping & Han, Wei & Huang, Chengcheng & Duong, Duy, 2022. "Forecasting realised volatility from search volume and overnight sentiment: Evidence from China," Research in International Business and Finance, Elsevier, vol. 62(C).
    50. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    51. 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.
    52. Chen, Jian & Jiang, Fuwei & Liu, Yangshu & Tu, Jun, 2017. "International volatility risk and Chinese stock return predictability," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 183-203.
    53. Steven Y. K. Wong & Jennifer S. K. Chan & Lamiae Azizi & Richard Y. D. Xu, 2022. "Time‐varying neural network for stock return prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 3-18, January.
    54. Chen, Wang & Ma, Feng & Wei, Yu & Liu, Jing, 2020. "Forecasting oil price volatility using high-frequency data: New evidence," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 1-12.
    55. Fuwei Jiang & Joshua Lee & Xiumin Martin & Guofu Zhou, 2019. "Manager sentiment and stock returns," CEMA Working Papers 677, China Economics and Management Academy, Central University of Finance and Economics.
    56. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
    57. Barbara Rossi & Tatevik Sekhposyan, 2014. "Forecast rationality tests in the presence of instabilities, with applications to Federal Reserve and survey forecasts," Economics Working Papers 1426, Department of Economics and Business, Universitat Pompeu Fabra, revised Nov 2014.
    58. Basistha, Arabinda & Kurov, Alexander & Wolfe, Marketa Halova, 2019. "Volatility Forecasting: The Role of Internet Search Activity and Implied Volatility," MPRA Paper 111037, University Library of Munich, Germany.
    59. Hoang, Khoa & Cannavan, Damien & Huang, Ronghong & Peng, Xiaowen, 2021. "Predicting stock returns with implied cost of capital: A partial least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
    60. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
    61. Reikard, Gordon & Robertson, Bryson & Bidlot, Jean-Raymond, 2015. "Combining wave energy with wind and solar: Short-term forecasting," Renewable Energy, Elsevier, vol. 81(C), pages 442-456.
    62. Joseph Agyapong, 2021. "Application of Taylor Rule Fundamentals in Forecasting Exchange Rates," Economies, MDPI, vol. 9(2), pages 1-27, June.
    63. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    64. Chen, Jian & Tang, Guohao & Yao, Jiaquan & Zhou, Guofu, 2023. "Employee sentiment and stock returns," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    65. Peter Reinhard Hansen & Allan Timmermann, 2012. "Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics," CREATES Research Papers 2012-45, Department of Economics and Business Economics, Aarhus University.
    66. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    67. Song, Yixuan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market volatility: A newspaper-based predictor regarding petroleum market volatility," Resources Policy, Elsevier, vol. 79(C).
    68. Guo, Yangli & Ma, Feng & Li, Haibo & Lai, Xiaodong, 2022. "Oil price volatility predictability based on global economic conditions," International Review of Financial Analysis, Elsevier, vol. 82(C).
    69. McGurk, Zachary, 2020. "US real estate inflation prediction: Exchange rates and net foreign assets," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 53-66.
    70. Shiu-Sheng Chen, 2016. "Commodity prices and related equity prices," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 949-967, August.
    71. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
    72. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    73. Francis X. Diebold, 2012. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," NBER Working Papers 18391, National Bureau of Economic Research, Inc.
    74. He, Mengxi & Wang, Yudong & Zeng, Qing & Zhang, Yaojie, 2023. "Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index," Research in International Business and Finance, Elsevier, vol. 65(C).
    75. Hong, Yanran & Wang, Lu & Liang, Chao & Umar, Muhammad, 2022. "Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework," Resources Policy, Elsevier, vol. 77(C).
    76. Yaojie Zhang & Mengxi He & Danyan Wen & Yudong Wang, 2022. "Forecasting Bitcoin volatility: A new insight from the threshold regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 633-652, April.
    77. Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019. "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 466-477.
    78. Khoa Hoang & Robert Faff, 2021. "Is the ex‐ante equity risk premium always positive? Evidence from a new conditional expectations model," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(1), pages 95-124, March.
    79. Xu, Qifa & Zhuo, Xingxuan & Jiang, Cuixia & Liu, Xi & Liu, Yezheng, 2018. "Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth," Economic Modelling, Elsevier, vol. 75(C), pages 221-236.
    80. Yu, Miao & Song, Jinguo, 2018. "Volatility forecasting: Global economic policy uncertainty and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 316-323.
    81. Zhou, Zhongbao & Fu, Zhangyan & Jiang, Yong & Zeng, Ximei & Lin, Ling, 2020. "Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 34(C).
    82. Deryugina, Elena & Ponomarenko, Alexey & Rozhkova, Anna, 2020. "When are credit gap estimates reliable?," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 221-238.
    83. Feng Ma & M. I. M. Wahab & Julien Chevallier & Ziyang Li, 2023. "A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 60-75, January.
    84. Zhang, Yaojie & Zeng, Qing & Ma, Feng & Shi, Benshan, 2019. "Forecasting stock returns: Do less powerful predictors help?," Economic Modelling, Elsevier, vol. 78(C), pages 32-39.
    85. 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.
    86. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
    87. Zhang, Yaojie & Wang, Yudong, 2023. "Forecasting crude oil futures market returns: A principal component analysis combination approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 659-673.
    88. Liang, Chao & Li, Yan & Ma, Feng & Wei, Yu, 2021. "Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information," International Review of Financial Analysis, Elsevier, vol. 75(C).
    89. 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.
    90. Enoch Cheng & Clemens C. Struck, 2019. "Time-Series Momentum: A Monte-Carlo Approach," Working Papers 201906, School of Economics, University College Dublin.
    91. Lu Wang & Feng Ma & Guoshan Liu, 2020. "Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 797-810, August.
    92. Chen, Jian & Jiang, Fuwei & Li, Hongyi & Xu, Weidong, 2016. "Chinese stock market volatility and the role of U.S. economic variables," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 70-83.
    93. Stephen G. Hall & George S. Tavlas & Yongli Wang, 2023. "Forecasting inflation: The use of dynamic factor analysis and nonlinear combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 514-529, April.
    94. Wen, Danyan & Wang, Huihui & Wang, Yudong & Xiao, Jihong, 2024. "Crude oil futures and the short-term price predictability of petroleum products," Energy, Elsevier, vol. 307(C).
    95. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    96. Abbate, Angela & Marcellino, Massimiliano, 2016. "Point, interval and density forecasts of exchange rates with time-varying parameter models," Discussion Papers 19/2016, Deutsche Bundesbank.
    97. Brooks, Chris & Burke, Simon P. & Stanescu, Silvia, 2016. "Finite sample weighting of recursive forecast errors," International Journal of Forecasting, Elsevier, vol. 32(2), pages 458-474.
    98. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    99. Jiqian Wang & Feng Ma & Chao Liang & Zhonglu Chen, 2022. "Volatility forecasting revisited using Markov‐switching with time‐varying probability transition," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1387-1400, January.
    100. Chen, Juan & Xiao, Zuoping & Bai, Jiancheng & Guo, Hongling, 2023. "Predicting volatility in natural gas under a cloud of uncertainties," Resources Policy, Elsevier, vol. 82(C).
    101. Mörstedt, Torsten & Lutz, Bernhard & Neumann, Dirk, 2024. "Cross validation based transfer learning for cross-sectional non-linear shrinkage: A data-driven approach in portfolio optimization," European Journal of Operational Research, Elsevier, vol. 318(2), pages 670-685.
    102. Dai, Zhifeng & Zhou, Huiting & Wen, Fenghua & He, Shaoyi, 2020. "Efficient predictability of stock return volatility: The role of stock market implied volatility," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    103. Guo, Xiaozhu & Huang, Yisu & Liang, Chao & Umar, Muhammad, 2022. "Forecasting volatility of EUA futures: New evidence," Energy Economics, Elsevier, vol. 110(C).
    104. Zhang, Yaojie & Ma, Feng & Shi, Benshan & Huang, Dengshi, 2018. "Forecasting the prices of crude oil: An iterated combination approach," Energy Economics, Elsevier, vol. 70(C), pages 472-483.
    105. 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.
    106. Chen, Jian & Jiang, Fuwei & Xue, Shuyu & Yao, Jiaquan, 2019. "The world predictive power of U.S. equity market skewness risk," Journal of International Money and Finance, Elsevier, vol. 96(C), pages 210-227.
    107. Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
    108. 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.
    109. 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.
    110. Torben Klarl, 2019. "The response of CO2 emissions to the business cycle: New evidence for the U.S," Bremen Papers on Economics & Innovation 1902, University of Bremen, Faculty of Business Studies and Economics.
    111. Lee Tae-Hwy & Xi Zhou & Zhang Ru, 2013. "Testing for Neglected Nonlinearity Using Artificial Neural Networks with Many Randomized Hidden Unit Activations," Journal of Time Series Econometrics, De Gruyter, vol. 5(1), pages 61-68, January.
    112. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    113. 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.
    114. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
    115. Christophe Amat & Tomasz Michalski & Gilles Stoltz, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Working Papers halshs-01003914, HAL.
    116. Chao Liang & Yu Wei & Likun Lei & Feng Ma, 2022. "Global equity market volatility forecasting: New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 594-609, January.
    117. Ye, Wuyi & Xia, Wenjing & Wu, Bin & Chen, Pengzhan, 2022. "Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market," International Review of Financial Analysis, Elsevier, vol. 83(C).
    118. Zhang, Yaojie & Ma, Feng & Zhu, Bo, 2019. "Intraday momentum and stock return predictability: Evidence from China," Economic Modelling, Elsevier, vol. 76(C), pages 319-329.
    119. Anibal Emiliano Da Silva Neto & Jesús Gonzalo & Jean‐Yves Pitarakis, 2021. "Uncovering Regimes in Out of Sample Forecast Errors from Predictive Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 713-741, June.
    120. Mei, Dexiang & Zeng, Qing & Cao, Xiang & Diao, Xiaohua, 2019. "Uncertainty and oil volatility: New evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 155-163.
    121. Yu‐Sheng Lai, 2018. "Estimation of the optimal futures hedge ratio for equity index portfolios using a realized beta generalized autoregressive conditional heteroskedasticity model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(11), pages 1370-1390, November.
    122. Mei, Dexiang & Xie, Yutang, 2022. "U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?," Finance Research Letters, Elsevier, vol. 48(C).
    123. Chang, Chih-Hao & Chen, Zih-Bing & Huang, Shih-Feng, 2022. "Forecasting of high-resolution electricity consumption with stochastic climatic covariates via a functional time series approach," Applied Energy, Elsevier, vol. 309(C).
    124. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics, Canadian Economics Association, vol. 51(3), pages 695-746, August.
    125. Michael W. McCracken, 2020. "Tests of Conditional Predictive Ability: Existence, Size, and Power," Working Papers 2020-050, Federal Reserve Bank of St. Louis.
    126. Batten, Jonathan A. & Mo, Di & Pourkhanali, Armin, 2024. "Can inflation predict energy price volatility?," Energy Economics, Elsevier, vol. 129(C).
    127. Jin-Kyu Jung & Manasa Patnam & Anna Ter-Martirosyan, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
    128. Richard A. Ashley & Kwok Ping Tsang, 2014. "Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach," Econometrics, MDPI, vol. 2(1), pages 1-20, March.
    129. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2018. "Forecasting the prices of crude oil using the predictor, economic and combined constraints," Economic Modelling, Elsevier, vol. 75(C), pages 237-245.
    130. Nonejad, Nima, 2020. "Crude oil price changes and the United Kingdom real gross domestic product growth rate: An out-of-sample investigation," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    131. Jiqian Wang & Rangan Gupta & Oguzhan Cepni & Feng Ma, 2021. "Forecasting International REITs Volatility: The Role of Oil-Price Uncertainty," Working Papers 202173, University of Pretoria, Department of Economics.
    132. 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).
    133. Ma, Feng & Guo, Yangli & Chevallier, Julien & Huang, Dengshi, 2022. "Macroeconomic attention, economic policy uncertainty, and stock volatility predictability," International Review of Financial Analysis, Elsevier, vol. 84(C).
    134. Rossi, José Luiz Júnior, 2014. "The Usefulness of Financial Variables in Predicting Exchange Rate Movements," Insper Working Papers wpe_332, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    135. Papahristodoulou, Christos, 2019. "Is there any theory that explains the SEK?," MPRA Paper 95072, University Library of Munich, Germany, revised 08 Jul 2019.
    136. Atsushi Inoue & Barbara Rossi, 2011. "Out-of-sample forecast tests robust to the choice of window size," Working Papers 11-31, Federal Reserve Bank of Philadelphia.
    137. Zhang, Li & Liang, Chao & Huynh, Luu Duc Toan & Wang, Lu & Damette, Olivier, 2024. "Measuring the impact of climate risk on renewable energy stock volatility: A case study of G20 economies," Journal of Economic Behavior & Organization, Elsevier, vol. 223(C), pages 168-184.
    138. Guo, Xiaozhu & Huang, Dengshi & Li, Xiafei & Liang, Chao, 2023. "Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 672-693.
    139. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    140. Zhang, Li & Li, Yan & Yu, Sixin & Wang, Lu, 2023. "Risk transmission of El Niño-induced climate change to regional Green Economy Index," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 860-872.
    141. Tian, Guangning & Peng, Yuchao & Meng, Yuhao, 2023. "Forecasting crude oil prices in the COVID-19 era: Can machine learn better?," Energy Economics, Elsevier, vol. 125(C).
    142. Nima Nonejad, 2022. "New Findings Regarding the Out-of-Sample Predictive Impact of the Price of Crude Oil on the United States Industrial Production," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(1), pages 1-35, March.
    143. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
    144. Wen, Danyan & Wang, Yudong & Zhang, Yaojie, 2021. "Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism," Economic Modelling, Elsevier, vol. 96(C), pages 209-219.
    145. Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.
    146. Huang, Yisu & Xu, Weiju & Huang, Dengshi & Zhao, Chenchen, 2023. "Chinese crude oil futures volatility and sustainability: An uncertainty indices perspective," Resources Policy, Elsevier, vol. 80(C).
    147. Ma, Feng & Liu, Jing & Wahab, M.I.M. & Zhang, Yaojie, 2018. "Forecasting the aggregate oil price volatility in a data-rich environment," Economic Modelling, Elsevier, vol. 72(C), pages 320-332.
    148. Nandana Sengupta & Fallaw Sowell, 2019. "The Ridge Path Estimator for Linear Instrumental Variables," Papers 1908.09237, arXiv.org.
    149. Zhang, Yaojie & Ma, Feng & Liao, Yin, 2020. "Forecasting global equity market volatilities," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1454-1475.
    150. Dai, Zhifeng & Zhu, Huan & Dong, Xiaodi, 2020. "Forecasting Chinese industry return volatilities with RMB/USD exchange rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    151. Kambouroudis, Dimos S. & McMillan, David G., 2015. "Is there an ideal in-sample length for forecasting volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 114-137.
    152. Caraiani, Petre, 2016. "The role of money in DSGE models: a forecasting perspective," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 315-330.
    153. Nima Nonejad, 2020. "A detailed look at crude oil price volatility prediction using macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1119-1141, November.
    154. Liu, Guangqiang & Guo, Xiaozhu, 2022. "Forecasting stock market volatility using commodity futures volatility information," Resources Policy, Elsevier, vol. 75(C).
    155. Lu, Fei & Ma, Feng & Hu, Shiyang, 2024. "Does energy consumption play a key role? Re-evaluating the energy consumption-economic growth nexus from GDP growth rates forecasting," Energy Economics, Elsevier, vol. 129(C).
    156. Alberto Caruso, 2016. "The Impact of Macroeconomic News on the Euro-Dollar Exchange Rate," Working Papers ECARES ECARES 2016-32, ULB -- Universite Libre de Bruxelles.
    157. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
    158. PERRON, Pierre & YAMAMOTO, Yohei & 山本, 庸平, 2018. "Testing for Changes in Forecasting Performance," Discussion Papers 2018-03, Graduate School of Economics, Hitotsubashi University.
    159. Liu, Jing & Ma, Feng & Yang, Ke & Zhang, Yaojie, 2018. "Forecasting the oil futures price volatility: Large jumps and small jumps," Energy Economics, Elsevier, vol. 72(C), pages 321-330.
    160. Christis Katsouris, 2023. "Predictability Tests Robust against Parameter Instability," Papers 2307.15151, arXiv.org.
    161. Solikin M. Juhro & Dinh Hoang Bach Phan, 2018. "Can Economic Policy Uncertainty Predict Exchange Rate And Its Volatility? Evidence From Asean Countries," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 21(2), pages 251-268, October.
    162. Nonejad, Nima, 2020. "A comprehensive empirical analysis of the predictive impact of the price of crude oil on aggregate equity return volatility," Journal of Commodity Markets, Elsevier, vol. 20(C).
    163. Wang, Lu & Wu, Rui & Ma, WeiChun & Xu, Weiju, 2023. "Examining the volatility of soybean market in the MIDAS framework: The importance of bagging-based weather information," International Review of Financial Analysis, Elsevier, vol. 89(C).
    164. Liang, Chao & Luo, Qin & Li, Yan & Huynh, Luu Duc Toan, 2023. "Global financial stress index and long-term volatility forecast for international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    165. Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
    166. Reikard, Gordon & Haupt, Sue Ellen & Jensen, Tara, 2017. "Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models," Renewable Energy, Elsevier, vol. 112(C), pages 474-485.
    167. Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022. "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, vol. 77(C).
    168. Zhang, Yaojie & Lei, Likun & Wei, Yu, 2020. "Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    169. Mei, Dexiang & Ma, Feng & Liao, Yin & Wang, Lu, 2020. "Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models," Energy Economics, Elsevier, vol. 86(C).
    170. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    171. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
    172. Yu, Jize & Zhang, Li & Peng, Lijuan & Wu, Rui, 2023. "Which component of air quality index drives stock price volatility in China: a decomposition-based forecasting method," Finance Research Letters, Elsevier, vol. 51(C).
    173. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
    174. Li, Dakai, 2024. "Forecasting stock market realized volatility: The role of investor attention to the price of petroleum products," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 115-122.
    175. Liu, Zhichao & Liu, Jing & Zeng, Qing & Wu, Lan, 2022. "VIX and stock market volatility predictability: A new approach," Finance Research Letters, Elsevier, vol. 48(C).
    176. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    177. Wang, Lu & Ma, Feng & Hao, Jianyang & Gao, Xinxin, 2021. "Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?," International Review of Financial Analysis, Elsevier, vol. 76(C).
    178. Valentina Corradi & Norman Swanson, 2013. "Testing for Structural Stability of Factor Augmented Forecasting Models," Departmental Working Papers 201314, Rutgers University, Department of Economics.
    179. Liang, Chao & Xia, Zhenglan & Lai, Xiaodong & Wang, Lu, 2022. "Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model," Energy Economics, Elsevier, vol. 116(C).
    180. Chen, Zhonglu & Zhang, Li & Weng, Chen, 2023. "Does climate policy uncertainty affect Chinese stock market volatility?," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 369-381.
    181. Chen, Zhonglu & Liang, Chao & Umar, Muhammad, 2021. "Is investor sentiment stronger than VIX and uncertainty indices in predicting energy volatility?," Resources Policy, Elsevier, vol. 74(C).
    182. Fang, Yue & Luo, Deming & Yao, Zhongwei, 2024. "Belief dispersion in the Chinese stock market and fund flows," Journal of Banking & Finance, Elsevier, vol. 166(C).
    183. 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.
    184. Song, Yixuan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2023. "Forecasting crude oil prices: A reduced-rank approach," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 698-711.
    185. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
    186. Lu, Fei & Zeng, Qing & Bouri, Elie & Tao, Ying, 2024. "Forecasting US GDP growth rates in a rich environment of macroeconomic data," International Review of Economics & Finance, Elsevier, vol. 95(C).
    187. Steven Y. K. Wong & Jennifer Chan & Lamiae Azizi & Richard Y. D. Xu, 2020. "Time-varying neural network for stock return prediction," Papers 2003.02515, arXiv.org, revised Jan 2021.
    188. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
    189. Mengxi He & Yudong Wang & Yaojie Zhang, 2023. "The predictability of iron ore futures prices: A product‐material lead–lag effect," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1289-1304, September.
    190. 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).
    191. He, Mengxi & Wen, Danyan & Xing, Lu & Zhang, Yaojie, 2024. "Industry volatility concentration and the predictability of aggregate stock market volatility," International Review of Economics & Finance, Elsevier, vol. 95(C).
    192. Su, Yuandong & Liang, Chao & Zhang, Li & Zeng, Qing, 2022. "Uncover the response of the U.S grain commodity market on El Niño–Southern Oscillation," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 98-112.
    193. Hirukawa, Masayuki, 2023. "Robust Covariance Matrix Estimation in Time Series: A Review," Econometrics and Statistics, Elsevier, vol. 27(C), pages 36-61.
    194. Chao Liang & Yaojie Zhang & Xiafei Li & Feng Ma, 2022. "Which predictor is more predictive for Bitcoin volatility? And why?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1947-1961, April.
    195. Xu, Yanyan & Liu, Jing & Ma, Feng & Chu, Jielei, 2024. "Liquidity and realized volatility prediction in Chinese stock market: A time-varying transitional dynamic perspective," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 543-560.
    196. Ma, Feng & Zhang, Yaojie & Huang, Dengshi & Lai, Xiaodong, 2018. "Forecasting oil futures price volatility: New evidence from realized range-based volatility," Energy Economics, Elsevier, vol. 75(C), pages 400-409.
    197. Dong, Dayong & Yue, Sishi & Cao, Jiawei, 2020. "Site visit information content and return predictability: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    198. Golez, Benjamin & Koudijs, Peter, 2018. "Four centuries of return predictability," Journal of Financial Economics, Elsevier, vol. 127(2), pages 248-263.
    199. Zhang, Yue-Jun & Li, Zhao-Chen, 2021. "Forecasting the stock returns of Chinese oil companies: Can investor attention help?," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 531-555.
    200. Nonejad, Nima, 2021. "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, vol. 104(C).
    201. Li, Tao & Ma, Feng & Zhang, Xuehua & Zhang, Yaojie, 2020. "Economic policy uncertainty and the Chinese stock market volatility: Novel evidence," Economic Modelling, Elsevier, vol. 87(C), pages 24-33.
    202. Xiafei Li & Yu Wei & Xiaodan Chen & Feng Ma & Chao Liang & Wang Chen, 2022. "Which uncertainty is powerful to forecast crude oil market volatility? New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4279-4297, October.
    203. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
    204. Guo, Yangli & Li, Pan & Wu, Hanlin, 2023. "Jumps in the Chinese crude oil futures volatility forecasting: New evidence," Energy Economics, Elsevier, vol. 126(C).
    205. Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
    206. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).
    207. Barbara Rossi, 2012. "Comment on "Taylor Rule Exchange Rate Forecasting during the Financial Crisis"," NBER Chapters, in: NBER International Seminar on Macroeconomics 2012, pages 106-116, National Bureau of Economic Research, Inc.
    208. Joscha Beckmann & Dionysius Glycopantis & Keith Pilbeam, 2018. "The dollar–euro exchange rate and monetary fundamentals," Empirical Economics, Springer, vol. 54(4), pages 1389-1410, June.
    209. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.
    210. Lin, Qi & Lin, Xi, 2021. "Cash conversion cycle and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 52(C).

  21. Barbara Rossi & Atsushi Inoue, 2010. "Testing for Weak Identification in Possibly Nonlinear Models," Working Papers 10-92, Duke University, Department of Economics.

    Cited by:

    1. Khalaf, Lynda & Lin, Zhenjiang, 2021. "Projection-based inference with particle swarm optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    2. Inoue, Atsushi & Kuo, Chun-Hung & Rossi, Barbara, 2020. "Identifying the sources of model misspecification," Journal of Monetary Economics, Elsevier, vol. 110(C), pages 1-18.
    3. Yuya Sasaki & Yulong Wang, 2020. "Testing Finite Moment Conditions for the Consistency and the Root-N Asymptotic Normality of the GMM and M Estimators," Papers 2006.02541, arXiv.org, revised Sep 2020.
    4. Arthur Lewbel, 2018. "The Identification Zoo - Meanings of Identification in Econometrics," Boston College Working Papers in Economics 957, Boston College Department of Economics, revised 14 Dec 2019.
    5. Morris, Stephen D., 2017. "DSGE pileups," Journal of Economic Dynamics and Control, Elsevier, vol. 74(C), pages 56-86.
    6. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2013. "Identification-robust analysis of DSGE and structural macroeconomic models," Journal of Monetary Economics, Elsevier, vol. 60(3), pages 340-350.
    7. Bertille Antoine & Eric Renault, 2012. "Testing Identification Strength," Discussion Papers dp12-17, Department of Economics, Simon Fraser University, revised Jan 2017.
    8. Zisimos Koustas & Jean-François Lamarche, 2012. "Instrumental variable estimation of a nonlinear Taylor rule," Empirical Economics, Springer, vol. 42(1), pages 1-20, February.
    9. Rachida Ouysse, 2014. "On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models," Computational Statistics, Springer, vol. 29(1), pages 233-261, February.
    10. Jean-Jacques Forneron, 2019. "Detecting Identification Failure in Moment Condition Models," Papers 1907.13093, arXiv.org, revised Oct 2023.
    11. Forneron, Jean-Jacques, 2024. "Detecting identification failure in moment condition models," Journal of Econometrics, Elsevier, vol. 238(1).
    12. Xiaohong Chen & David Jacho-Chávez & Oliver Linton, 2012. "Averaging of moment condition estimators," CeMMAP working papers CWP26/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    14. Dimitris Hatzinikolaou & Agathi Tsoka, 2016. "Modeling and Estimating the Effects of Institutional Variables on a Pay-as-you-go Social Security System and on Household Saving," Public Finance Review, , vol. 44(5), pages 589-609, September.

  22. Kilian, Lutz & Inoue, Atsushi & Guerron-Quintana, Pablo A., 2009. "Frequentist Inference in Weakly Identified DSGE Models," CEPR Discussion Papers 7447, C.E.P.R. Discussion Papers.

    Cited by:

    1. Frank Schorfheide, 2011. "Estimation and evaluation of DSGE models: progress and challenges," Working Papers 11-7, Federal Reserve Bank of Philadelphia.
    2. Oscar Jorda & Massimiliano Marcellino, 2008. "Path Forecast Evaluation," Working Papers 131, University of California, Davis, Department of Economics.
    3. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2013. "Identification-robust analysis of DSGE and structural macroeconomic models," Journal of Monetary Economics, Elsevier, vol. 60(3), pages 340-350.
    4. Andrle, Michal, 2010. "A note on identification patterns in DSGE models," Working Paper Series 1235, European Central Bank.
    5. Nikolay Iskrev, 2010. "Evaluating the strength of identification in DSGE models. An a priori approach," Working Papers w201032, Banco de Portugal, Economics and Research Department.
    6. Zhongjun Qu, 2011. "Inference and Speci?cation Testing in DSGE Models with Possible Weak Identification," Boston University - Department of Economics - Working Papers Series WP2011-058, Boston University - Department of Economics.

  23. Inoue, Atsushi & Rossi, Barbara, 2008. "Which Structural Parameters Are "Structural"? Identifying the Sources of Instabilities in Economic Models," Working Papers 08-02, Duke University, Department of Economics.

    Cited by:

    1. Philippe Bacchetta & Eric van Wincoop, 2009. "On the Unstable Relationship between Exchange Rates and Macroeconomic Fundamentals," Working Papers 272009, Hong Kong Institute for Monetary Research.
    2. Samuel Hurtado, 2013. "DSGE Models and the Lucas critique," Working Papers 1310, Banco de España.
    3. Emilian DOBRESCU, 2017. "Modelling an Emergent Economy and Parameter Instability Problem," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-28, June.

  24. Hall, Alastair & Inoue, Atsushi & Nason M, James & Rossi, Barbara, 2007. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Working Papers 07-04, Duke University, Department of Economics.

    Cited by:

    1. Müller, Gernot & Wolf, Martin & Hettig, Thomas, 2019. "Exchange Rate Undershooting: Evidence and Theory," CEPR Discussion Papers 13597, C.E.P.R. Discussion Papers.
    2. Ravn, Morten O. & Schmitt-Grohe, Stephanie & Uribe, Martín & Uuskula, Lenno, 2010. "Deep habits and the dynamic effects of monetary policy shocks," Journal of the Japanese and International Economies, Elsevier, vol. 24(2), pages 236-258, June.
    3. Patrick Fève & Julien Matheron & Jean‐Guillaume Sahuc, 2009. "Minimum Distance Estimation and Testing of DSGE Models from Structural VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 883-894, December.
    4. Pablo Guerron-Quintana & Atsushi Inoue & Lutz Kilian, 2016. "Impulse Response Matching Estimators for DSGE Models," CESifo Working Paper Series 5730, CESifo.
    5. Rochelle M. Edge & Thomas Laubach & John C. Williams, 2010. "Welfare‐maximizing monetary policy under parameter uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 129-143, January.
    6. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    7. Karel Mertens & Morten O. Ravn, 2008. "The Aggregate Effects of Anticipated and Unanticipated U.S. Tax Policy Shocks: Theory and Empirical Evidence," Economics Working Papers ECO2008/05, European University Institute.
    8. Karel Mertens & Morten Overgaard Ravn, 2010. "Online Appendix to "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy Shocks"," Online Appendices 09-221, Review of Economic Dynamics.
    9. Prosper Donovon & Alastair R. Hall, 2015. "GMM and Indirect Inference: An appraisal of their connections and new results on their properties under second order identification," Economics Discussion Paper Series 1505, Economics, The University of Manchester.
    10. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2021. "Uncertainty and Monetary Policy during the Great Recession," Economics Working Papers 2021-05, Department of Economics and Business Economics, Aarhus University.
    11. Matteo Barigozzi & Roxana Halbleib & David Veredas, 2012. "Which model to match?," Working Papers 1229, Banco de España.
    12. Punnoose Jacob & Lenno Uuskula, 2016. "Deep habits and exchange rate pass-through," Bank of Estonia Working Papers wp2016-4, Bank of Estonia, revised 19 Jul 2016.
    13. 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.
    14. Cloyne, James, 2014. "Government spending shocks, wealth effects and distortionary taxes," LSE Research Online Documents on Economics 58024, London School of Economics and Political Science, LSE Library.
    15. Ruge-Murcia, Francisco, 2020. "Estimating nonlinear dynamic equilibrium models by matching impulse responses," Economics Letters, Elsevier, vol. 197(C).
    16. Ronayne, David, 2011. "Which Impulse Response Function?," Economic Research Papers 270753, University of Warwick - Department of Economics.
    17. Özer Karagedikli & Troy Matheson & Christie Smith & Shaun P. Vahey, 2007. "RBCs and DSGEs:The Computational Approach to Business Cycle Theory and Evidence," Reserve Bank of New Zealand Discussion Paper Series DP2007/15, Reserve Bank of New Zealand.
    18. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "Testing DSGE Models by indirect inference: a survey of recent findings," Cardiff Economics Working Papers E2018/14, Cardiff University, Cardiff Business School, Economics Section.
    19. Hall, Alastair & Inoue, Atsushi & Nason M, James & Rossi, Barbara, 2007. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Working Papers 07-04, Duke University, Department of Economics.
    20. Minford, Patrick & Wickens, Michael R. & Davidson, James & Meenagh, David, 2010. "Why crises happen - nonstationary macroeconomics," CEPR Discussion Papers 8157, C.E.P.R. Discussion Papers.
    21. Luca Brugnolini, 2018. "About Local Projection Impulse Response Function Reliability," CEIS Research Paper 440, Tor Vergata University, CEIS, revised 09 Jun 2018.
    22. Riccardo DiCecio & Edward Nelson, 2007. "An estimated DSGE model for the United Kingdom," Review, Federal Reserve Bank of St. Louis, vol. 89(Jul), pages 215-232.
    23. Mario Martinoli & Raffaello Seri & Fulvio Corsi, 2024. "Generalized Optimization Algorithms for Complex Objective Functions," LEM Papers Series 2024/18, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    24. Anna Mikusheva, 2014. "Estimation of dynamic stochastic general equilibrium models (in Russian)," Quantile, Quantile, issue 12, pages 1-21, February.
    25. Danthine, Jean-Pierre & Kurmann, André, 2010. "The business cycle implications of reciprocity in labor relations," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 837-850, October.
    26. Poghosyan, K. & Boldea, O., 2011. "Structural versus Matching Estimation : Transmission Mechanisms in Armenia," Discussion Paper 2011-104, Tilburg University, Center for Economic Research.
    27. Pablo A Guerron-Quintana & James M Nason, 2012. "Bayesian Estimation of DSGE Models," CAMA Working Papers 2012-10, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    28. Anna Kormilitsina, 2011. "Oil Price Shocks and the Optimality of Monetary Policy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 199-223, January.
    29. Meenagh, David & Minford, Patrick & Wickens, Michael, 2012. "Testing macroeconomic models by indirect inference on unfiltered data," Cardiff Economics Working Papers E2012/17, Cardiff University, Cardiff Business School, Economics Section.
    30. Òscar Jordà & Sharon Kozicki, 2007. "Estimation and Inference by the Method of Projection Minimum Distance," Staff Working Papers 07-56, Bank of Canada.
    31. Niko Hauzenberger & Florian Huber & Luca Onorante, 2021. "Combining shrinkage and sparsity in conjugate vector autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 304-327, April.
    32. Michael Creel & Dennis Kristensen, 2015. "On Selection of Statistics for Approximate Bayesian Computing or the Method of Simulated Moments," UFAE and IAE Working Papers 950.15, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC), revised 27 Feb 2015.
    33. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2020. "Uncertainty and Monetary Policy during Extreme Events," Economics Working Papers 2020-11, Department of Economics and Business Economics, Aarhus University.
    34. Minford, Patrick & Wickens, Michael R. & Le, Vo Phuong Mai, 2009. "How much nominal rigidity is there in the US Economy? Testing a New Keynesian DSGE model using indirect inference," CEPR Discussion Papers 7537, C.E.P.R. Discussion Papers.
    35. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    36. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "The small sample properties of Indirect Inference in testing and estimating DSGE models," Cardiff Economics Working Papers E2018/7, Cardiff University, Cardiff Business School, Economics Section.
    37. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian Model: a formal test of backward- and forward-looking expectations," MPRA Paper 40278, University Library of Munich, Germany.
    38. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    39. Mario Martinoli & Alessio Moneta & Gianluca Pallante, 2022. "Calibration and Validation of Macroeconomic Simulation Models by Statistical Causal Search," LEM Papers Series 2022/33, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    40. 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.
    41. Michiru Sakane, 2010. "News-Driven International Business Cycles: Effects of the US News Shock on the Canadian Economy," Global COE Hi-Stat Discussion Paper Series gd09-129, Institute of Economic Research, Hitotsubashi University.
    42. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian model: A formal test of backward- and forward-looking behavior," Economics Working Papers 2012-07, Christian-Albrechts-University of Kiel, Department of Economics.
    43. Cengiz Tunc & Denis Pelletier, 2013. "Endogenous Life-Cycle Housing Investment and Portfolio Allocation," Working Papers 1345, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    44. Morten O. Ravn & Karel Mertens, 2009. "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy shocks," 2009 Meeting Papers 480, Society for Economic Dynamics.
    45. Poghosyan, K. & Boldea, O., 2011. "Structural versus Matching Estimation : Transmission Mechanisms in Armenia," Other publications TiSEM cbb75e20-8475-4f79-ba65-d, Tilburg University, School of Economics and Management.
    46. Lucy Minford & David Meenagh, 2020. "Supply-Side Policy and Economic Growth: A Case Study of the UK," Open Economies Review, Springer, vol. 31(1), pages 159-193, February.
    47. Òscar Jordà & Alan M. Taylor, 2024. "Local Projections," NBER Working Papers 32822, National Bureau of Economic Research, Inc.
    48. Fève, Patrick & Matheron, Julien & Sahuc, Jean-Guillaume, 2007. "Optimal Monetary Policy and Technological Shocks in the Post-War US Business Cycle," IDEI Working Papers 484, Institut d'Économie Industrielle (IDEI), Toulouse.
    49. Daniil Lomonosov, 2023. "Shocks of Business Activity and Specific Shocks to Oil Market in DSGE Model of Russian Economy and Their Influence Under Different Monetary Policy Regimes," Russian Journal of Money and Finance, Bank of Russia, vol. 82(4), pages 44-79, December.
    50. Rui Faustino, 2019. "Endogenous Quality and Firm Entry," Working Papers REM 2019/0107, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    51. Francisco RUGE-MURCIA, 2014. "Indirect Inference Estimation of Nonlinear Dynamic General Equilibrium Models : With an Application to Asset Pricing under Skewness Risk," Cahiers de recherche 15-2014, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    52. Tayebeh Sadat Tabatabaei & Pedram Asef, 2021. "Evaluation of Energy Price Liberalization in Electricity Industry: A Data-Driven Study on Energy Economics," Energies, MDPI, vol. 14(22), pages 1-19, November.
    53. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    54. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian Model: a formal test of backward- and forward-looking expectations," MPRA Paper 39669, University Library of Munich, Germany.
    55. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    56. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    57. Guay, Alain & Pelgrin, Florian, 2023. "Structural VAR models in the Frequency Domain," Journal of Econometrics, Elsevier, vol. 236(1).

  25. Jim Nason & Barbara Rossi & Atsushi Inoue & Alastair Hall, 2007. "Information Criteria for Impulse Response Function Matching Estimation," 2007 Meeting Papers 293, Society for Economic Dynamics.

    Cited by:

    1. Ravn, Morten O. & Schmitt-Grohe, Stephanie & Uribe, Martín & Uuskula, Lenno, 2010. "Deep habits and the dynamic effects of monetary policy shocks," Journal of the Japanese and International Economies, Elsevier, vol. 24(2), pages 236-258, June.
    2. Patrick Fève & Julien Matheron & Jean‐Guillaume Sahuc, 2009. "Minimum Distance Estimation and Testing of DSGE Models from Structural VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 883-894, December.
    3. Rochelle M. Edge & Thomas Laubach & John C. Williams, 2010. "Welfare‐maximizing monetary policy under parameter uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 129-143, January.
    4. Karel Mertens & Morten O. Ravn, 2008. "The Aggregate Effects of Anticipated and Unanticipated U.S. Tax Policy Shocks: Theory and Empirical Evidence," Economics Working Papers ECO2008/05, European University Institute.
    5. Ronayne, David, 2011. "Which Impulse Response Function?," Economic Research Papers 270753, University of Warwick - Department of Economics.
    6. Özer Karagedikli & Troy Matheson & Christie Smith & Shaun P. Vahey, 2007. "RBCs and DSGEs:The Computational Approach to Business Cycle Theory and Evidence," Reserve Bank of New Zealand Discussion Paper Series DP2007/15, Reserve Bank of New Zealand.
    7. Hall, Alastair & Inoue, Atsushi & Nason M, James & Rossi, Barbara, 2007. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Working Papers 07-04, Duke University, Department of Economics.
    8. Riccardo DiCecio & Edward Nelson, 2007. "An estimated DSGE model for the United Kingdom," Review, Federal Reserve Bank of St. Louis, vol. 89(Jul), pages 215-232.
    9. Danthine, Jean-Pierre & Kurmann, André, 2010. "The business cycle implications of reciprocity in labor relations," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 837-850, October.
    10. Anna Kormilitsina, 2011. "Oil Price Shocks and the Optimality of Monetary Policy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 199-223, January.
    11. Òscar Jordà & Sharon Kozicki, 2007. "Estimation and Inference by the Method of Projection Minimum Distance," Staff Working Papers 07-56, Bank of Canada.
    12. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    13. Morten O. Ravn & Karel Mertens, 2009. "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy shocks," 2009 Meeting Papers 480, Society for Economic Dynamics.
    14. Fève, Patrick & Matheron, Julien & Sahuc, Jean-Guillaume, 2007. "Optimal Monetary Policy and Technological Shocks in the Post-War US Business Cycle," IDEI Working Papers 484, Institut d'Économie Industrielle (IDEI), Toulouse.
    15. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.

  26. Kilian, Lutz & Inoue, Atsushi & ,, 2006. "Do Actions Speak Louder than Words? Household Expectations of Inflation Based on Micro Consumption Data," CEPR Discussion Papers 5790, C.E.P.R. Discussion Papers.

    Cited by:

    1. Michael J. Lamla & Sarah Lein, 2010. "The Euro Cash Changeover, Inflation Perceptions and the Media," KOF Working papers 10-254, KOF Swiss Economic Institute, ETH Zurich.
    2. Rossi, Barbara & Inoue, Atsushi & Anderson, Emily, 2013. "Heterogeneous Consumers and Fiscal Policy Shocks," CEPR Discussion Papers 9631, C.E.P.R. Discussion Papers.
    3. Eda Gulsen & Hakan Kara, 2020. "Formation of inflation expectations: Does macroeconomic and policy environment matter?," Koç University-TUSIAD Economic Research Forum Working Papers 2017, Koc University-TUSIAD Economic Research Forum.
    4. N. Gregory Mankiw & Ricardo Reis, 2010. "Imperfect Information and Aggregate Supply," NBER Working Papers 15773, National Bureau of Economic Research, Inc.
    5. MURASAWA Yasutomo, 2010. "Measuring Inflation Expectations Using Interval-Coded Data," ESRI Discussion paper series 236, Economic and Social Research Institute (ESRI).
    6. Fabio Canova & Luca Gambetti, 2010. "Do Expectations Matter? The Great Moderation Revisited," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(3), pages 183-205, July.
    7. Yavari, Kazem & Najjarzade, Reza & Tavakolian, Hossein & Bahador, Ali, 2016. "Effect of Nominal Exchange Rate Volatility on Output in Iran’s Economy," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 11(4), pages 419-442, October.
    8. Olivier Armantier & Wändi Bruine de Bruin & Giorgio Topa & Wilbert van der Klaauw & Basit Zafar, 2015. "Inflation Expectations And Behavior: Do Survey Respondents Act On Their Beliefs?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(2), pages 505-536, May.
    9. Carrera, César, 2012. "Estimating Information Rigidity using Firms’ Survey Data," Working Papers 2012-004, Banco Central de Reserva del Perú.
    10. Menz, Jan-Oliver & Poppitz, Philipp, 2013. "Households' disagreement on inflation expectations and socioeconomic media exposure in Germany," Discussion Papers 27/2013, Deutsche Bundesbank.
    11. Gbaguidi, David, 2012. "La courbe de Phillips : temps d’arbitrage et/ou arbitrage de temps," L'Actualité Economique, Société Canadienne de Science Economique, vol. 88(1), pages 87-119, mars.
    12. 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.
    13. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.

  27. Atsushi Inoue & Gary Solon, 2005. "A Portmanteau Test for Serially Correlated Errors in Fixed Effects Models," NBER Technical Working Papers 0310, National Bureau of Economic Research, Inc.

    Cited by:

    1. Paul M. Guest, 2021. "Risk Management in Financial Institutions: A Replication," Journal of Finance, American Finance Association, vol. 76(5), pages 2689-2707, October.
    2. Cepparulo, Alessandra & Eusepi, Giuseppe & Giuriato, Luisa, 2020. "Public finances and Public Private Partnerships in the European Union," MPRA Paper 103918, University Library of Munich, Germany.
    3. Renz, Franziska M. & Vogel, Julian U.N. & Xie, Feixue, 2023. "Do as they say or do as they do? — Uncovering the effects of inappropriate methods and unreliable data in boardroom diversity research," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 410-420.
    4. Srivastava, Preety & Trinh, Trong-Anh, 2021. "The effect of parental smoking on children’s cognitive and non-cognitive skills," Economics & Human Biology, Elsevier, vol. 41(C).
    5. Okui, Ryo, 2009. "Testing serial correlation in fixed effects regression models based on asymptotically unbiased autocorrelation estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2897-2909.
    6. Asiyenur Helhel & Eray Akgun & Yesim Helhel, 2024. "Did ESG Affect the Financial Performance of North American Fast-Moving Consumer Goods Firms in the Second Period of the Kyoto Protocol?," Sustainability, MDPI, vol. 16(22), pages 1-20, November.
    7. Juhl, Ted & Sosa-Escudero, Walter, 2014. "Testing for heteroskedasticity in fixed effects models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 484-494.
    8. Jadiyappa, Nemiraja & Shette, Rachappa, 2024. "CSR regulation and the working capital management policy," Global Finance Journal, Elsevier, vol. 59(C).
    9. Jochmans, Koen, 2020. "A Portmanteau Test For Correlation In Short Panels," Econometric Theory, Cambridge University Press, vol. 36(6), pages 1159-1166, December.
    10. Jochmans, K. & Verardi, V., 2019. "xtserialpm: A portmanteau test for serial correlation in a linear panel model," Cambridge Working Papers in Economics 1944, Faculty of Economics, University of Cambridge.
    11. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.
    12. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    13. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    14. Alejo, Javier & Montes-Rojas, Gabriel & Sosa-Escudero, Walter, 2018. "Testing for serial correlation in hierarchical linear models," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 101-116.
    15. Boto-García, David, 2023. "Investigating the two-way relationship between mobility flows and COVID-19 cases," Economic Modelling, Elsevier, vol. 118(C).
    16. Walter Sosa Escudero, 2007. "Testing for Persistence in the Error Component Model:A One-Sided Approach," Working Papers 94, Universidad de San Andres, Departamento de Economia, revised Feb 2007.
    17. Jochmans, K., 2019. "Testing Correlation in Error-Component Models," Cambridge Working Papers in Economics 1993, Faculty of Economics, University of Cambridge.
    18. Okui Ryo, 2014. "Asymptotically Unbiased Estimation of Autocovariances and Autocorrelations with Panel Data in the Presence of Individual and Time Effects," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 129-181, July.
    19. Tijl Hendrich & Jennifer Buurma-Olsen & Judith Bayer, 2022. "Entries and Regional Growth: The Role of Relatedness," CPB Discussion Paper 433, CPB Netherlands Bureau for Economic Policy Analysis.
    20. Gregory A. Falls & Paul A. Natke & Linlan Xiao, 2022. "College football attendance in the long run: The Football Championship Subdivision," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 2172-2183, September.
    21. Joel Cariolle, 2020. "International Connectivity and the Digital Divide in Sub-Saharan Africa," Working Papers hal-02865546, HAL.
    22. Yamagata, Takashi, 2008. "A joint serial correlation test for linear panel data models," Journal of Econometrics, Elsevier, vol. 146(1), pages 135-145, September.
    23. Koen Jochmans, 2020. "Testing for correlation in error‐component models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 860-878, November.
    24. Attila, Joseph G., 2022. "Does bank deposits volatility react to political instability in developing countries?," Finance Research Letters, Elsevier, vol. 49(C).

  28. Atsushi Inoue & Gary Solon, 2005. "Two-Sample Instrumental Variables Estimators," NBER Technical Working Papers 0311, National Bureau of Economic Research, Inc.

    Cited by:

    1. Jäntti, Markus & Jenkins, Stephen P., 2013. "Income Mobility," IZA Discussion Papers 7730, Institute of Labor Economics (IZA).
    2. Zuo, Hong & Li, Shi & Ge, Zhenyu & Chen, Jialu, 2023. "The impact of education on relative poverty and its intergenerational transmission —— Causal identification based on the Compulsory Education Law," China Economic Review, Elsevier, vol. 82(C).
    3. Simon Freyaldenhoven & Christian Hansen & Jesse M. Shapiro, 2019. "Pre-event Trends in the Panel Event-Study Design," American Economic Review, American Economic Association, vol. 109(9), pages 3307-3338, September.
    4. Nicoletti Cheti & Ermisch John F, 2008. "Intergenerational Earnings Mobility: Changes across Cohorts in Britain," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 7(2), pages 1-38, January.
    5. Gihleb, Rania & Giuntella, Osea, 2017. "Nuns and the effects of catholic schools. Evidence from Vatican II," Journal of Economic Behavior & Organization, Elsevier, vol. 137(C), pages 191-213.
    6. Benjamin Scharadin & Edward C. Jaenicke, 2020. "Time spent on childcare and the household Healthy Eating Index," Review of Economics of the Household, Springer, vol. 18(2), pages 357-386, June.
    7. Michele Cantarella & Chiara Strozzi, 2018. "Labour market effects of crowdwork in the US and EU: an empirical investigation," Center for Economic Research (RECent) 140, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    8. Grenet, Julien & Hart, Robert A & Roberts, J Elizabeth, 2010. "Above and beyond the call. Long-term real earnings effects of British male military conscription in the post-war years," Stirling Economics Discussion Papers 2010-08, University of Stirling, Division of Economics.
    9. Felix Chopras & Ingar Haaland & Christopher Roth, 2024. "The Demand for News: Accuracy Concerns Versus Belief Confirmation Motives," The Economic Journal, Royal Economic Society, vol. 134(661), pages 1806-1834.
    10. Poy, Samuele & Schüller, Simone, 2016. "Internet and Voting in the Web 2.0 Era: Evidence from a Local Broadband Policy," IZA Discussion Papers 9991, Institute of Labor Economics (IZA).
    11. Nancy A. Daza Báez, 2021. "Intergenerational Earnings Mobility in Mexico," DoQSS Working Papers 21-10, Quantitative Social Science - UCL Social Research Institute, University College London.
    12. Henry S Farber & Daniel Herbst & Ilyana Kuziemko & Suresh Naidu, 2021. "Unions and Inequality over the Twentieth Century: New Evidence from Survey Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(3), pages 1325-1385.
    13. Paul J. Devereux & Robert A. Hart, 2008. "Forced to be rich? Returns to compulsory schooling in Britain," Open Access publications 10197/738, School of Economics, University College Dublin.
    14. Berg, Gerard J. van den & Pinger, Pia & Schoch, Johannes, 2012. "Instrumental Variable Estimation of the Causal Effect of Hunger Early in Life on Health Later in Life," Working Papers 12-02, University of Mannheim, Department of Economics.
    15. Poy, Samuele & Schüller, Simone, 2020. "Internet and voting in the social media era: Evidence from a local broadband policy," Research Policy, Elsevier, vol. 49(1).
    16. Martin, Gregory J. & Yurukoglu, Ali, 2017. "Bias in Cable News: Persuasion and Polarization," Research Papers 3343, Stanford University, Graduate School of Business.
    17. Bertoni, Marco & Brunello, Giorgio & Mazzarella, Gianluca, 2018. "Does postponing minimum retirement age improve healthy behaviors before retirement? Evidence from middle-aged Italian workers," Journal of Health Economics, Elsevier, vol. 58(C), pages 215-227.
    18. Jonas Hjort & Xuan Li & Heather Sarsons, 2020. "Across-Country Wage Compression in Multinationals," NBER Working Papers 26788, National Bureau of Economic Research, Inc.
    19. Peter Siminski, 2013. "Employment Effects of Army Service and Veterans' Compensation: Evidence from the Australian Vietnam-Era Conscription Lotteries," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 87-97, March.
    20. Peter Siminski & Simon Ville, 2012. "I Was Only Nineteen, 45 Years Ago: What Can we Learn from Australia’s Conscription Lotteries?," Economics Working Papers wp12-06, School of Economics, University of Wollongong, NSW, Australia.
    21. Evgenia Kogan Dechter, 2014. "Maternity Leave, Effort Allocation, and Postmotherhood Earnings," Journal of Human Capital, University of Chicago Press, vol. 8(2), pages 97-125.
    22. Massimo Bordignon & Di Xiang & Lue Zhan, 2018. "Predicting the Effects of a Sugar Sweetened Beverage Tax in a Household Production Model," DISCE - Working Papers del Dipartimento di Economia e Finanza def075, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    23. Vestman, Roine & Bojeryd, Jesper & Tyrefors, Björn & Kessel, Dany, 2023. "The Housing Wealth Effect: Quasi-Experimental Evidence," CEPR Discussion Papers 18034, C.E.P.R. Discussion Papers.
    24. Guglielmo Barone & Sauro Mocetti, 2016. "Intergenerational mobility in the very long run: Florence 1427-2011," Temi di discussione (Economic working papers) 1060, Bank of Italy, Economic Research and International Relations Area.
    25. Daiji Kawaguchi, 2013. "Fewer School Days, More Inequality," Global COE Hi-Stat Discussion Paper Series gd12-271, Institute of Economic Research, Hitotsubashi University.
    26. Bahadir Dursun & Resul Cesur & Inas Rashad Kelly, 2017. "The Value of Mandating Maternal Education in a Developing Country," NBER Working Papers 23492, National Bureau of Economic Research, Inc.
    27. Kenedi, Gustave & Sirugue, Louis, 2023. "Intergenerational income mobility in France: A comparative and geographic analysis," Journal of Public Economics, Elsevier, vol. 226(C).
    28. Julie L. Hotchkiss & Anil Rupasingha, 2021. "Individual social capital and migration," Growth and Change, Wiley Blackwell, vol. 52(2), pages 808-837, June.
    29. Maribel Jiménez, 2011. "Un Análisis Empírico de las No Linealidades en la Movilidad Intergeneracional del Ingreso. El caso de la Argentina," CEDLAS, Working Papers 0114, CEDLAS, Universidad Nacional de La Plata.
    30. Cervini-Plá, María, 2012. "Exploring the sources of earnings transmission in Spain," MPRA Paper 36093, University Library of Munich, Germany.
    31. Amy Finkelstein & Sarah Miller & Katherine Baicker, 2024. "The Effect of Medicaid on Crime: Evidence from the Oregon Health Insurance Experiment," NBER Working Papers 33244, National Bureau of Economic Research, Inc.
    32. de Vries, Sander & Ketel, Nadine & Lindeboom, Maarten, 2024. "From Parents' Cradle to Children's Career: Intergenerational Effects of Parental Investments," IZA Discussion Papers 17570, Institute of Labor Economics (IZA).
    33. Atsushi Inoue & Tong Li & Qi Xu, 2021. "Two Sample Unconditional Quantile Effect," Papers 2105.09445, arXiv.org.
    34. Yu-Wei Luke Chu & Ming-Jen Lin, 2020. "Intergenerational earnings mobility in Taiwan: 1990–2010," Empirical Economics, Springer, vol. 59(1), pages 11-45, July.
    35. Hans van Kippersluis, & Owen O’Donnell & Eddy van Doorslaer, 2011. "Long-Run Returns to Education: Does Schooling Lead to an Extended Old Age?," Journal of Human Resources, University of Wisconsin Press, vol. 46(4), pages 695-721.
    36. Wei Zou & Ruiqi Ma, 2024. "An Extended Family Perspective on Intergenerational Human Capital Transmission in China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 174(3), pages 1101-1139, September.
    37. Thomas Crossley & Peter Levell & Stavros Poupakis, 2020. "Regression with an imputed dependent variable," IFS Working Papers W20/25, Institute for Fiscal Studies.
    38. Piraino, Patrizio, 2014. "Intergenerational earnings mobility and equality of opportunity in South Africa," SALDRU Working Papers 131, Southern Africa Labour and Development Research Unit, University of Cape Town.
    39. Cervini-Plá, María, 2011. "Intergenerational earnings and income mobility in Spain," MPRA Paper 34942, University Library of Munich, Germany.
    40. Bertrand GARBINTI & Frédérique SAVIGNAC, 2020. "Accounting for Intergenerational Wealth Mobility in France over the 20th Century: Method and Estimations," Working Papers 2020-16, Center for Research in Economics and Statistics.
    41. Belchior, Carlos Alberto & Gonzaga, Gustavo & Ulyssea, Gabriel, 2023. "Unpacking Neighborhood Effects: Experimental Evidence from a Large-Scale Housing Program in Brazil," IZA Discussion Papers 16113, Institute of Labor Economics (IZA).
    42. Duha T. Altindag & Bahadіr Dursun & Elif S. Filiz, 2022. "The effect of education on unemployment duration," Economic Inquiry, Western Economic Association International, vol. 60(1), pages 21-42, January.
    43. Martin O’Connell & Howard Smith & Øyvind Thomassen, 2023. "A two sample size estimator for large data sets," Economics Series Working Papers 1001, University of Oxford, Department of Economics.
    44. Bhaven Sampat & Heidi L. Williams, 2015. "How Do Patents Affect Follow-On Innovation? Evidence from the Human Genome," NBER Working Papers 21666, National Bureau of Economic Research, Inc.
    45. David Card & Zhuan Pei & David S. Lee & Andrea Weber, 2014. "Inference on Causal Effects in a Generalized Regression Kink Design," Working Papers 83, Brandeis University, Department of Economics and International Business School, revised Jan 2015.
    46. Rashmi Barua & Kevin Lang, 2009. "School Entry, Educational Attainment and Quarter of Birth: A Cautionary Tale of LATE," NBER Working Papers 15236, National Bureau of Economic Research, Inc.
    47. Bertoni, Marco & Brunello, Giorgio, 2014. "Pappa Ante Portas: The Retired Husband Syndrome in Japan," IZA Discussion Papers 8350, Institute of Labor Economics (IZA).
    48. Anja Gaentzsch & Gabriela Zapata Román, 2018. "More educated, less mobile? Diverging trends in income and educational mobility in Chile and Peru," Global Development Institute Working Paper Series 312018, GDI, The University of Manchester.
    49. Jonas Hjort & Xuan Li & Heather Sarsons, 2020. "Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares," Working Papers 2020-15, Becker Friedman Institute for Research In Economics.
    50. Johnston, David W. & Shields, Michael A. & Siminski, Peter, 2015. "Long-Term Health Effects of Vietnam-Era Military Service: A Quasi-Experiment Using Australian Conscription Lotteries," IZA Discussion Papers 8852, Institute of Labor Economics (IZA).
    51. Pacini, David & Windmeijer, Frank, 2016. "Robust inference for the Two-Sample 2SLS estimator," Economics Letters, Elsevier, vol. 146(C), pages 50-54.
    52. Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2016. "Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 288-301, April.
    53. Joan Llull, 2015. "The Effect of Immigration on Wages: Exploiting Exogenous Variation at the National Level," Working Papers 783, Barcelona School of Economics.
    54. Erik Meijer & Edward Oczkowski & Tom Wansbeek, 2021. "How measurement error affects inference in linear regression," Empirical Economics, Springer, vol. 60(1), pages 131-155, January.
    55. Leuven, Edwin & Akerman, Anders & Mogstad, Magne, 2018. "Information Frictions, Internet and the Relationship between Distance and Trade," Memorandum 1/2018, Oslo University, Department of Economics, revised 17 Sep 2020.
    56. Jesse Rothstein & Nathan Wozny, 2011. "Permanent Income and the Black-White Test Score Gap," NBER Working Papers 17610, National Bureau of Economic Research, Inc.
    57. Michele Cantarella & Chiara Strozzi, 2018. "Labour market effects of crowdwork in US and EU: an empirical investigation," Department of Economics 0139, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    58. Christian Dustmann & Patrick A. Puhani & Uta Schönberg, 2012. "The Long-term Effects of School Quality on Labor Market Outcomes and Educational Attainment," RF Berlin - CReAM Discussion Paper Series 1208, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    59. Doan, Quang Hung & Nguyen, Ngoc Anh, 2016. "Intergenerational Income Mobility in Vietnam," MPRA Paper 70603, University Library of Munich, Germany.
    60. Andreu Arenas & Clément Malgouyres, 2018. "Countercyclical school attainment and intergenerational mobility," LIDAM Reprints CORE 2976, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    61. Claudia Olivetti & M. Daniele Paserman & Laura Salisbury, 2016. "Three-generation Mobility in the United States, 1850-1940: The Role of Maternal and Paternal Grandparents," Boston College Working Papers in Economics 903, Boston College Department of Economics.
    62. Bauer, Thomas K. & Bender, Stefan & Paloyo, Alfredo R. & Schmidt, Christoph M., 2009. "Evaluating the Labor-Market Effects of Compulsory Military Service," IZA Discussion Papers 4535, Institute of Labor Economics (IZA).
    63. Xiang, Di & Zhan, Lue & Bordignon, Massimo, 2020. "A reconsideration of the sugar sweetened beverage tax in a household production model," Food Policy, Elsevier, vol. 95(C).
    64. Arnaud Lefranc, 2018. "Intergenerational Earnings Persistence and Economic Inequality in the Long Run: Evidence from French Cohorts, 1931–75," Economica, London School of Economics and Political Science, vol. 85(340), pages 808-845, October.
    65. John Jerrim & Álvaro Choi & Rosa Simancas Rodríguez, 2014. "Two-sample two-stage least squares (TSTSLS) estimates of earnings mobility: how consistent are they?," Working Papers 2014/35, Institut d'Economia de Barcelona (IEB).
    66. Anderson, Michael L. & Matsa, David A., 2010. "Are Restaurants Really Supersizing America?," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt4vm5m5vr, Department of Agricultural & Resource Economics, UC Berkeley.
    67. Cantarella, Michele & Strozzi, Chiara, 2019. "Workers in the Crowd: The Labour Market Impact of the Online Platform Economy," IZA Discussion Papers 12327, Institute of Labor Economics (IZA).
    68. Katherine Eriksson, 2015. "Access to Schooling and the Black-White Incarceration Gap in the Early 20th Century US South: Evidence from Rosenwald Schools," NBER Working Papers 21727, National Bureau of Economic Research, Inc.
    69. Hirukawa, Masayuki & Prokhorov, Artem, 2018. "Consistent estimation of linear regression models using matched data," Journal of Econometrics, Elsevier, vol. 203(2), pages 344-358.
    70. Julie L. Hotchkiss & Anil Rupasingha & Thor Watson, 2022. "In-migration and Dilution of Community Social Capital," International Regional Science Review, , vol. 45(1), pages 36-57, January.
    71. Thomas Crossley & Peter Levell & Hamish Low, 2020. "House Price Rises and Borrowing to Invest," IFS Working Papers W20/2, Institute for Fiscal Studies.
    72. Gicheva, Dora, 2012. "Worker mobility, employer-provided general training, and the choice of graduate education," Labour Economics, Elsevier, vol. 19(2), pages 232-240.
    73. Monnet, Jessica, 2019. "The effect of preschool participation on intellectual and behavioral disorder diagnoses: Evidence from surveys on children’s health," Economics of Education Review, Elsevier, vol. 68(C), pages 136-147.
    74. Douglas Almond & Joseph J. Doyle & Amanda E. Kowalski & Heidi Williams, 2010. "Estimating Marginal Returns to Medical Care: Evidence from At-risk Newborns," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(2), pages 591-634.
    75. Christina Gathmann & Hendrik Jürges & Steffen Reinhold, 2012. "Compulsory Schooling Reforms, Education and Mortality in Twentieth Century Europe," CESifo Working Paper Series 3755, CESifo.
    76. Mai Seki, 2013. "Heterogeneous Returns to U.S. College Selectivity and the Value of Graduate Degree Attainment," Staff Working Papers 13-46, Bank of Canada.
    77. Bauer, Thomas K. & Bender, Stefan & Paloyo, Alfredo R. & Schmidt, Christoph M., 2014. "Do guns displace books? The impact of compulsory military service on educational attainment," Economics Letters, Elsevier, vol. 124(3), pages 513-515.
    78. Patulny, Roger & Siminski, Peter & Mendolia, Silvia, 2015. "The front line of social capital creation – A natural experiment in symbolic interaction," Social Science & Medicine, Elsevier, vol. 125(C), pages 8-18.
    79. Chen, Jiaying & Park, Albert, 2021. "School entry age and educational attainment in developing countries: Evidence from China's compulsory education law," Journal of Comparative Economics, Elsevier, vol. 49(3), pages 715-732.
    80. Bertille Antoine & Otilia Boldea & Niccolo Zaccaria, 2024. "Efficient two-sample instrumental variable estimators with change points and near-weak identification," Papers 2406.17056, arXiv.org.
    81. Arnaud Lefranc, 2018. "Intergenerational earnings persistence and economic inequality in the long-run : Evidence from French cohorts, 1931-1975," Post-Print hal-02528217, HAL.
    82. Zhang, Kexin, 2023. "The long-term impact of higher education: Evidence from the Gaokao reinstatement in China," Economics of Education Review, Elsevier, vol. 97(C).
    83. Frisvold, David E., 2015. "Nutrition and cognitive achievement: An evaluation of the School Breakfast Program," Journal of Public Economics, Elsevier, vol. 124(C), pages 91-104.
    84. Agüero,Jorge M. & Fasola,Eniola, 2022. "Distributional Policies and Social Cohesion in a High-Unemployment Setting," Policy Research Working Paper Series 10103, The World Bank.
    85. Kadir Atalay & Garry F. Barrett & Peter Siminski, 2019. "Pension incentives and the joint retirement of couples: evidence from two natural experiments," Journal of Population Economics, Springer;European Society for Population Economics, vol. 32(3), pages 735-767, July.
    86. Keller, Wolfgang & Shiue, Carol, 2014. "The Link Between Fundamentals and Proximate Factors in Development," CEPR Discussion Papers 9883, C.E.P.R. Discussion Papers.
    87. Rashmi Barua & Kevin Lang, 2016. "School Entry, Educational Attainment, and Quarter of Birth: A Cautionary Tale of a Local Average Treatment Effect," Journal of Human Capital, University of Chicago Press, vol. 10(3), pages 347-376.
    88. Brunner, Eric J. & Cho, Sung-Woo & Reback, Randall, 2012. "Mobility, housing markets, and schools: Estimating the effects of inter-district choice programs," Journal of Public Economics, Elsevier, vol. 96(7), pages 604-614.
    89. Noghanibehambari, Hamid & Engelman, Michal, 2022. "Social insurance programs and later-life mortality: Evidence from new deal relief spending," Journal of Health Economics, Elsevier, vol. 86(C).
    90. Julie L. Hotchkiss, 2019. "US Decennial Census return rates: the role of social capital," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 46(5), pages 648-668, January.
    91. Choudhury, Sanchari, 2023. "Non-random selection into entrepreneurship in the realm of government decentralization and corruption," European Journal of Political Economy, Elsevier, vol. 78(C).
    92. Kumari, Meena & Bao, Yanchun & S. Clarke, Paul & Smart, Melissa, 2018. "A comparison of robust methods for Mendelian randomization using multiple genetic variants," ISER Working Paper Series 2018-08, Institute for Social and Economic Research.
    93. Claudia Olivetti & M. Daniele Paserman, 2013. "In the Name of the Son (and the Daughter): Intergenerational Mobility in the United States, 1850-1930," NBER Working Papers 18822, National Bureau of Economic Research, Inc.
    94. John Jerrim & Anna Vignoles & Ross Finnie, 2012. "University access for disadvantaged children: A comparison across English speaking countries," DoQSS Working Papers 12-11, Quantitative Social Science - UCL Social Research Institute, University College London.
    95. Nizam MelikÅŸah Demirtas & Orhan Torul, 2021. "Intergenerational Income Mobility in Turkey Abstract:," Working Papers 2021/05, Bogazici University, Department of Economics.
    96. Reshmaan Hussam, 2014. "Marry Rich, Poor Girl: Investigating the Effects of Sex Selection on Intrahousehold Outcomes in India," Harvard Business School Working Papers 18-029, Harvard Business School.
    97. Prokhorov, Artem & Schmidt, Peter, 2009. "GMM redundancy results for general missing data problems," Journal of Econometrics, Elsevier, vol. 151(1), pages 47-55, July.
    98. Nordin, Mattias, 2019. "Local television, citizen knowledge and U.S. senators' roll-call voting," European Journal of Political Economy, Elsevier, vol. 56(C), pages 212-232.
    99. Panzone, Luca & Hilton, Denis & Sale, Laura & Cohen, Doron, 2016. "Socio-demographics, implicit attitudes, explicit attitudes, and sustainable consumption in supermarket shopping," Journal of Economic Psychology, Elsevier, vol. 55(C), pages 77-95.
    100. Nathan D. Grawe, 2010. "Primary and Secondary School Quality and Intergenerational Earnings Mobility," Journal of Human Capital, University of Chicago Press, vol. 4(4), pages 331-364.
    101. Julie L. Hotchkiss & Anil Rupasingha, 2018. "Wage Determination in Social Occupations: The Role of Individual Social Capital☆," Research in Labor Economics, in: Transitions through the Labor Market, volume 46, pages 127-181, Emerald Group Publishing Limited.
    102. Justine S. Hastings & Ryan E. Kessler & Jesse M. Shapiro, 2019. "The Effect of SNAP on the Composition of Purchased Foods: Evidence and Implications," NBER Working Papers 25953, National Bureau of Economic Research, Inc.
    103. Mocan, Naci & Raschke, Christian & Unel, Bulent, 2015. "The impact of mothers’ earnings on health inputs and infant health," Economics & Human Biology, Elsevier, vol. 19(C), pages 204-223.
    104. Brendan Kline, 2016. "Identification of the Direction of a Causal Effect by Instrumental Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 176-184, April.
    105. Seki, Mai, 2014. "Heterogeneous Returns to U.S. College Selectivity and the Value of Graduate Degree Attainment," CLSSRN working papers clsrn_admin-2014-53, Vancouver School of Economics, revised 25 Nov 2014.
    106. Jose L. Fillat & STEFANIA GARETTO & Lindsay Oldenski, 2014. "Diversification, Cost Structure, and the Risk Premium of Multinational Corporations," Boston University - Department of Economics - Working Papers Series WP2014-007, Boston University - Department of Economics.
    107. Herz, Benedikt, 2017. "Specific Human Capital and Wait Unemployment," MPRA Paper 76777, University Library of Munich, Germany.
    108. Sokbae Lee & Serena Ng, 2020. "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Papers 2007.07781, arXiv.org, revised Jun 2022.
    109. Stephan, Gesine & van den Berg, Gerard & Homrighausen, Pia, 2016. "Randomizing information on a targeted wage support program for older workers: A field experiment," VfS Annual Conference 2016 (Augsburg): Demographic Change 145487, Verein für Socialpolitik / German Economic Association.
    110. DeCicca, Philip & Krashinsky, Harry & Nesson, Erik, 2023. "Rockefellers and Goldwaters: The effect of compulsory schooling on voting preferences," Economics of Education Review, Elsevier, vol. 96(C).
    111. Daniel Mejía & Pascual Restrepo, 2013. "Bushes and Bullets: Illegal Cocaine Markets and Violence in Colombia," Documentos CEDE 11934, Universidad de los Andes, Facultad de Economía, CEDE.
    112. Buchinsky, Moshe & Li, Fanghua & Liao, Zhipeng, 2022. "Estimation and inference of semiparametric models using data from several sources," Journal of Econometrics, Elsevier, vol. 226(1), pages 80-103.

  29. Kilian, Lutz & Inoue, Atsushi, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers.

    Cited by:

    1. Francisco Dias & Maximiano Pinheiro & António Rua, 2010. "Forecasting using targeted diffusion indexes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 341-352.
    2. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    3. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
    4. Eric Hillebrand & Tae-Hwy Lee & Marcelo C. Medeiros, 2012. "Let's Do It Again: Bagging Equity Premium Predictors," CREATES Research Papers 2012-41, Department of Economics and Business Economics, Aarhus University.
    5. Ina Nurmalia Kurniati, 2015. "Forecasting Growth Of Third Party Funds," Working Papers WP/10/2015, Bank Indonesia.
    6. Carlos Capistrán & Allan Timmermann, 2008. "Forecast Combination With Entry and Exit of Experts," CREATES Research Papers 2008-55, Department of Economics and Business Economics, Aarhus University.
    7. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    8. Cheng, Xu & Hansen, Bruce E., 2015. "Forecasting with factor-augmented regression: A frequentist model averaging approach," Journal of Econometrics, Elsevier, vol. 186(2), pages 280-293.
    9. Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics.
    10. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, Department of Economics and Business Economics, Aarhus University.
    11. Rangan Gupta & Mampho P. Modise & Josine Uwilingiye, 2011. "Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors," Working Papers 201122, University of Pretoria, Department of Economics.
    12. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.
    13. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    14. Francesco Audrino & Marcelo C. Medeiros, 2008. "Smooth Regimes, Macroeconomic Variables, and Bagging for the Short-Term Interest Rate Process," University of St. Gallen Department of Economics working paper series 2008 2008-16, Department of Economics, University of St. Gallen.

  30. Inoue, Atsushi & Rossi, Barbara, 2005. "Monitoring and Forecasting Currency Crises," Working Papers 05-02, Duke University, Department of Economics.

    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. 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".
    3. Ryota Nakatani, 2017. "The Effects of Productivity Shocks, Financial Shocks, and Monetary Policy on Exchange Rates: An Application of the Currency Crisis Model and Implications for Emerging Market Crises," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(11), pages 2545-2561, November.
    4. Hyeyoen Kim, 2011. "Large Data Sets, Nonlinearity and the Speed of Adjustment to Real Exchange Rate Shocks," Post-Print hal-00665456, HAL.
    5. Teuta Ismaili Muharremi, 2015. "Currency Crisis Revisited: A Literature Review," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 11(6), pages 117-124, December.
    6. Yucel, Eray, 2011. "A Review and Bibliography of Early Warning Models," MPRA Paper 32893, University Library of Munich, Germany.
    7. Gatopoulos, Georgios & Loubergé, Henri, 2013. "Combined use of foreign debt and currency derivatives under the threat of currency crises: The case of Latin American firms," Journal of International Money and Finance, Elsevier, vol. 35(C), pages 54-75.
    8. 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).

  31. Alastair R. Hall & Atsushi Inoue, 2005. "The Large Sample Behaviour of the Generalized Method of Moments Estimator in Misspecified Models," Econometrics 0505002, University Library of Munich, Germany.

    Cited by:

    1. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
    2. Seojeong Lee, 2014. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for GEL Estimators," Discussion Papers 2014-02, School of Economics, The University of New South Wales.
    3. Magnolfi, Lorenzo & Sullivan, Christopher, 2022. "A comparison of testing and estimation of firm conduct," Economics Letters, Elsevier, vol. 212(C).
    4. Antoine, Bertille & Dovonon, Prosper, 2021. "Robust estimation with exponentially tilted Hellinger distance," Journal of Econometrics, Elsevier, vol. 224(2), pages 330-344.
    5. Onishi, Rikuto & Otsu, Taisuke, 2021. "Sample sensitivity for two-step and continuous updating GMM estimators," Economics Letters, Elsevier, vol. 198(C).
    6. Raymond Kan & Cesare Robotti, 0. "Comment on: Pseudo-True SDFs in Conditional Asset Pricing Models," Journal of Financial Econometrics, Oxford University Press, vol. 18(4), pages 729-735.
    7. Ida Wolden Bache & Øistein Røislanda & Kjersti Næss Torstensen, 2011. "Interest Rate Smoothing and "Calvo-Type" Interest Rate Rules: A Comment on Levine, McAdam, and Pearlman (2007)," International Journal of Central Banking, International Journal of Central Banking, vol. 7(3), pages 79-90, September.
    8. Vadim Marmer & Taisuke Otsu, 2009. "Optimal Comparison of Misspecified Moment Restriction Models under a Chosen Measure of Fit," Cowles Foundation Discussion Papers 1724, Cowles Foundation for Research in Economics, Yale University, revised Jul 2011.
    9. Florian PELGRIN & Alain GUAY & Richard LUGER, 2004. "The New Keynesian Phillips Curve: An empirical assessment," Econometric Society 2004 North American Summer Meetings 418, Econometric Society.
    10. Frank Kleibergen & Zhaoguo Zhan, 2021. "Double robust inference for continuous updating GMM," Papers 2105.08345, arXiv.org.
    11. Ai, Chunrong & Chen, Xiaohong, 2007. "Estimation of possibly misspecified semiparametric conditional moment restriction models with different conditioning variables," Journal of Econometrics, Elsevier, vol. 141(1), pages 5-43, November.
    12. Michael Jansson & Demian Pouzo, 2019. "Towards a general large sample theory for regularized estimators," CeMMAP working papers CWP63/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Jeffrey C. Fuhrer & Glenn D. Rudebusch, 2002. "Estimating the Euler equation for output," Working Papers 02-3, Federal Reserve Bank of Boston.
    14. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    15. Prosper Donovon & Alastair R. Hall, 2015. "GMM and Indirect Inference: An appraisal of their connections and new results on their properties under second order identification," Economics Discussion Paper Series 1505, Economics, The University of Manchester.
    16. George Hall & John Rust, 2002. "Econometric Methods for Endogenously Sampled Time Series: The Case of Commodity Price Speculation in the Steel Market," NBER Technical Working Papers 0278, National Bureau of Economic Research, Inc.
    17. Nikolay Gospodinov & Raymond Kan & Cesare Robotti, 2018. "Asymptotic variance approximations for invariant estimators in uncertain asset-pricing models," Econometric Reviews, Taylor & Francis Journals, vol. 37(7), pages 695-718, August.
    18. Hwang, Jungbin & Kang, Byunghoon & Lee, Seojeong, 2022. "A doubly corrected robust variance estimator for linear GMM," Journal of Econometrics, Elsevier, vol. 229(2), pages 276-298.
    19. Onishi, Rikuto & Otsu, Taisuke, 2021. "Sample sensitivity for two-step and continuous updating GMM estimators," LSE Research Online Documents on Economics 107522, London School of Economics and Political Science, LSE Library.
    20. Mehmet Caner, 2005. "Near Exogeneity and Weak Identification in Generalized Empirical Likelihood Estimators: Fixed and Many Moment Asymptotics," Econometrics 0509018, University Library of Munich, Germany.
    21. Bruce E. Hansen & Seojeong Jay Lee, 2017. "Asymptotic Theory for Clustered Samples," Discussion Papers 2017-18, School of Economics, The University of New South Wales.
    22. Bruce E. Hansen & Seojeong Jay Lee, 2018. "Inference for Iterated GMM Under Misspecification and Clustering," Discussion Papers 2018-07, School of Economics, The University of New South Wales.
    23. Caner, Mehmet, 2014. "Near exogeneity and weak identification in generalized empirical likelihood estimators: Many moment asymptotics," Journal of Econometrics, Elsevier, vol. 182(2), pages 247-268.
    24. Seojeong Lee, 2015. "A Consistent Variance Estimator for 2SLS When Instruments Identify Different LATEs," Discussion Papers 2015-01, School of Economics, The University of New South Wales.
    25. Raymond Kan & Cesare Robotti, 2009. "Model Comparison Using the Hansen-Jagannathan Distance," The Review of Financial Studies, Society for Financial Studies, vol. 22(9), pages 3449-3490, September.
    26. Valentina Corradi & Norman R. Swanson, 2003. "Bootstrap Specification Tests for Diffusion Processes," Departmental Working Papers 200321, Rutgers University, Department of Economics.
    27. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2021. "Optimal Minimax Rates of Specification Testing with Data-driven Bandwidth," KIER Working Papers 1053, Kyoto University, Institute of Economic Research.
    28. Jondeau, E. & Le Bihan, H., 2003. "ML vs GMM Estimates of Hybrid Macroeconomic Models (With an Application to the New Phillips Curve)," Working papers 103, Banque de France.
    29. Jondeau, Eric & Le Bihan, Hervé, 2008. "Examining bias in estimators of linear rational expectations models under misspecification," Journal of Econometrics, Elsevier, vol. 143(2), pages 375-395, April.
    30. Hnatkovska, Viktoria & Marmer, Vadim & Tang, Yao, 2009. "Supplement to "Comparison of Misspecified Calibrated Models"," Microeconomics.ca working papers vadim_marmer-2009-58, Vancouver School of Economics, revised 03 Feb 2011.
    31. Pierre Chausse & Dinghai Xu, 2012. "GMM Estimation of a Stochastic Volatility Model with Realized Volatility: A Monte Carlo Study," Working Papers 1203, University of Waterloo, Department of Economics, revised May 2012.
    32. Arthur Lewbel & Jin-Young Choi & Zhuzhu Zhou, 2019. "Over-Identified Doubly Robust Identification and Estimation," Boston College Working Papers in Economics 1003, Boston College Department of Economics, revised 15 Jan 2022.
    33. Raymond Kan & Cesare Robotti, 2006. "Specification tests of asset pricing models using excess returns," FRB Atlanta Working Paper 2006-10, Federal Reserve Bank of Atlanta.
    34. Chen, Xiaohong & Hansen, Lars Peter & Hansen, Peter G., 2024. "Robust inference for moment condition models without rational expectations," Journal of Econometrics, Elsevier, vol. 243(1).
    35. Bruce E. Hansen & Seojeong Lee, 2021. "Inference for Iterated GMM Under Misspecification," Econometrica, Econometric Society, vol. 89(3), pages 1419-1447, May.
    36. Kiviet, Jan F. & Niemczyk, Jerzy, 2007. "The asymptotic and finite sample distributions of OLS and simple IV in simultaneous equations," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3296-3318, April.
    37. Alexander Tsyplakov, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models (in Russian)," Quantile, Quantile, issue 8, pages 69-122, July.
    38. Richard Ashley & Christopher Parmeter, 2015. "Sensitivity analysis for inference in 2SLS/GMM estimation with possibly flawed instruments," Empirical Economics, Springer, vol. 49(4), pages 1153-1171, December.
    39. Raymond Kan & Cesare Robotti & Jay Shanken, 2009. "Pricing model performance and the two-pass cross-sectional regression methodology," FRB Atlanta Working Paper 2009-11, Federal Reserve Bank of Atlanta.
    40. Gagliardini, Patrick & Trojani, Fabio & Urga, Giovanni, 2005. "Robust GMM tests for structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 139-182.
    41. Geweke, John F. & Horowitz, Joel L. & Pesaran, M. Hashem, 2006. "Econometrics: A Bird's Eye View," IZA Discussion Papers 2458, Institute of Labor Economics (IZA).
    42. Pierluigi Balduzzi & Cesare Robotti, 2005. "Asset-pricing models and economic risk premia: a decomposition," FRB Atlanta Working Paper 2005-13, Federal Reserve Bank of Atlanta.
    43. Michael Creel & Jiti Gao & Han Hong & Dennis Kristensen, 2016. "Bayesian Indirect Inference and the ABC of GMM," Monash Econometrics and Business Statistics Working Papers 1/16, Monash University, Department of Econometrics and Business Statistics.
    44. Lavergne, Pascal, 2015. "Assessing the Approximate Validity of Moment Restrictions," TSE Working Papers 15-562, Toulouse School of Economics (TSE), revised May 2020.
    45. Seojeong Lee, 2018. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Empirical Likelihood Estimators," Papers 1806.00953, arXiv.org, revised Jun 2018.
    46. Timothy B. Armstrong & Michal Koles'r, 2018. "Sensitivity Analysis using Approximate Moment Condition Models," Cowles Foundation Discussion Papers 2158R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2019.
    47. Jean-Jacques Forneron & Liang Zhong, 2023. "Convexity Not Required: Estimation of Smooth Moment Condition Models," Papers 2304.14386, arXiv.org.
    48. Mehmet Caner, 2006. "Near Exogeneity and Weak Identification in Generlized Empirical Likelihood estimators : Fixed and Many Moment Asymptotics," Working Paper 212, Department of Economics, University of Pittsburgh, revised Jan 2006.
    49. Seojeong Lee, 2013. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators," Discussion Papers 2013-09, School of Economics, The University of New South Wales.
    50. Guo, Yingwen & Zhou Z.F., Sherry, 2011. "Duration Analysis of Interest Rate Spells : Cross-National Study of Interest Rate Policy," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 52(1), pages 1-11, June.
    51. Hnatkovska, Viktoria & Marmer, Vadim & Tang, Yao, 2012. "Comparison of misspecified calibrated models: The minimum distance approach," Journal of Econometrics, Elsevier, vol. 169(1), pages 131-138.
    52. Bhardwaj, Geetesh & Corradi, Valentina & Swanson, Norman R., 2008. "A Simulation-Based Specification Test for Diffusion Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 176-193, April.
    53. Andrews, Isaiah, 2019. "On the structure of IV estimands," Journal of Econometrics, Elsevier, vol. 211(1), pages 294-307.
    54. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers 55/13, Institute for Fiscal Studies.
    55. Huigang Chen & Mr. Alin T Mirestean & Mr. Charalambos G Tsangarides, 2011. "Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model," IMF Working Papers 2011/230, International Monetary Fund.
    56. Jan F. Kiviet & Jerzy Niemczyk, 2014. "On the Limiting and Empirical Distributions of IV Estimators When Some of the Instruments are Actually Endogenous," Advances in Econometrics, in: Essays in Honor of Peter C. B. Phillips, volume 33, pages 425-490, Emerald Group Publishing Limited.
    57. A. Felipe & N. Martín & P. Miranda & L. Pardo, 2018. "Testing with Exponentially Tilted Empirical Likelihood," Methodology and Computing in Applied Probability, Springer, vol. 20(4), pages 1319-1358, December.
    58. Mihai Giurcanu & Brett Presnell, 2018. "Bootstrap inference for misspecified moment condition models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 605-630, June.
    59. Man Fai Ip & Kin Wai Chan, 2024. "Inference in Coarsened Time Series via Generalized Method of Moments," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(5), pages 823-846, September.
    60. Tao Chen & Gautam Tripathi, 2011. "Testing Conditional Symmetry Without Smoothing," Working papers 2011-01, University of Connecticut, Department of Economics.
    61. Otilia Boldea & Alastair R. Hall, 2013. "Testing structural stability in macroeconometric models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 9, pages 206-228, Edward Elgar Publishing.
    62. Gao, Xiaodan & Hnatkovska, Viktoria & Marmer, Vadim, 2012. "Limited Participation in International Business Cycle Models: A Formal Evaluation," Microeconomics.ca working papers vadim_marmer-2012-1, Vancouver School of Economics, revised 21 Dec 2013.
    63. Prosper Dovonon, 2016. "Large Sample Properties of the Three-Step Euclidean Likelihood Estimators under Model Misspecification," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 465-514, April.
    64. Patrick Gagliardini & Diego Ronchetti, 2020. "Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 333-394.
    65. Morris Davis & Robert F. Martin, 2005. "Housing, House Prices, and the Equity Premium Revisited," 2005 Meeting Papers 753, Society for Economic Dynamics.
    66. Daniel Berkowitz & Mehmet Caner & Ying Fang, 2013. "The Validity of Instruments Revisited," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    67. Francesco Bravo, 2022. "Misspecified semiparametric model selection with weakly dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 558-586, July.
    68. Morris A. Davis & Robert F. Martin, 2005. "Housing, house prices, and the equity premium puzzle," Finance and Economics Discussion Series 2005-13, Board of Governors of the Federal Reserve System (U.S.).
    69. Jiti Gao & Han Hong, 2014. "A Computational Implementation of GMM," Monash Econometrics and Business Statistics Working Papers 24/14, Monash University, Department of Econometrics and Business Statistics.
    70. Kirill S. Evdokimov & Michal Kolesár, 2018. "Inference in Instrumental Variable Regression Analysis with Heterogeneous Treatment Effects," Working Papers 2018-16, Princeton University. Economics Department..
    71. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    72. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    73. Yaroslav Mukhin, 2018. "Sensitivity of Regular Estimators," Papers 1805.08883, arXiv.org.
    74. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
    75. Junpei Komiyama & Hajime Shimao, 2018. "Cross Validation Based Model Selection via Generalized Method of Moments," Papers 1807.06993, arXiv.org.
    76. Valentin Verdier, 2020. "Average treatment effects for stayers with correlated random coefficient models of panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 917-939, November.
    77. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2018. "Rate Optimal Specification Test When the Number of Instruments is Large," KIER Working Papers 986, Kyoto University, Institute of Economic Research.
    78. Sueishi, Naoya, 2013. "Identification problem of the exponential tilting estimator under misspecification," Economics Letters, Elsevier, vol. 118(3), pages 509-511.
    79. Ying Fang, 2013. "GMM with Weak Identification and Near Exogenneity," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    80. Xu Cheng & Zhipeng Liao & Ruoyao Shi, 2013. "Uniform Asymptotic Risk of Averaging GMM Estimator Robust to Misspecification, Second Version," PIER Working Paper Archive 15-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Mar 2015.
    81. Gospodinov, Nikolay & Maasoumi, Esfandiar, 2021. "Generalized aggregation of misspecified models: With an application to asset pricing," Journal of Econometrics, Elsevier, vol. 222(1), pages 451-467.

  32. Kilian, Lutz & Inoue, Atsushi, 2004. "Bagging Time Series Models," CEPR Discussion Papers 4333, C.E.P.R. Discussion Papers.

    Cited by:

    1. Francisco Dias & Maximiano Pinheiro & António Rua, 2010. "Forecasting using targeted diffusion indexes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 341-352.
    2. Michael McAleer & Marcelo Cunha Medeiros, 2010. "Forecasting Realized Volatility with Linear and Nonlinear Models," Textos para discussão 568, Department of Economics PUC-Rio (Brazil).
    3. Chao Liang & Yi Zhang & Yaojie Zhang, 2022. "Forecasting the volatility of the German stock market: New evidence," Applied Economics, Taylor & Francis Journals, vol. 54(9), pages 1055-1070, February.
    4. Kilian, Lutz & Alquist, Ron & Vigfusson, Robert J., 2011. "Forecasting the Price of Oil," CEPR Discussion Papers 8388, C.E.P.R. Discussion Papers.
    5. Peng, Lijuan & Liang, Chao & Yang, Baoying & Wang, Lu, 2024. "Crude oil volatility forecasting: Insights from a novel time-varying parameter GARCH-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 94(C).
    6. 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.
    7. Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Research Working Paper RWP 09-10, Federal Reserve Bank of Kansas City.
    8. Eric Hillebrand & Marcelo Medeiros, 2010. "The Benefits of Bagging for Forecast Models of Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 571-593.
    9. Eric Hillebrand & Marcelo Cunha Medeiros, 2007. "Forecasting realized volatility models:the benefits of bagging and nonlinear specifications," Textos para discussão 547, Department of Economics PUC-Rio (Brazil).
    10. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
    11. Jin, Daxiang & Yu, Jize, 2023. "Predicting cryptocurrency market volatility: Novel evidence from climate policy uncertainty," Finance Research Letters, Elsevier, vol. 58(PC).
    12. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    13. Liu, Na & Gao, Fumin, 2022. "The world uncertainty index and GDP growth rate," Finance Research Letters, Elsevier, vol. 49(C).
    14. Chao Liang & Yu Wei & Likun Lei & Feng Ma, 2022. "Global equity market volatility forecasting: New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 594-609, January.
    15. 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.
    16. Francesco Audrino & Kameliya Filipova, 2009. "Yield Curve Predictability, Regimes, and Macroeconomic Information: A Data-Driven Approach," University of St. Gallen Department of Economics working paper series 2009 2009-10, Department of Economics, University of St. Gallen.
    17. Francesco Audrino & Marcelo C. Medeiros, 2011. "Modeling and forecasting short‐term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 999-1022, September.
    18. 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.
    19. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
    20. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
    21. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
    22. Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.
    23. Francesco Audrino & Marcelo C. Medeiros, 2008. "Smooth Regimes, Macroeconomic Variables, and Bagging for the Short-Term Interest Rate Process," University of St. Gallen Department of Economics working paper series 2008 2008-16, Department of Economics, University of St. Gallen.
    24. Wang, Jiqian & Huang, Yisu & Ma, Feng & Chevallier, Julien, 2020. "Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence," Energy Economics, Elsevier, vol. 91(C).

  33. Kilian, Lutz & Inoue, Atsushi, 2003. "On the Selection of Forecasting Models," CEPR Discussion Papers 3809, C.E.P.R. Discussion Papers.

    Cited by:

    1. Carlos, Thiago Carlomagno & Marçal, Emerson Fernandes, 2013. "Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon," Textos para discussão 346, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    2. Wright, Jonathan H., 2019. "Some observations on forecasting and policy," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1186-1192.
    3. Atsushi Inoue & Mototsugu Shintani, 2018. "Quasi‐Bayesian model selection," Quantitative Economics, Econometric Society, vol. 9(3), pages 1265-1297, November.
    4. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    5. Kathryn Dominguez & Freyan Panthaki, 2005. "What Defines "News" in Foreign Exchange Markets?," NBER Working Papers 11769, National Bureau of Economic Research, Inc.
    6. 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.
    7. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    8. Filip Staněk, 2023. "Optimal out‐of‐sample forecast evaluation under stationarity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2249-2279, December.
    9. Pierre-Olivier Gourinchas & Hélène Rey, 2007. "International Financial Adjustment," Journal of Political Economy, University of Chicago Press, vol. 115(4), pages 665-703, August.
    10. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    11. Atsushi Inoue & Lutz Kilian & Fatma Burcu Kiraz, 2009. "Do Actions Speak Louder Than Words? Household Expectations of Inflation Based on Micro Consumption Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(7), pages 1331-1363, October.
    12. Kilian, Lutz & Inoue, Atsushi, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers.
    13. Kilian, Lutz & Baumeister, Christiane, 2012. "What Central Bankers Need to Know about Forecasting Oil Prices," CEPR Discussion Papers 9118, C.E.P.R. Discussion Papers.
    14. Kirstin Hubrich & Kenneth D. West, 2008. "Forecast Evaluation of Small Nested Model Sets," NBER Working Papers 14601, National Bureau of Economic Research, Inc.
    15. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production with Automated Procedures," Econometric Society 2004 Latin American Meetings 177, Econometric Society.
    16. Hansen, Bruce E., 2010. "Averaging estimators for autoregressions with a near unit root," Journal of Econometrics, Elsevier, vol. 158(1), pages 142-155, September.
    17. Daniel L. Thornton & Giorgio Valente, 2009. "Revisiting the predictability of bond risk premia," Working Papers 2009-009, Federal Reserve Bank of St. Louis.
    18. Ahmed, Shamim & Liu, Xiaoquan & Valente, Giorgio, 2016. "Can currency-based risk factors help forecast exchange rates?," International Journal of Forecasting, Elsevier, vol. 32(1), pages 75-97.
    19. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo Group, vol. 56(2), pages 192-220, June.
    20. Bec Frédérique & Salem Melika Ben, 2013. "Inventory investment and the business cycle: the usual suspect," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(3), pages 335-343, May.
    21. Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers Archive 32462, Iowa State University, Department of Economics.
    22. Inoue, Atsushi & Kilian, Lutz, 2003. "On the selection of forecasting models," Working Paper Series 214, European Central Bank.
    23. Alexander Vosseler & Enzo Weber, 2018. "Forecasting seasonal time series data: a Bayesian model averaging approach," Computational Statistics, Springer, vol. 33(4), pages 1733-1765, December.
    24. Kilian, Lutz & Alquist, Ron & Vigfusson, Robert J., 2011. "Forecasting the Price of Oil," CEPR Discussion Papers 8388, C.E.P.R. Discussion Papers.
    25. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    26. Matteo Fragetta & Giovanni Melina, 2013. "Identification of monetary policy in SVAR models: a data-oriented perspective," Empirical Economics, Springer, vol. 45(2), pages 831-844, October.
    27. Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB-Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    28. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    29. Panos K. Pouliasis & Nikos C. Papapostolou, 2018. "Volatility and correlation timing: The role of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(11), pages 1407-1439, November.
    30. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Computing in Economics and Finance 2004 112, Society for Computational Economics.
    31. Teodosio Perez-Amaral & Giampiero M. Gallo & Halbert White, 2003. "Flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach (RETINA)," Documentos de Trabajo del ICAE 0309, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    32. Österholm, Pär, 2009. "Improving Unemployment Rate Forecasts Using Survey Data," Working Papers 112, National Institute of Economic Research.
    33. Helmut Herwartz, 2011. "Forecast accuracy and uncertainty in applied econometrics: a recommendation of specific-to-general predictor selection," Empirical Economics, Springer, vol. 41(2), pages 487-510, October.
    34. Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Research Working Paper RWP 09-10, Federal Reserve Bank of Kansas City.
    35. Inci, Ahmet Can & Lu, Biao, 2004. "Exchange rates and interest rates: can term structure models explain currency movements?," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1595-1624, June.
    36. Heikki Kauppi, 2008. "Yield-Curve Based Probit Models for Forecasting U.S. Recessions: Stability and Dynamics," Discussion Papers 31, Aboa Centre for Economics.
    37. Kilian, Lutz & Inoue, Atsushi, 2004. "Bagging Time Series Models," CEPR Discussion Papers 4333, C.E.P.R. Discussion Papers.
    38. Adusei Jumah & Robert M. Kunst, 2008. "Seasonal prediction of European cereal prices: good forecasts using bad models?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 391-406.
    39. Mehmood, Sultan, 2013. "Terrorism and the macroeconomy: Evidence from Pakistan," MPRA Paper 44546, University Library of Munich, Germany.
    40. Barbara Rossi, 2005. "Are Exchange Rates Really Random Walks? Some Evidence Robust to Parameter Instability," International Finance 0503006, University Library of Munich, Germany.
    41. Kilian, Lutz & Manganelli, Simone, 2003. "The central bank as a risk manager: quantifying and forecasting inflation risks," Working Paper Series 226, European Central Bank.
    42. 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.
    43. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    44. Francis X. Diebold, 2012. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," NBER Working Papers 18391, National Bureau of Economic Research, Inc.
    45. 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).
    46. Nima Zarrabi & Stuart Snaith & Jerry Coakley, 2022. "Exchange rate forecasting using economic models and technical trading rules," The European Journal of Finance, Taylor & Francis Journals, vol. 28(10), pages 997-1018, July.
    47. Ahmed, Shamim & Valente, Giorgio, 2015. "Understanding the price of volatility risk in carry trades," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 118-129.
    48. Ching-Kang Ing, 2005. "Accumulated Prediction Errors, Information Criteria And Optimal Forecasting For Autoregressive Time Series," Econometrics 0503020, University Library of Munich, Germany.
    49. Mario Meichle & Angelo Ranaldo & Attilio Zanetti, 2011. "Do financial variables help predict the state of the business cycle in small open economies? Evidence from Switzerland," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(4), pages 435-453, December.
    50. Clark, Todd E. & McCracken, Michael W., 2005. "The power of tests of predictive ability in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 124(1), pages 1-31, January.
    51. Chu, Pyung Kun & Hoff, Kristian & Molnár, Peter & Olsvik, Magnus, 2022. "Crude oil: Does the futures price predict the spot price?," Research in International Business and Finance, Elsevier, vol. 60(C).
    52. 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.
    53. Giovanni Caggiano & Pietro Calice & Leone Leonida, 2013. "Working Paper 190 - Early Warning Systems and Systemic Banking Crises in Low Income Countries: A Multinomial Logit Approach," Working Paper Series 993, African Development Bank.
    54. James Yae & Yang Luo, 2023. "Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
    55. Corina SAMAN, 2015. "Out-Of-Sample Forecasting Performance Of A Robust Neural Exchange Rate Model Of Ron/Usd," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 93-106, March.
    56. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    57. Susanne M. Schennach & Daniel Wilhelm, 2014. "A simple parametric model selection test," CeMMAP working papers 10/14, Institute for Fiscal Studies.
    58. Kuang-Liang Chang & Nan-Kuang Chen & Charles Ka Yui Leung, 2016. "Losing Track of the Asset Markets: the Case of Housing and Stock," International Real Estate Review, Global Social Science Institute, vol. 19(4), pages 435-492.
    59. Brooks, Chris & Burke, Simon P. & Stanescu, Silvia, 2016. "Finite sample weighting of recursive forecast errors," International Journal of Forecasting, Elsevier, vol. 32(2), pages 458-474.
    60. Petropoulos, Fotios & Spiliotis, Evangelos & Panagiotelis, Anastasios, 2023. "Model combinations through revised base rates," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1477-1492.
    61. Della Corte, Pasquale & Sarno, Lucio & Valente, Giorgio, 2010. "A century of equity premium predictability and the consumption-wealth ratio: An international perspective," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 313-331, June.
    62. Taylor, James W., 2008. "Exponentially weighted information criteria for selecting among forecasting models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 513-524.
    63. Todd E. Clark & Michael W. McCracken, 2007. "Combining forecasts from nested models," Finance and Economics Discussion Series 2007-43, Board of Governors of the Federal Reserve System (U.S.).
    64. Kirstin Hubrich & David F. Hendry, 2005. "Forecasting Aggregates by Disaggregates," Computing in Economics and Finance 2005 270, Society for Computational Economics.
    65. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
    66. Lutz Kilian & Simone Manganelli, 2007. "Quantifying the Risk of Deflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2‐3), pages 561-590, March.
    67. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    68. Kaihua Deng, 2015. "Predicting By Learning: An Adaptive Rationale," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-14, December.
    69. Rotger, G.P. & Franses, Ph.H.B.F., 2006. "Forecasting high-frequency electricity demand with a diffusion index model," Econometric Institute Research Papers EI 2006-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    70. Alexandra Bozhechkova & Urmat Dzhunkeev, 2024. "CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 45-69, September.
    71. Helmut Herwartz, 2011. "Specific-to-general predictor selection in approximate autoregressions—Monte Carlo evidence and a large scale performance assessment with real data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 147-168, June.
    72. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    73. Nan Cai & Zongwu Cai & Ying Fang & Qiuhua Xu, 2015. "Forecasting major Asian exchange rates using a new semiparametric STAR model," Empirical Economics, Springer, vol. 48(1), pages 407-426, February.
    74. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España.
    75. Marcus Cobb, 2009. "Forecasting Chilean Inflation From Disaggregate Components," Working Papers Central Bank of Chile 545, Central Bank of Chile.
    76. Peter Vlaar & Ard den Reijer, 2004. "Forecasting inflation: An art as well as a science!," Computing in Economics and Finance 2004 148, Society for Computational Economics.
    77. Skriner, Edith, 2007. "Forecasting Global Flows," Economics Series 214, Institute for Advanced Studies.
    78. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2014. "Predicting BRICS stock returns using ARFIMA models," Applied Financial Economics, Taylor & Francis Journals, vol. 24(17), pages 1159-1166, September.
    79. Duarte, Claudia & Rua, Antonio, 2007. "Forecasting inflation through a bottom-up approach: How bottom is bottom?," Economic Modelling, Elsevier, vol. 24(6), pages 941-953, November.
    80. Lance Bachmeier & Qi Li & Dandan Liu, 2008. "Should Oil Prices Receive So Much Attention? An Evaluation Of The Predictive Power Of Oil Prices For The U.S. Economy," Economic Inquiry, Western Economic Association International, vol. 46(4), pages 528-539, October.
    81. 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.
    82. Aatola, Piia, 2013. "Putting a Price on Carbon – Econometric Essays on the European Union Emissions Trading Scheme and its Impacts," Research Reports P62, VATT Institute for Economic Research.
    83. 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.
    84. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics.
    85. Amor Aniss Benmoussa & Reinhard Ellwanger & Stephen Snudden, 2020. "The New Benchmark for Forecasts of the Real Price of Crude Oil," Staff Working Papers 20-39, Bank of Canada.
    86. 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.
    87. Ghandar, Adam & Michalewicz, Zbigniew & Zurbruegg, Ralf, 2016. "The relationship between model complexity and forecasting performance for computer intelligence optimization in finance," International Journal of Forecasting, Elsevier, vol. 32(3), pages 598-613.
    88. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    89. Costantini, Mauro & Kunst, Robert M., 2011. "On the Usefulness of the Diebold-Mariano Test in the Selection of Prediction Models," Economics Series 276, Institute for Advanced Studies.
    90. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    91. Zhu, Xiaoneng, 2015. "Out-of-sample bond risk premium predictions: A global common factor," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 155-173.
    92. Angelo Marsiglia Fasolo, 2006. "Interdependence and Contagion: an Analysis of Information Transmission in Latin America's Stock Markets," Working Papers Series 112, Central Bank of Brazil, Research Department.
    93. Katharina Hampel & Marcus Kunz & Norbert Schanne & Ruediger Wapler & Antje Weyh, 2006. "Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation," ERSA conference papers ersa06p196, European Regional Science Association.
    94. Paul D. McNelis & Salih N. Neftci, 2006. "Renminbi Revaluation, Euro Appreciation and Chinese Markets: What Can We Learn From Data?," Working Papers 012006, Hong Kong Institute for Monetary Research.
    95. Aatola, Piia & Ollikka, Kimmo & Ollikainen, Markku, 2012. "Informational Efficiency of the EU ETS market – a study of price predictability and profitable trading," Working Papers 28, VATT Institute for Economic Research.
    96. Giacomini, Raffaella & Rossi, Barbara, 2008. "Forecast Comparisons in Unstable Environments," Working Papers 08-04, Duke University, Department of Economics.

  34. Rossi, Barbara & Inoue, Atsushi, 2003. "Recursive Predictability Tests for Real-Time Data," Working Papers 03-24, Duke University, Department of Economics.

    Cited by:

    1. In-Koo Cho & Kenneth Kasa, 2006. "Learning and Model Validation," 2006 Meeting Papers 178, Society for Economic Dynamics.
    2. Norman R. Swanson & Nii Ayi Armah, 2011. "Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output," Departmental Working Papers 201103, Rutgers University, Department of Economics.
    3. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    4. Valentina Corradi & Norman Swanson, 2004. "Bootstrap Procedures for Recursive Estimation Schemes With Applications to Forecast Model Selection," Departmental Working Papers 200418, Rutgers University, Department of Economics.
    5. Todd E. Clark & Michael W. McCracken, 2004. "Improving forecast accuracy by combining recursive and rolling forecasts," Research Working Paper RWP 04-10, Federal Reserve Bank of Kansas City.
    6. 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.
    7. Mihaela NICOLAU & Giulio PALOMBA & Ilaria TRAINI, 2013. "Are Futures Prices Influenced by Spot;Prices or Vice-versa? An Analysis of Crude;Oil, Natural Gas and Gold Markets," Working Papers 394, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    8. Paulo M.M. Rodrigues & Matei Demetrescu, 2019. "Testing for Episodic Predictability in Stock Returns," Working Papers w201906, Banco de Portugal, Economics and Research Department.
    9. Luca FANELLI & Giulio PALOMBA, 2007. "Simulation-Based Tests of Forward-Looking Models Under VAR Learning Dynamics," Working Papers 298, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    10. Rossi, Barbara & Sekhposyan, Tatevik, 2010. "Have economic models' forecasting performance for US output growth and inflation changed over time, and when?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 808-835, October.
    11. Garratt, Anthony & Koop, Gary & Mise, Emi & Vahey, Shaun P., 2009. "Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 480-491.
    12. Stanislav Anatolyev & Grigory Kosenok, 2011. "Sequential Testing with Uniformly Distributed Size," Working Papers w0123, New Economic School (NES).
    13. Fanelli, Luca, 2008. "Evaluating the New Keynesian Phillips Curve under VAR-Based Learning," Economics Discussion Papers 2008-15, Kiel Institute for the World Economy (IfW Kiel).
    14. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    15. Stanislav Anatolyev, 2006. "Nonparametric retrospection and monitoring of predictability of financial returns," Working Papers w0071, New Economic School (NES).
    16. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics.
    17. Nicolau, Mihaela & Palomba, Giulio, 2015. "Dynamic relationships between spot and futures prices. The case of energy and gold commodities," Resources Policy, Elsevier, vol. 45(C), pages 130-143.

  35. Kilian, Lutz & Inoue, Atsushi, 2002. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," CEPR Discussion Papers 3671, C.E.P.R. Discussion Papers.

    Cited by:

    1. Imad Moosa, 2013. "Why is it so difficult to outperform the random walk in exchange rate forecasting?," Applied Economics, Taylor & Francis Journals, vol. 45(23), pages 3340-3346, August.
    2. Libero Monteforte, 2004. "Aggregation bias in macro models: does it matter foir the euro area?," Temi di discussione (Economic working papers) 534, Bank of Italy, Economic Research and International Relations Area.
    3. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    4. Roberto Golinelli & Giuseppe Parigi, 2003. "What is this thing called confidence? A comparative analysis of consumer confidence indices in eight major countries," Temi di discussione (Economic working papers) 484, Bank of Italy, Economic Research and International Relations Area.
    5. David Genesove & James Hansen, 2014. "Predicting Dwelling Prices with Consideration of the Sales Mechanism," RBA Research Discussion Papers rdp2014-09, Reserve Bank of Australia.
    6. Ayinde, Taofeek O. & Olaniran, Abeeb O. & Abolade, Onomeabure C. & Ogbonna, Ahamuefula Ephraim, 2023. "Technology shocks - Gold market connection: Is the effect episodic to business cycle behaviour?," Resources Policy, Elsevier, vol. 84(C).
    7. Pablo Pincheira, 2008. "Combining Tests of Predictive Ability Theory and Evidence for Chilean and Canadian Exchange Rates," Working Papers Central Bank of Chile 459, Central Bank of Chile.
    8. Chen, Shiu-Sheng, 2013. "Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks," MPRA Paper 49240, University Library of Munich, Germany.
    9. Peter Congdon, 2022. "A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic," IJERPH, MDPI, vol. 19(11), pages 1-17, May.
    10. Wang, Cheng & Han, Jing, 2023. "Prospect theory and mutual fund flows: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    11. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," IWH Discussion Papers 2/2019, Halle Institute for Economic Research (IWH).
    12. Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
    13. Julliard, Christian, 2007. "Labor income risk and asset returns," LSE Research Online Documents on Economics 4811, London School of Economics and Political Science, LSE Library.
    14. Jank, Stephan, 2012. "Changes in the composition of publicly traded firms: Implications for the dividend-price ratio and return predictability," CFR Working Papers 12-08, University of Cologne, Centre for Financial Research (CFR).
    15. Kilian, Lutz & Alquist, Ron, 2007. "What Do We Learn from the Price of Crude Oil Futures?," CEPR Discussion Papers 6548, C.E.P.R. Discussion Papers.
    16. Xu, Yongan & Li, Ming & Yan, Wen & Bai, Jiancheng, 2022. "Predictability of the renewable energy market returns: The informational gains from the climate policy uncertainty," Resources Policy, Elsevier, vol. 79(C).
    17. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.
    18. Chan, Kam Fong & Chhagan, Mahesh & Marsden, Alastair, 2017. "Cross-border scheduled macroeconomic news impacts: Evidence from high-frequency Asia Pacific currencies," Pacific-Basin Finance Journal, Elsevier, vol. 43(C), pages 37-54.
    19. John Y. Campbell & Samuel B. Thompson, 2005. "Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?," NBER Working Papers 11468, National Bureau of Economic Research, Inc.
    20. Xu, Hongyi & Katselas, Dean & Drienko, Jo, 2024. "A portfolio-level, sum-of-the-parts approach to return predictability," Journal of Empirical Finance, Elsevier, vol. 78(C).
    21. Ansgar Belke & Thorsten Polleit, 2007. "How the ECB and the US Fed set interest rates," Applied Economics, Taylor & Francis Journals, vol. 39(17), pages 2197-2209.
    22. Todd E. Clark, 2004. "Can out-of-sample forecast comparisons help prevent overfitting?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 115-139.
    23. Giampiero M. Gallo & Edoardo Otranto, 2012. "Realized Volatility and Change of Regimes," Econometrics Working Papers Archive 2012_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Jul 2012.
    24. Kang, Jangkoo & Kim, Tong Suk & Lee, Changjun & Min, Byoung-Kyu, 2011. "Macroeconomic risk and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3158-3173.
    25. Dai, Zhifeng & Zhang, Xiaotong & Li, Tingyu, 2023. "Forecasting stock return volatility in data-rich environment: A new powerful predictor," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    26. Schrimpf, Andreas, 2008. "International Stock Return Predictability Under Model Uncertainty," ZEW Discussion Papers 08-048, ZEW - Leibniz Centre for European Economic Research.
    27. Miguel A. Ferreira & Pedro Santa-Clara, 2008. "Forecasting Stock Market Returns: The Sum of the Parts is More than the Whole," NBER Working Papers 14571, National Bureau of Economic Research, Inc.
    28. Kirstin Hubrich & Kenneth D. West, 2008. "Forecast Evaluation of Small Nested Model Sets," NBER Working Papers 14601, National Bureau of Economic Research, Inc.
    29. Qing Zhou & Robert Faff, 2017. "The complementary role of cross-sectional and time-series information in forecasting stock returns," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 113-139, February.
    30. João Henrique G. Mazzeu & Gloria González-Rivera & Esther Ruiz & Helena Veiga, 2020. "A bootstrap approach for generalized Autocontour testing Implications for VIX forecast densities," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 971-990, November.
    31. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production with Automated Procedures," Econometric Society 2004 Latin American Meetings 177, Econometric Society.
    32. Zachary McGurk & Adam Nowak & Joshua C. Hall, 2019. "Stock Returns and Investor Sentiment: Textual Analysis and Social Media," Working Papers 19-03, Department of Economics, West Virginia University.
    33. Daniel L. Thornton & Giorgio Valente, 2009. "Revisiting the predictability of bond risk premia," Working Papers 2009-009, Federal Reserve Bank of St. Louis.
    34. C. N. V. Krishnan & Peter H. Ritchken & James B. Thomson, 2007. "On forecasting the term structure of credit spreads," Working Papers (Old Series) 0705, Federal Reserve Bank of Cleveland.
    35. Ralph S.J. Koijen & Stijn Van Nieuwerburgh, 2011. "Predictability of Returns and Cash Flows," Annual Review of Financial Economics, Annual Reviews, vol. 3(1), pages 467-491, December.
    36. Rangan Gupta & Christian Pierdzioch & Andrew J. Vivian & Mark E. Wohar, 2018. "The Predictive Value of Inequality Measures for Stock Returns: An Analysis of Long-Span UK Data Using Quantile Random Forests," Working Papers 201809, University of Pretoria, Department of Economics.
    37. Nitschka, Thomas, 2006. "The U.S. consumption-wealth ratio and foreign stock markets: International evidence for return predictability," Technical Reports 2006,11, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    38. Steven J. Jordan & Andrew Vivian & Mark E. Wohar, 2015. "Location, location, location: currency effects and return predictability?," Applied Economics, Taylor & Francis Journals, vol. 47(18), pages 1883-1898, April.
    39. Hambuckers, J. & Ulm, M., 2023. "On the role of interest rate differentials in the dynamic asymmetry of exchange rates," Economic Modelling, Elsevier, vol. 129(C).
    40. David R. Haab & Thomas Nitschka, 2019. "What Goliaths and Davids among Swiss firms tell us about expected returns on Swiss asset markets," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 155(1), pages 1-17, December.
    41. Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers Archive 32462, Iowa State University, Department of Economics.
    42. Kasai, Ndahiriwe & Naraidoo, Ruthira, 2011. "Evaluating the forecasting performance of linear and nonlinear monetary policy rules for South Africa," MPRA Paper 40699, University Library of Munich, Germany.
    43. Inoue, Atsushi & Kilian, Lutz, 2003. "On the selection of forecasting models," Working Paper Series 214, European Central Bank.
    44. Mohamad B. Karaki, 2018. "Asymmetries In The Responses Of Regional Job Flows To Oil Price Shocks," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1827-1845, July.
    45. David E. Rapach & Matthew C. Ringgenberg & Guofu Zhou, 2016. "Short interest and aggregate stock returns," CEMA Working Papers 716, China Economics and Management Academy, Central University of Finance and Economics.
    46. Dai, Zhifeng & Chang, Xiaoming, 2021. "Forecasting stock market volatility: Can the risk aversion measure exert an important role?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    47. Kilian, Lutz & Alquist, Ron & Vigfusson, Robert J., 2011. "Forecasting the Price of Oil," CEPR Discussion Papers 8388, C.E.P.R. Discussion Papers.
    48. Rogoff, Kenneth S. & Chen, Yu-Chin & Rossi, Barbara, 2010. "Can Exchange Rates Forecast Commodity Prices?," Scholarly Articles 29412033, Harvard University Department of Economics.
    49. Brand, Claus & Reimers, Hans-Eggert & Seitz, Franz, 2003. "Forecasting real GDP: what role for narrow money?," Working Paper Series 254, European Central Bank.
    50. Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil market volatility: A comprehensive look at uncertainty variables," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1022-1041.
    51. Francesco Ravazzolo & Philip Rothman, 2011. "Oil and US GDP: A Real-Time out-of Sample Examination," Working Papers No 2/2011, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    52. Ricardo M. Sousa, 2005. "Consumption, (Dis) Aggregate Wealth and Asset Returns," NIPE Working Papers 9/2005, NIPE - Universidade do Minho.
    53. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Computing in Economics and Finance 2004 112, Society for Computational Economics.
    54. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    55. Kaihua Deng & Chang-Jin Kim, 2015. "Predicting Stock Returns — The Information Content Of Predictors Across Horizons," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-27, December.
    56. Krishnan, C.N.V. & Ritchken, Peter H. & Thomson, James B., 2010. "Predicting credit spreads," Journal of Financial Intermediation, Elsevier, vol. 19(4), pages 529-563, October.
    57. Clancy, Daragh & Gabriele, Carmine & Žigraiová, Diana, 2022. "Sovereign bond market spillovers from crisis-time developments in Greece," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    58. Pablo Pincheira, 2013. "A Simple Out-of-Sample Test for the Martingale Difference Hypothesis," Working Papers Central Bank of Chile 698, Central Bank of Chile.
    59. Molodtsova, Tanya & Papell, David H., 2009. "Out-of-sample exchange rate predictability with Taylor rule fundamentals," Journal of International Economics, Elsevier, vol. 77(2), pages 167-180, April.
    60. Ramdane Djoudad & Jack Selody & Carolyn A. Wilkins, 2005. "Does Financial Structure Matter for the Information Content of Financial Indicators?," Staff Working Papers 05-33, Bank of Canada.
    61. Renata Braga Berenguer de Vasconcelos & Joséte Florencio dos Santos & Jackeline Amantino de Andrade, 2021. "Innovation in Micro and Small Enterprises: Resources and Capabilities," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 25(2), pages 190106-1901.
    62. Lutz Kilian & Clara Vega, 2008. "Do energy prices respond to U.S. macroeconomic news? a test of the hypothesis of predetermined energy prices," International Finance Discussion Papers 957, Board of Governors of the Federal Reserve System (U.S.).
    63. Kilian, Lutz & Vigfusson, Robert J., 2011. "Nonlinearities In The Oil Price–Output Relationship," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 337-363, November.
    64. Rangan Gupta & Mampho P. Modise, 2011. "Macroeconomic Variables and South African Stock Return Predictability," Working Papers 201107, University of Pretoria, Department of Economics.
    65. Mr. Guy M Meredith, 2003. "Medium-Term Exchange Rate Forecasting: What Can We Expect?," IMF Working Papers 2003/021, International Monetary Fund.
    66. Martin, Ian & Nagel, Stefan, 2019. "Market Efficiency in the Age of Big Data," CEPR Discussion Papers 14235, C.E.P.R. Discussion Papers.
    67. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
    68. Pham, Quynh Thi Thuy & Rudolf, Markus, 2021. "Gold, platinum, and industry stock returns," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 252-266.
    69. Fullerton, Thomas M., Jr. & Kelley, Brian W., 2008. "El Paso Housing Sector Econometric Forecast Accuracy," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 40(01), pages 1-18, April.
    70. Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
    71. Stephan Jank, 2015. "Changes in the Composition of Publicly Traded Firms: Implications for the Dividend-Price Ratio and Return Predictability," Management Science, INFORMS, vol. 61(6), pages 1362-1377, June.
    72. Junsoo Lee & John A. List & Mark Strazicich, 2005. "Nonrenewable Resource Prices: Deterministic or Stochastic Trends?," NBER Working Papers 11487, National Bureau of Economic Research, Inc.
    73. Dunbar, Kwamie, 2021. "Pricing the hedging factor in the cross-section of stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    74. Ciner, Cetin, 2022. "Predicting the equity market risk premium: A model selection approach," Economics Letters, Elsevier, vol. 215(C).
    75. Massimo Guidolin & Francesco Chincoli, 2017. "Linear and Nonlinear Predictability in Investment Style Factors: Multivariate Evidence," BAFFI CAREFIN Working Papers 1754, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    76. Hubrich, Kirstin & Granziera, Eleonora & Moon, Hyungsik Roger, 2013. "A predictability test for a small number of nested models," Working Paper Series 1580, European Central Bank.
    77. Song, Yixuan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market volatility: A newspaper-based predictor regarding petroleum market volatility," Resources Policy, Elsevier, vol. 79(C).
    78. McGurk, Zachary, 2020. "US real estate inflation prediction: Exchange rates and net foreign assets," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 53-66.
    79. Shiu-Sheng Chen, 2016. "Commodity prices and related equity prices," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 949-967, August.
    80. Gelper, Sarah & Croux, Christophe, 2007. "Multivariate out-of-sample tests for Granger causality," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3319-3329, April.
    81. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    82. Jaqueson K. Galimberti, 2020. "Forecasting GDP growth from outer space," Working Papers 2020-02, Auckland University of Technology, Department of Economics.
    83. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Drivers of Realized Volatility for Emerging Countries with a Focus on South Africa: Fundamentals versus Sentiment," Mathematics, MDPI, vol. 11(6), pages 1-26, March.
    84. Pablo Pincheira & Jorge Selaive, 2011. "External imbalance, valuation adjustments and real Exchange rate: evidence of predictability in an emerging economy," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 26(1), pages 107-125, Junio.
    85. Marmer, Vadim, 2008. "Nonlinearity, nonstationarity, and spurious forecasts," Journal of Econometrics, Elsevier, vol. 142(1), pages 1-27, January.
    86. Todd E. Clark & Kenneth D. West, 2005. "Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference," NBER Technical Working Papers 0305, National Bureau of Economic Research, Inc.
    87. Shiu-Sheng, Chen, 2012. "Predicting swings in exchange rates with macro fundamentals," MPRA Paper 35772, University Library of Munich, Germany.
    88. Francis X. Diebold, 2012. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," NBER Working Papers 18391, National Bureau of Economic Research, Inc.
    89. He, Mengxi & Wang, Yudong & Zeng, Qing & Zhang, Yaojie, 2023. "Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index," Research in International Business and Finance, Elsevier, vol. 65(C).
    90. Pincheira, Pablo, 2017. "A Power Booster Factor for Out-of-Sample Tests of Predictability," MPRA Paper 77027, University Library of Munich, Germany.
    91. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
    92. Ryan Greenaway-McGrevy & Nelson C. Mark & Donggyu Sul & Jyh-Lin Wu, 2012. "Exchange Rates as Exchange Rate Common Factors," Working Papers 212012, Hong Kong Institute for Monetary Research.
    93. Kyriazakou, Eleni & Panagiotidis, Theodore, 2017. "Causality analysis of the Canadian city house price indices: A cross-sample validation approach," The Journal of Economic Asymmetries, Elsevier, vol. 16(C), pages 42-52.
    94. Yin-Wong Cheung & Menzie D. Chinn & Antonio Garcia-Pascual, 2005. "Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive?," Working Papers 122005, Hong Kong Institute for Monetary Research.
    95. Mahir Binici & Yin-Wong Cheung, 2011. "Exchange Rate Dynamics under Alternative Optimal Interest Rate Rules," CESifo Working Paper Series 3577, CESifo.
    96. Sarah Gelper & Christophe Croux, 2010. "On the Construction of the European Economic Sentiment Indicator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 47-62, February.
    97. Javier J. Pérez, 2005. "Early-warning tools to forecast General Government deficit in the euro area: the role of intra-annual fiscal Indicators," Economic Working Papers at Centro de Estudios Andaluces E2005/14, Centro de Estudios Andaluces.
    98. Robert J. Hodrick & Tuomas Tomunen, 2018. "Taking the Cochrane-Piazzesi Term Structure Model Out of Sample: More Data, Additional Currencies, and FX Implications," NBER Working Papers 25092, National Bureau of Economic Research, Inc.
    99. Ricardo M. Sousa, 2011. "Asset Returns Under Model Uncertainty: Evidence from the euro area, the U.K. and the U.S," Working Papers w201119, Banco de Portugal, Economics and Research Department.
    100. Nombulelo Gumata & Alain Kabundi & Eliphas Ndou, 2013. "Important channels of transmission of monetary policy shock in South Africa," Working Papers 6021, South African Reserve Bank.
    101. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    102. Ming-Chih Lee & Chien-Liang Chiu & Wan-Hsiu Cheng, 2007. "Enhancing Forecast Accuracy By Using Long Estimation Periods," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 1(2), pages 1-9.
    103. Yang Bai, 2022. "150 Years of Return Predictability Around the World: A Holistic View," Papers 2209.00121, arXiv.org.
    104. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
    105. Shi, Qi & Li, Bin, 2022. "Further evidence on financial information and economic activity forecasts in the United States," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    106. Ana María Abarca & Felipe Alarcón & Pablo Pincheira & Jorge Selaive, 2007. "Chilean Nominal Exchange Rate: Forecasting Based Upon Technical Analysis," Working Papers Central Bank of Chile 425, Central Bank of Chile.
    107. James Yae & Yang Luo, 2023. "Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
    108. Enoch Cheng & Clemens C. Struck, 2019. "Time-Series Momentum: A Monte-Carlo Approach," Working Papers 201906, School of Economics, University College Dublin.
    109. Alqahtani, Abdullah & Bouri, Elie & Vo, Xuan Vinh, 2020. "Predictability of GCC stock returns: The role of geopolitical risk and crude oil returns," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 239-249.
    110. Michael W. McCracken & Todd E. Clark, 2003. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Computing in Economics and Finance 2003 183, Society for Computational Economics.
    111. Lucas W. Davis & Lutz Kilian, 2011. "The Allocative Cost of Price Ceilings in the U.S. Residential Market for Natural Gas," Journal of Political Economy, University of Chicago Press, vol. 119(2), pages 212-241.
    112. Rapach, David E. & Wohar, Mark E. & Rangvid, Jesper, 2005. "Macro variables and international stock return predictability," International Journal of Forecasting, Elsevier, vol. 21(1), pages 137-166.
    113. Shi, Qi, 2023. "The RP-PCA factors and stock return predictability: An aligned approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    114. Huang, Dashan & Li, Jiangyuan & Wang, Liyao & Zhou, Guofu, 2020. "Time series momentum: Is it there?," Journal of Financial Economics, Elsevier, vol. 135(3), pages 774-794.
    115. Chunming Yuan, 2008. "The Exchange Rate and Macroeconomic Determinants: Time-Varying Transitional Dynamics," UMBC Economics Department Working Papers 09-114, UMBC Department of Economics, revised 01 Nov 2009.
    116. He, Mengxi & Zhang, Zhikai & Zhang, Yaojie, 2024. "Forecasting crude oil prices with global ocean temperatures," Energy, Elsevier, vol. 311(C).
    117. Huang, Wei-Qiang & Wang, Dan, 2020. "Financial network linkages to predict economic output," Finance Research Letters, Elsevier, vol. 33(C).
    118. Jin, Sainan & Corradi, Valentina & Swanson, Norman R., 2017. "Robust Forecast Comparison," Econometric Theory, Cambridge University Press, vol. 33(6), pages 1306-1351, December.
    119. John Cotter & Emmanuel Eyiah-Donkor & Valerio Potì, 2023. "Commodity futures return predictability and intertemporal asset pricing," Post-Print hal-04192933, HAL.
    120. Brooks, Chris & Burke, Simon P. & Stanescu, Silvia, 2016. "Finite sample weighting of recursive forecast errors," International Journal of Forecasting, Elsevier, vol. 32(2), pages 458-474.
    121. Milas, Costas & Naraidoo, Ruthira, 2012. "Financial conditions and nonlinearities in the European Central Bank (ECB) reaction function: In-sample and out-of-sample assessment," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 173-189, January.
    122. Della Corte, Pasquale & Sarno, Lucio & Valente, Giorgio, 2010. "A century of equity premium predictability and the consumption-wealth ratio: An international perspective," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 313-331, June.
    123. Sepehr Ramyar & Farhad Kianfar, 2019. "Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 743-761, February.
    124. Paulo Silva, 2015. "The information content of the open interest of credit default swaps," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(4), pages 381-427, November.
    125. Garratt, Anthony & Koop, Gary & Mise, Emi & Vahey, Shaun P., 2009. "Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 480-491.
    126. Sarno, Lucio & Thornton, Daniel L & Della Corte, Pasquale, 2007. "The Expectation Hypothesis of the Term Structure of Very Short-Term Rates: Statistical Tests and Economic Value," CEPR Discussion Papers 6445, C.E.P.R. Discussion Papers.
    127. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    128. Burns, Kelly & Moosa, Imad A., 2015. "Enhancing the forecasting power of exchange rate models by introducing nonlinearity: Does it work?," Economic Modelling, Elsevier, vol. 50(C), pages 27-39.
    129. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
    130. Ron Alquist & Gregory Bauer & Antonio Diez de los Rios, 2014. "What Does the Convenience Yield Curve Tell Us about the Crude Oil Market?," Staff Working Papers 14-42, Bank of Canada.
    131. Ricardo Mestre & Peter McAdam, 2011. "Is forecasting with large models informative? Assessing the role of judgement in macroeconomic forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 303-324, April.
    132. Ana María Abarca G. & Felipe Alarcón G. & Pablo Pincheira B. & Jorge Selaive C., 2007. "Nominal Exchange Rate in Chile: Predictions based on technical analysis," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 10(2), pages 57-80, August.
    133. Jorge Selaive & Vicente Tuesta, 2004. "Can Fluctuations in the Consumption-Wealth Ratio Help to Predict Exchange Rates?," International Finance 0404014, University Library of Munich, Germany.
    134. Laurence Fung & Ip-wing Yu, 2008. "Predicting Stock Market Returns by Combining Forecasts," Working Papers 0801, Hong Kong Monetary Authority.
    135. Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
    136. Genesove, David & Hansen, James, 2016. "The Role of Auctions and Negotiation in Housing Prices," CEPR Discussion Papers 11392, C.E.P.R. Discussion Papers.
    137. Sumudu W. Watugala, 2015. "Economic Uncertainty and Commodity Futures Volatility," Working Papers 15-14, Office of Financial Research, US Department of the Treasury.
    138. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    139. Yu, Deshui & Huang, Difang, 2023. "Cross-sectional uncertainty and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 321-340.
    140. Kaihua Deng, 2015. "Predicting By Learning: An Adaptive Rationale," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-14, December.
    141. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
    142. Nuno Silva, 2013. "Equity Premia Predictability in the EuroZone," GEMF Working Papers 2013-22, GEMF, Faculty of Economics, University of Coimbra.
    143. Wu, Jyh-Lin & Hu, Yu-Hau, 2009. "New evidence on nominal exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 28(6), pages 1045-1063, October.
    144. Arbués, Ignacio & Matilla-García, Mariano, 2024. "Multibenchmark reality checks," Economic Modelling, Elsevier, vol. 140(C).
    145. Manzan, Sebastiano & Westerhoff, Frank H., 2007. "Heterogeneous expectations, exchange rate dynamics and predictability," Journal of Economic Behavior & Organization, Elsevier, vol. 64(1), pages 111-128, September.
    146. Pan, Zheyao & Chan, Kam Fong, 2018. "A new government bond volatility index predictor for the U.S. equity premium," Pacific-Basin Finance Journal, Elsevier, vol. 50(C), pages 200-215.
    147. Scott Cederburg & Travis L Johnson & Michael S O’Doherty, 2023. "On the Economic Significance of Stock Return Predictability," Review of Finance, European Finance Association, vol. 27(2), pages 619-657.
    148. Hui Guo, 2009. "Data Revisions And Out‐Of‐Sample Stock Return Predictability," Economic Inquiry, Western Economic Association International, vol. 47(1), pages 81-97, January.
    149. Peter Reinhard Hansen & Allan Timmermann, 2015. "Comment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 17-21, January.
    150. Huang, Dashan & Jiang, Fuwei & Li, Kunpeng & Tong, Guoshi & Zhou, Guofu, 2023. "Are bond returns predictable with real-time macro data?," Journal of Econometrics, Elsevier, vol. 237(2).
    151. Zhu, Min, 2013. "Return distribution predictability and its implications for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 209-223.
    152. 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.
    153. Valcarcel, Victor J. & Vivian, Andrew J. & Wohar, Mark E., 2017. "Predictability and underreaction in industry-level returns: Evidence from commodity markets," Journal of Commodity Markets, Elsevier, vol. 6(C), pages 1-15.
    154. Wei Dong & Deokwoo Nam, 2011. "Exchange Rates and Individual Good’s Price Misalignment: Some Preliminary Evidence of Long-Horizon Predictability," Discussion Papers 11-8, Bank of Canada.
    155. Markus Hertrich, 2022. "Foreign exchange interventions under a minimum exchange rate regime and the Swiss franc," Review of International Economics, Wiley Blackwell, vol. 30(2), pages 450-489, May.
    156. Sydney C. Ludvigson, 2004. "Consumer Confidence and Consumer Spending," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 29-50, Spring.
    157. Elliott, Graham & Timmermann, Allan G, 2016. "Forecasting in Economics and Finance," University of California at San Diego, Economics Working Paper Series qt6z55v472, Department of Economics, UC San Diego.
    158. Ren, Xiaohang & Duan, Kun & Tao, Lizhu & Shi, Yukun & Yan, Cheng, 2022. "Carbon prices forecasting in quantiles," Energy Economics, Elsevier, vol. 108(C).
    159. David Haab & Thomas Nitschka, 2017. "Predicting returns on asset markets of a small, open economy and the influence of global risks," Working Papers 2017-14, Swiss National Bank.
    160. Virginie Coudert & Mathieu Gex, 2007. "Does Risk Aversion Drive Financial Crises? Testing the Predictive Power of Empirical Indicators," Working Papers 2007-02, CEPII research center.
    161. Kilian, Lutz & Vigfusson, Robert J., 2012. "Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries," CEPR Discussion Papers 8980, C.E.P.R. Discussion Papers.
    162. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    163. Rapach, David E. & Wohar, Mark E., 2006. "In-sample vs. out-of-sample tests of stock return predictability in the context of data mining," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 231-247, March.
    164. Kim, Myung Suk, 2018. "Impacts of supply and demand factors on declining oil prices," Energy, Elsevier, vol. 155(C), pages 1059-1065.
    165. Todd E. Clark & Michael W. McCracken, 2010. "Reality checks and nested forecast model comparisons," Working Papers 2010-032, Federal Reserve Bank of St. Louis.
    166. Lu, Fei & Ma, Feng, 2023. "Cross-sectional uncertainty and stock market volatility: New evidence," Finance Research Letters, Elsevier, vol. 57(C).
    167. Gary J. Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2019. "Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 275-292, Emerald Group Publishing Limited.
    168. Ryan Compton & Syeed Khan, 2010. "An examination of the stability of short-run Canadian stock predictability," Economics Bulletin, AccessEcon, vol. 30(2), pages 1293-1306.
    169. Panopoulou, Ekaterini, 2007. "Predictive financial models of the euro area: A new evaluation test," International Journal of Forecasting, Elsevier, vol. 23(4), pages 695-705.
    170. Li, Wei-Xuan & Chen, Clara Chia-Sheng & French, Joseph J., 2015. "Toward an early warning system of financial crises: What can index futures and options tell us?," The Quarterly Review of Economics and Finance, Elsevier, vol. 55(C), pages 87-99.
    171. Inna S. Lola & Anton Manukov, 2020. "Forecasting Employment In Small Businesses In Russia: The Relevance Of Business Tendency Surveys," HSE Working papers WP BRP 113/STI/2020, National Research University Higher School of Economics.
    172. Tatiana PÎŞCHINA & Romeo Fortuna, 2017. "Moldova’s Phenomenon: Can Foreign Investments Help Out of the Poverty Circle?," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 3, ejes_v3_i.
    173. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    174. Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," SSE/EFI Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
    175. Campbell, John & Thompson, Samuel P., 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Scholarly Articles 2622619, Harvard University Department of Economics.
    176. Sousa, Ricardo M. & Vivian, Andrew & Wohar, Mark E., 2016. "Predicting asset returns in the BRICS: The role of macroeconomic and fundamental predictors," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 122-143.
    177. 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.
    178. Lance Bachmeier & Qi Li & Dandan Liu, 2008. "Should Oil Prices Receive So Much Attention? An Evaluation Of The Predictive Power Of Oil Prices For The U.S. Economy," Economic Inquiry, Western Economic Association International, vol. 46(4), pages 528-539, October.
    179. Ansgar Belke & Wim Kösters & Martin Leschke & Thorsten Polleit, 2005. "Back to the rules," Diskussionspapiere aus dem Institut für Volkswirtschaftslehre der Universität Hohenheim 268/2005, Department of Economics, University of Hohenheim, Germany.
    180. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    181. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
    182. Yu, Deshui & Huang, Difang & Chen, Li, 2023. "Stock return predictability and cyclical movements in valuation ratios," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 36-53.
    183. Mr. Andreas Billmeier, 2004. "Ghostbusting: Which Output Gap Measure Really Matters?," IMF Working Papers 2004/146, International Monetary Fund.
    184. Rocha Armada, Manuel J. & Sousa, Ricardo M. & Wohar, Mark E., 2015. "Consumption growth, preference for smoothing, changes in expectations and risk premium," The Quarterly Review of Economics and Finance, Elsevier, vol. 56(C), pages 80-97.
    185. Schmeling, Maik & Schrimpf, Andreas, 2011. "Expected inflation, expected stock returns, and money illusion: What can we learn from survey expectations?," European Economic Review, Elsevier, vol. 55(5), pages 702-719, June.
    186. Jaime Casassus & Freddy Higuera, 2011. "Stock Return Predictability and Oil Prices," Documentos de Trabajo 406, Instituto de Economia. Pontificia Universidad Católica de Chile..
    187. Edel Barnes & Gael Hardie‐Brown, 2006. "The Diversification Puzzle: Revisiting the Value Impact of Diversification for UK Firms," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(9‐10), pages 1508-1534, November.
    188. Fang, Jiali & Jacobsen, Ben & Qin, Yafeng, 2014. "Predictability of the simple technical trading rules: An out-of-sample test," Review of Financial Economics, Elsevier, vol. 23(1), pages 30-45.
    189. Hollstein, Fabian & Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Wese Simen, Chardin, 2019. "International tail risk and World Fear," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 244-259.
    190. Gupta, Rangan & Modise, Mampho P., 2012. "South African stock return predictability in the context data mining: The role of financial variables and international stock returns," Economic Modelling, Elsevier, vol. 29(3), pages 908-916.
    191. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    192. Li, Dakai, 2024. "Forecasting stock market realized volatility: The role of investor attention to the price of petroleum products," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 115-122.
    193. Yung, Julieta, 2021. "Can interest rate factors explain exchange rate fluctuations?," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 34-56.
    194. Neil Kellard & John Nankervis & Fotis Papadimitriou, 2007. "Predicting the UK Equity Premium with Dividend Ratios: An Out-Of-Sample Recursive Residuals Graphical Approach," Money Macro and Finance (MMF) Research Group Conference 2006 129, Money Macro and Finance Research Group.
    195. 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.
    196. Yin, Libo & Feng, Jiabao & Liu, Li & Wang, Yudong, 2019. "It's not that important: The negligible effect of oil market uncertainty," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 62-84.
    197. Qing Xu & Xiao-Ming Li, 2009. "Estimation of dynamic asymmetric tail dependences: an empirical study on Asian developed futures markets," Applied Financial Economics, Taylor & Francis Journals, vol. 19(4), pages 273-290.
    198. Enilov, Martin & Mensi, Walid & Stankov, Petar, 2023. "Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic," Journal of Commodity Markets, Elsevier, vol. 29(C).
    199. Angelidis, Timotheos & Tessaromatis, Nikolaos, 2009. "Idiosyncratic risk matters! A regime switching approach," International Review of Economics & Finance, Elsevier, vol. 18(1), pages 132-141, January.
    200. Dladla, Pholile & Malikane, Christopher, 2019. "Stock return predictability: Evidence from a structural model," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 412-424.
    201. Funk, Christoph, 2018. "Forecasting the real price of oil - Time-variation and forecast combination," Energy Economics, Elsevier, vol. 76(C), pages 288-302.
    202. Huang, Yisu & Ma, Feng & Bouri, Elie & Huang, Dengshi, 2023. "A comprehensive investigation on the predictive power of economic policy uncertainty from non-U.S. countries for U.S. stock market returns," International Review of Financial Analysis, Elsevier, vol. 87(C).
    203. Jeffrey S. Racine & Christopher F. Parmeter, 2012. "Data-Driven Model Evaluation: A Test for Revealed Performance," Department of Economics Working Papers 2012-13, McMaster University.
    204. Zhu, Yinchu & Timmermann, Allan, 2022. "Conditional rotation between forecasting models," Journal of Econometrics, Elsevier, vol. 231(2), pages 329-347.
    205. Kellard, Neil M. & Nankervis, John C. & Papadimitriou, Fotios I., 2010. "Predicting the equity premium with dividend ratios: Reconciling the evidence," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 539-551, September.
    206. Lili Hao & Eric C.Y. Ng, 2011. "Predicting Canadian recessions using dynamic probit modelling approaches," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(4), pages 1297-1330, November.
    207. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.
    208. Nelson Mark & Kimberly Berg, 2013. "Third-Country Effects on the Exchange Rate," 2013 Meeting Papers 1050, Society for Economic Dynamics.
    209. Arcidiacono, Sally Giuseppe & Corrente, Salvatore & Greco, Salvatore, 2021. "Robust stochastic sorting with interacting criteria hierarchically structured," European Journal of Operational Research, Elsevier, vol. 292(2), pages 735-754.
    210. He, Mengxi & Wen, Danyan & Xing, Lu & Zhang, Yaojie, 2024. "Industry volatility concentration and the predictability of aggregate stock market volatility," International Review of Economics & Finance, Elsevier, vol. 95(C).
    211. José Afonso Faias & Tiago Castel-Branco, 2018. "Out-Of-Sample Stock Return Prediction Using Higher-Order Moments," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(06), pages 1-27, September.
    212. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
    213. Ruan, Xinfeng & Zhang, Jin E., 2018. "Risk-neutral moments in the crude oil market," Energy Economics, Elsevier, vol. 72(C), pages 583-600.
    214. Doyle, Matthew, 2006. "Empirical Phillips Curves in OECD Countries: Has There Been A Common Breakdown?," Staff General Research Papers Archive 12684, Iowa State University, Department of Economics.
    215. Hanan Naser & Fatema Alaali, 2018. "Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach," Empirical Economics, Springer, vol. 55(4), pages 1757-1777, December.
    216. Thomas Fullerton & Roberto Tinajero & Martha Barraza de Anda, 2006. "Short-Term Water Consumption Patterns in Ciudad Juárez, Mexico," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 34(4), pages 467-479, December.
    217. Timmermann, Allan & Zhu, Yinchu, 2021. "Conditional Rotation Between Forecasting Models," CEPR Discussion Papers 15917, C.E.P.R. Discussion Papers.
    218. Timmermann, Allan & Zhu, Yinchu, 2019. "Comparing Forecasting Performance with Panel Data," CEPR Discussion Papers 13746, C.E.P.R. Discussion Papers.
    219. Czudaj, Robert, 2011. "P-star in times of crisis - Forecasting inflation for the euro area," Economic Systems, Elsevier, vol. 35(3), pages 390-407, September.
    220. Tais Carestiato Da Silva & Helder Ferreira De Mendonça, 2011. "Setting The Interest Rate For Twooutlier Countries," Anais do XXXVIII Encontro Nacional de Economia [Proceedings of the 38th Brazilian Economics Meeting] 207, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    221. Lawrenz, Jochen & Zorn, Josef, 2017. "Predicting international stock returns with conditional price-to-fundamental ratios," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 159-184.
    222. Chatterjee, Ujjal K. & Zirgulis, Aras & Hüttinger, Maik & French, Joseph J., 2024. "Reassessing the inversion of the Treasury yield curve as a sign of U.S. recessions: Insights from the housing and credit markets," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).

  36. Atsushi Inoue & Mototsugu Shintani, 2001. "Bootstrapping GMM Estimators for Time Series," Vanderbilt University Department of Economics Working Papers 0129, Vanderbilt University Department of Economics, revised Aug 2003.

    Cited by:

    1. Seojeong Lee, 2014. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for GEL Estimators," Discussion Papers 2014-02, School of Economics, The University of New South Wales.
    2. Firmin Doko Tchatoka & Robert Garrard & Virginie Masson, 2017. "Testing for Stochastic Dominance in Social Networks," School of Economics and Public Policy Working Papers 2017-02, University of Adelaide, School of Economics and Public Policy.
    3. Rossini, Jacopo & Canale, Antonio, 2019. "Quantifying prediction uncertainty for functional-and-scalar to functional autoregressive models under shape constraints," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 221-231.
    4. Paulo Parente & Richard J. Smith, 2019. "Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," CeMMAP working papers CWP60/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Kilian, Lutz & Inoue, Atsushi, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers.
    6. Creel, Michael & Kristensen, Dennis, 2011. "Indirect Likelihood Inference," Dynare Working Papers 8, CEPREMAP.
    7. Haitham A. Al-Zoubi, 2024. "An affine model for short rates when monetary policy is path dependent," Review of Derivatives Research, Springer, vol. 27(2), pages 151-201, July.
    8. Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
    9. Javed Iqbal & Robert Brooks & Don U.A. Galagedera, 2008. "Multivariate tests of asset pricing: Simulation evidence from an emerging market," Monash Econometrics and Business Statistics Working Papers 2/08, Monash University, Department of Econometrics and Business Statistics.
    10. Timothy Conley & Silvia Gonçalves & Christian Hansen, 2018. "Inference with Dependent Data in Accounting and Finance Applications," Journal of Accounting Research, Wiley Blackwell, vol. 56(4), pages 1139-1203, September.
    11. Valentina Corradi & Norman R. Swanson, 2003. "Bootstrap Specification Tests for Diffusion Processes," Departmental Working Papers 200321, Rutgers University, Department of Economics.
    12. Wei-Ming Lee & Chung-Ming Kuan, 2006. "Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix," IEAS Working Paper : academic research 06-A009, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    13. Goncalves, Silvia & White, Halbert, 2004. "Maximum likelihood and the bootstrap for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 119(1), pages 199-219, March.
    14. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    15. Jason Allen & Allan Gregory & Katsumi Shimotsu, 2008. "Empirical Likelihood Block Bootstrapping," Staff Working Papers 08-18, Bank of Canada.
    16. Kilian, Lutz & Inoue, Atsushi, 2004. "Bagging Time Series Models," CEPR Discussion Papers 4333, C.E.P.R. Discussion Papers.
    17. Politis, D N, 2009. "Higher-Order Accurate, Positive Semi-definite Estimation of Large-Sample Covariance and Spectral Density Matrices," University of California at San Diego, Economics Working Paper Series qt66w826hz, Department of Economics, UC San Diego.
    18. Amilcar Velez, 2023. "The Local Projection Residual Bootstrap for AR(1) Models," Papers 2309.01889, arXiv.org, revised Mar 2025.
    19. Ghysels, Eric & Pereira, João Pedro, 2008. "Liquidity and conditional portfolio choice: A nonparametric investigation," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 679-699, September.
    20. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2020. "A higher-order correct fast moving-average bootstrap for dependent data," Working Papers unige:129395, University of Geneva, Geneva School of Economics and Management.
    21. Härdle, Wolfgang & Horowitz, Joel L. & Kreiss, Jens-Peter, 2001. "Bootstrap methods for time series," SFB 373 Discussion Papers 2001,59, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    22. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
    23. Valentina Corradi & Norman R. Swanson, 2003. "Evaluation of Dynamic Stochastic General Equilibrium Models Based on Distributional Comparison of Simulated and Historical Data," Departmental Working Papers 200320, Rutgers University, Department of Economics.
    24. Seojeong Lee, 2018. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Empirical Likelihood Estimators," Papers 1806.00953, arXiv.org, revised Jun 2018.
    25. Paulo M.D.C. Parente & Richard J. Smith, 2018. "Generalised Empirical Likelihood Kernel Block Bootstrapping," Working Papers REM 2018/55, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    26. Diana N. Weymark & Mototsugu Shintani, 2004. "Measuring Inflation Pressure and Monetary Policy Response: A General Approach Applied to US Data 1966 - 2001," Vanderbilt University Department of Economics Working Papers 0424, Vanderbilt University Department of Economics.
    27. Seojeong Lee, 2013. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators," Discussion Papers 2013-09, School of Economics, The University of New South Wales.
    28. Eric Ghysels & João Pereira, 2003. "On Portfolio Choice, Liquidity, and Short Selling: A Nonparametric Investigation," CIRANO Working Papers 2003s-27, CIRANO.
    29. Laurini, Márcio Poletti & Sanvicente, Antônio Zoratto & Monteiro, Rogério da Costa, 2011. "Generalized Tests of Investment Fund Performance," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(2), December.
    30. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    31. Hill, Jonathan B. & Prokhorov, Artem, 2016. "GEL estimation for heavy-tailed GARCH models with robust empirical likelihood inference," Journal of Econometrics, Elsevier, vol. 190(1), pages 18-45.
    32. Prosper Dovonon & Silvia Gonçalves, 2014. "Bootstrapping the GMM overidentification test Under first-order underidentification," CIRANO Working Papers 2014s-25, CIRANO.
    33. Zisimos Koustas & Jean-François Lamarche, 2012. "Instrumental variable estimation of a nonlinear Taylor rule," Empirical Economics, Springer, vol. 42(1), pages 1-20, February.
    34. Rachida Ouysse, 2014. "On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models," Computational Statistics, Springer, vol. 29(1), pages 233-261, February.
    35. Al-Zoubi, Haitham A., 2019. "Bond and option prices with permanent shocks," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 272-290.
    36. Yixiao Sun & Peter C.B. Phillips, 2008. "Optimal Bandwidth Choice for Interval Estimation in GMM Regression," Cowles Foundation Discussion Papers 1661, Cowles Foundation for Research in Economics, Yale University.
    37. Blaskowitz, Oliver J. & Herwartz, Helmut, 2008. "Testing directional forecast value in the presence of serial correlation," SFB 649 Discussion Papers 2008-073, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    38. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.
    39. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics.
    40. Diana N. Weymark & Mototsugu Shintani, 2006. "Quantifying Inflation Pressure and Monetary Policy Response in the United States," Levine's Bibliography 321307000000000321, UCLA Department of Economics.
    41. Turnbull, Christopher & Sun, Sizhong & Anwar, Sajid, 2016. "Trade liberalisation, inward FDI and productivity within Australia’s manufacturing sector," Economic Analysis and Policy, Elsevier, vol. 50(C), pages 41-51.
    42. Blaskowitz, Oliver & Herwartz, Helmut, 2014. "Testing the value of directional forecasts in the presence of serial correlation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 30-42.
    43. Liyuan Cui & Guanhao Feng & Yongmiao Hong, 2024. "Regularized Gmm For Time‐Varying Models With Applications To Asset Pricing," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(2), pages 851-883, May.

  37. Peter Christoffersen & Jinyong Hahn & Atsushi Inoue, 2001. "Testing and Comparing Value-at-Risk Measures," CIRANO Working Papers 2001s-03, CIRANO.

    Cited by:

    1. Pinto, Cristian F. & Acuña, Andres A., 2011. "Consistencia de la evaluación de desempeño de inversiones financieras: Pruebas de dominación estocástica versus índices media-varianza [Consistency in the evaluation of financial investment perform," MPRA Paper 31301, University Library of Munich, Germany.
    2. Wagner Piazza Gaglianone & Luiz Renato Lima & Oliver Linton & Daniel R. Smith, 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 150-160, January.
    3. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 290-318.
    4. David Feldman & Xin Xu, 2018. "Equilibrium-based volatility models of the market portfolio rate of return (peacock tails or stotting gazelles)," Annals of Operations Research, Springer, vol. 262(2), pages 493-518, March.
    5. David E. Allen & Michael McAleer & Abhay K. Singh, 2017. "Risk Measurement and Risk Modelling Using Applications of Vine Copulas," Sustainability, MDPI, vol. 9(10), pages 1-34, September.
    6. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    7. Vadim Marmer & Taisuke Otsu, 2009. "Optimal Comparison of Misspecified Moment Restriction Models under a Chosen Measure of Fit," Cowles Foundation Discussion Papers 1724, Cowles Foundation for Research in Economics, Yale University, revised Jul 2011.
    8. Peter Christoffersen & Denis Pelletier, 2003. "Backtesting Value-at-Risk: A Duration-Based Approach," CIRANO Working Papers 2003s-05, CIRANO.
    9. João Caldeira & Guilherme Moura & André Santos, 2015. "Measuring Risk in Fixed Income Portfolios using Yield Curve Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 65-82, June.
    10. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," NBER Working Papers 11188, National Bureau of Economic Research, Inc.
    11. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    12. Ozan Evkaya & İsmail Gür & Bükre Yıldırım Külekci & Gülden Poyraz, 2024. "Vine Copula Approach to Understand the Financial Dependence of the Istanbul Stock Exchange Index," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2935-2980, November.
    13. Giacomini, Raffaella & Komunjer, Ivana, 2002. "Evaluation and Combination of Conditional Quantile Forecasts," University of California at San Diego, Economics Working Paper Series qt4n99t4wz, Department of Economics, UC San Diego.
    14. Li, Leon, 2017. "Testing and comparing the performance of dynamic variance and correlation models in value-at-risk estimation," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 116-135.
    15. Kraft, Holger & Schmidt, Alexander, 2013. "Systemic risk in the financial sector: What can se learn from option markets?," SAFE Working Paper Series 25, Leibniz Institute for Financial Research SAFE.
    16. Nikola Radivojevic & Milena Cvjetkovic & Saša Stepanov, 2016. "The new hybrid value at risk approach based on the extreme value theory," Estudios de Economia, University of Chile, Department of Economics, vol. 43(1 Year 20), pages 29-52, June.
    17. Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.
    18. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    19. Kimera Naradh & Retius Chifurira & Knowledge Chinhamu, 2022. "Analysis of stock exchange risk and currency in South African Financial Markets using stable parameter estimation," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 11(1), pages 120-131, January.
    20. Kerkhof, F.L.J. & Melenberg, B., 2002. "Backtesting for Risk-Based Regulatory Capital," Other publications TiSEM 2363cf81-9720-41f2-913c-f, Tilburg University, School of Economics and Management.
    21. David E Allen & Mohammad.A. Ashraf & Michael McAleer & Robert J Powell & Abhay K Singh, 2013. "Financial Dependence Analysis: Applications of Vine Copulae," KIER Working Papers 843, Kyoto University, Institute of Economic Research.
    22. Wong, Woon K., 2010. "Backtesting value-at-risk based on tail losses," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 526-538, June.
    23. Jakub Micha'nk'ow & {L}ukasz Kwiatkowski & Janusz Morajda, 2023. "Combining Deep Learning and GARCH Models for Financial Volatility and Risk Forecasting," Papers 2310.01063, arXiv.org.
    24. Vijverberg, Chu-Ping C. & Vijverberg, Wim P.M. & Taşpınar, Süleyman, 2016. "Linking Tukey’s legacy to financial risk measurement," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 595-615.
    25. Kerkhof, F.L.J. & Melenberg, B. & Schumacher, J.M., 2003. "Testing Expected Shortfall Models for Derivative Positions," Other publications TiSEM 98c22c46-0588-477f-b532-4, Tilburg University, School of Economics and Management.
    26. L. Kourouma & Denis Dupré & G. Sanfilippo & O. Taramasco, 2011. "Extreme Value at Risk and Expected Shortfall during Financial Crisis," Post-Print halshs-00658495, HAL.
    27. Ngozi G. Emenogu & Monday Osagie Adenomon & Nwaze Obini Nweze, 2020. "On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    28. Cocca, Teodoro & Gabauer, David & Pomberger, Stefan, 2024. "Clean energy market connectedness and investment strategies: New evidence from DCC-GARCH R2 decomposed connectedness measures," Energy Economics, Elsevier, vol. 136(C).
    29. Hamidreza Arian & Hossein Poorvasei & Azin Sharifi & Shiva Zamani, 2020. "The Uncertain Shape of Grey Swans: Extreme Value Theory with Uncertain Threshold," Papers 2011.06693, arXiv.org.
    30. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
    31. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    32. DeRossi, G. & Harvey, A., 2006. "Time-Varying Quantiles," Cambridge Working Papers in Economics 0649, Faculty of Economics, University of Cambridge.
    33. Wessam Abouarghoub & Iris Biefang-Frisancho Mariscal, 2013. "Measuring the level of risk exposure in tanker shipping freight markets," Working Papers 20131313, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    34. Fabozzi Frank J. & Stoyanov Stoyan V. & Rachev Svetlozar T., 2013. "Computational aspects of portfolio risk estimation in volatile markets: a survey," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 103-120, February.
    35. Tomáš Jeøábek, 2020. "The Efficiency of GARCH Models in Realizing Value at Risk Estimates," ACTA VSFS, University of Finance and Administration, vol. 14(1), pages 32-50.
    36. Escanciano, J. C. & Olmo, J., 2007. "Estimation risk effects on backtesting for parametric value-at-risk models," Working Papers 07/11, Department of Economics, City University London.
    37. Carol Alexander & Jose Maria Sarabia, 2010. "Endogenizing Model Risk to Quantile Estimates," ICMA Centre Discussion Papers in Finance icma-dp2010-07, Henley Business School, University of Reading.
    38. Metiu, N., 2011. "Financial contagion in developed sovereign bond markets," Research Memorandum 004, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    39. Santos, André A. P. & Nogales, Francisco J., 2009. "Comparing univariate and multivariate models to forecast portfolio value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws097222, Universidad Carlos III de Madrid. Departamento de Estadística.
    40. Wessam M. T. Abouarghoub & Iris Biefang-Frisancho Mariscal, 2011. "Measuring level of risk exposure in tanker Shipping freight markets," International Journal of Business and Social Research, LAR Center Press, vol. 1(1), pages 20-44, December.
    41. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," CIRJE F-Series CIRJE-F-430, CIRJE, Faculty of Economics, University of Tokyo.
    42. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
    43. Wagner Piazza Gaglianone & Jaqueline Terra Moura Marins, 2014. "Risk Assessment of the Brazilian FX Rate," Working Papers Series 344, Central Bank of Brazil, Research Department.
    44. Lehar, Alfred & Scheicher, Martin & Schittenkopf, Christian, 2002. "GARCH vs. stochastic volatility: Option pricing and risk management," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 323-345, March.
    45. Siva Kiran GUPTHA. K & Prabhakar RAO. R, 2019. "GARCH based VaR estimation: An empirical evidence from BRICS stock markets," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(621), W), pages 201-218, Winter.
    46. Georgios Fatouros & Georgios Makridis & Dimitrios Kotios & John Soldatos & Michael Filippakis & Dimosthenis Kyriazis, 2023. "DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks," Digital Finance, Springer, vol. 5(1), pages 29-56, March.
    47. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    48. Krzysztof Echaust & Małgorzata Just, 2021. "Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic," Energies, MDPI, vol. 14(14), pages 1-21, July.
    49. Schmidt, Ulrich, 2003. "The axiomatic basis of risk-value models," European Journal of Operational Research, Elsevier, vol. 145(1), pages 216-220, February.
    50. Xiao, Zhijie, 2009. "Quantile cointegrating regression," Journal of Econometrics, Elsevier, vol. 150(2), pages 248-260, June.
    51. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    52. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    53. Hasna Fadhila & Nora Amelda Rizal, 2013. "Analysis of Risk using Value at Risk (VaR) After Crisis in 2008 Study in Stocks of Bank Mandiri, Bank BRI and Bank BNI in 2009-2011," Information Management and Business Review, AMH International, vol. 5(8), pages 394-400.
    54. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    55. Roberta Fiori & Simonetta Iannotti, 2006. "Scenario Based Principal Component Value-at-Risk: an Application to Italian Banks' Interest Rate Risk Exposure," Temi di discussione (Economic working papers) 602, Bank of Italy, Economic Research and International Relations Area.
    56. Metiu, Norbert, 2012. "Sovereign risk contagion in the Eurozone," Economics Letters, Elsevier, vol. 117(1), pages 35-38.
    57. Köksal, Bülent & Orhan, Mehmet, 2012. "Market risk of developed and developing countries during the global financial crisis," MPRA Paper 37523, University Library of Munich, Germany.
    58. Mirjana Miletić & Siniša Miletić, 2016. "Performance of VaR in Developed and CEE Countries during the Global Financial Crisis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 54-75, March.
    59. Kilic, Ekrem, 2006. "Violation duration as a better way of VaR model evaluation : evidence from Turkish market portfolio," MPRA Paper 5610, University Library of Munich, Germany.
    60. Chen, Xiaohong & Hong, Han & Shum, Matthew, 2007. "Nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models," Journal of Econometrics, Elsevier, vol. 141(1), pages 109-140, November.
    61. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    62. Isengildina-Massa, Olga & Sharp, Julia L., 2013. "Interval Forecast Comparison," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150791, Agricultural and Applied Economics Association.
    63. Kerkhof, Jeroen & Melenberg, Bertrand, 2004. "Backtesting for risk-based regulatory capital," Journal of Banking & Finance, Elsevier, vol. 28(8), pages 1845-1865, August.
    64. Chesney, Marc & Reshetar, Ganna & Karaman, Mustafa, 2011. "The impact of terrorism on financial markets: An empirical study," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 253-267, February.
    65. Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," JRFM, MDPI, vol. 10(4), pages 1-15, October.
    66. Sueishi, Naoya, 2013. "Identification problem of the exponential tilting estimator under misspecification," Economics Letters, Elsevier, vol. 118(3), pages 509-511.
    67. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2019. "Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns," Journal of Econometrics, Elsevier, vol. 213(2), pages 493-515.
    68. Minglian Lin & Indranil SenGupta & William Wilson, 2023. "Estimation of VaR with jump process: application in corn and soybean markets," Papers 2311.00832, arXiv.org, revised Jun 2024.
    69. Cerović Julija & Lipovina-Božović Milena & Vujošević Saša, 2015. "A Comparative Analysis of Value at Risk Measurement on Emerging Stock Markets: Case of Montenegro," Business Systems Research, Sciendo, vol. 6(1), pages 36-55, March.

  38. Francis X. Diebold & Atsushi Inoue, 2000. "Long Memory and Regime Switching," NBER Technical Working Papers 0264, National Bureau of Economic Research, Inc.

    Cited by:

    1. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
    2. Guglielmo Caporale & Luis Gil-Alana, 2014. "Fractional integration and cointegration in US financial time series data," Empirical Economics, Springer, vol. 47(4), pages 1389-1410, December.
    3. Rea, William & Oxley, Les & Reale, Marco & Brown, Jennifer, 2013. "Not all estimators are born equal: The empirical properties of some estimators of long memory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 29-42.
    4. Gil-Alana, Luis Alberiko & Dettoni, Robinson & Costamagna, Rodrigo & Valenzuela, Mario, 2019. "Rational bubbles in the real housing stock market: Empirical evidence from Santiago de Chile," Research in International Business and Finance, Elsevier, vol. 49(C), pages 269-281.
    5. Sibbertsen, Philipp & Leschinski, Christian & Holzhausen, Marie, 2015. "A Multivariate Test Against Spurious Long Memory," Hannover Economic Papers (HEP) dp-547, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    6. Gil-Alana, Luis A. & Aye, Goodness C. & Gupta, Rangan, 2015. "Trends and cycles in historical gold and silver prices," Journal of International Money and Finance, Elsevier, vol. 58(C), pages 98-109.
    7. 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.
    8. Carlos P. Barros & Guglielmo Maria Caporale & Luis A. Gil-Alana, 2012. "Long Memory in German Energy Price Indices," Discussion Papers of DIW Berlin 1186, DIW Berlin, German Institute for Economic Research.
    9. Dittmann, Ingolf & Granger, Clive W. J., 2002. "Properties of nonlinear transformations of fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 110(2), pages 113-133, October.
    10. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," JRFM, MDPI, vol. 7(2), pages 1-30, June.
    11. 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.
    12. Kapetanios, George, 2006. "Nonlinear autoregressive models and long memory," Economics Letters, Elsevier, vol. 91(3), pages 360-368, June.
    13. L.A. Gil-Alana, 2005. "Fractional Cyclical Structures & Business Cycles in the Specification of the US Real Output," European Research Studies Journal, European Research Studies Journal, vol. 0(1-2), pages 99-126.
    14. 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.
    15. Marinho Bertanha & Marcelo J. Moreira, 2016. "Impossible Inference in Econometrics: Theory and Applications," Papers 1612.02024, arXiv.org, revised Feb 2020.
    16. David McMillan & Mark Wohar, 2011. "Structural breaks in volatility: the case of UK sector returns," Applied Financial Economics, Taylor & Francis Journals, vol. 21(15), pages 1079-1093.
    17. Christophe Andr頍 & Luis A. Gil-Alana & Rangan Gupta, 2014. "Testing for persistence in housing price-to-income and price-to-rent ratios in 16 OECD countries," Applied Economics, Taylor & Francis Journals, vol. 46(18), pages 2127-2138, June.
    18. Cliff L. F. Attfield & Jonathan R. W. Temple, 2006. "Balanced growth and the great ratios: new evidence for the US and UK," Centre for Growth and Business Cycle Research Discussion Paper Series 75, Economics, The University of Manchester.
    19. Fillol, Jerome, 2007. "Estimating long memory: Scaling function vs Andrews and Guggenberger GPH," Economics Letters, Elsevier, vol. 95(2), pages 309-314, May.
    20. Guglielmo Maria Caporale & Luis A. Gil-Alana & Carlos Poza, 2020. "Inflation in the G7 Countries: Persistence and Structural Breaks," CESifo Working Paper Series 8349, CESifo.
    21. Chen, Ying & Härdle, Wolfgang Karl & Pigorsch, Uta, 2010. "Localized Realized Volatility Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1376-1393.
    22. Chen, Xiaohong & Hansen, Lars Peter & Carrasco, Marine, 2008. "Nonlinearity and Temporal Dependence," Working Papers 48, Yale University, Department of Economics.
    23. J. Cuñado & L. Gil-Alana & F. Gracia, 2009. "US stock market volatility persistence: evidence before and after the burst of the IT bubble," Review of Quantitative Finance and Accounting, Springer, vol. 33(3), pages 233-252, October.
    24. Abi Morshed, Alaa & Andreou, E. & Boldea, Otilia, 2016. "Structural Break Tests Robust to Regression Misspecification," Discussion Paper 2016-019, Tilburg University, Center for Economic Research.
    25. Lu, Yang K. & Perron, Pierre, 2010. "Modeling and forecasting stock return volatility using a random level shift model," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 138-156, January.
    26. Andrea Monticini & Francesco Ravazzolo, 2014. "Forecasting the intraday market price of money," DISCE - Working Papers del Dipartimento di Economia e Finanza def010, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    27. Gabriel Rodríguez & Dennis Alvaro & Ángel Guillén, 2016. "Modelling the Volatility of Commodities Prices using a Stochastic Volatility Model with Random Level Shifts," Documentos de Trabajo / Working Papers 2016-414, Departamento de Economía - Pontificia Universidad Católica del Perú.
    28. Gao, Guangyuan & Ho, Kin-Yip & Shi, Yanlin, 2020. "Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    29. Chin Wen Cheong, 2010. "Estimating the Hurst parameter in financial time series via heuristic approaches," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 201-214.
    30. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 290-318.
    31. Gürtler, Marc & Rauh, Ronald, 2012. "Challenging traditional risk models by a non-stationary approach with nonparametric heteroscedasticity," Working Papers IF41V1, Technische Universität Braunschweig, Institute of Finance.
    32. 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.
    33. Morten Ø. Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Comparison Of Parametric, Semiparametric, And Wavelet Estimators Of Fractional Integration," Working Paper 1189, Economics Department, Queen's University.
    34. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    35. Haldrup, Niels & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2010. "A vector autoregressive model for electricity prices subject to long memory and regime switching," Energy Economics, Elsevier, vol. 32(5), pages 1044-1058, September.
    36. 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.
    37. Smith, Aaron D., 2004. "Level Shifts and the Illusion of Long Memory in Economic Time Series," Working Papers 11974, University of California, Davis, Department of Agricultural and Resource Economics.
    38. Gil-Alana, L.A., 2006. "Fractional integration in daily stock market indexes," Review of Financial Economics, Elsevier, vol. 15(1), pages 28-48.
    39. Luis Gil-Alana & Rolando Peláez, 2008. "The persistence of earnings per share," Review of Quantitative Finance and Accounting, Springer, vol. 31(4), pages 425-439, November.
    40. Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "U.S. shale oil production and WTI prices behaviour," Energy, Elsevier, vol. 141(C), pages 12-19.
    41. Patrick Minford & David Peel, 2007. "On the Equality of Real Interest Rates Across Borders in Integrated Capital Markets," Open Economies Review, Springer, vol. 18(1), pages 119-125, February.
    42. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    43. John Dawson & John Seater, 2013. "Federal regulation and aggregate economic growth," Journal of Economic Growth, Springer, vol. 18(2), pages 137-177, June.
    44. Carlos Barros & Luis Gil-Alana, 2012. "Inflation forecasting in Angola: a fractional approach," CEsA Working Papers 103, CEsA - Centre for African and Development Studies.
    45. 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.
    46. Luis Gil-Alana, 2008. "Real GDP growth rates across countries: long memory and mean shifts," Applied Economics Letters, Taylor & Francis Journals, vol. 15(6), pages 449-455.
    47. Gil-Alana, Luis A., 2002. "A mean shift break in the US interest rate," Economics Letters, Elsevier, vol. 77(3), pages 357-363, November.
    48. N. Antonakakis & J. Darby, 2013. "Forecasting volatility in developing countries' nominal exchange returns," Applied Financial Economics, Taylor & Francis Journals, vol. 23(21), pages 1675-1691, November.
    49. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.
    50. Charfeddine, Lanouar & Guégan, Dominique, 2012. "Breaks or long memory behavior: An empirical investigation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5712-5726.
    51. Adebola, Solarin Sakiru & Gil-Alana, Luis A. & Madigu, Godfrey, 2019. "Gold prices and the cryptocurrencies: Evidence of convergence and cointegration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1227-1236.
    52. Shikta Sing & Supun Chandrasena & Yue Shi & Abdullah Alhussain & Claude DIEBOLT & Martin Enilov & Tapas Mishra, 2024. "A Learning Model with Memory in the Financial Markets," Working Papers 06-24, Association Française de Cliométrie (AFC).
    53. Kuswanto, Heri & Sibbertsen, Philipp, 2009. "Testing for Long Memory Against ESTAR Nonlinearities," Hannover Economic Papers (HEP) dp-427, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    54. Hyung, N. & Franses, Ph.H.B.F., 2001. "Structural breaks and long memory in US inflation rates: do they matter for forecasting?," Econometric Institute Research Papers EI 2001-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    55. Jensen, Mark J. & Liu, Ming, 2006. "Do long swings in the business cycle lead to strong persistence in output?," Journal of Monetary Economics, Elsevier, vol. 53(3), pages 597-611, April.
    56. 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.
    57. Sibbertsen, Philipp, 2001. "Long-memory versus structural breaks: An overview," Technical Reports 2001,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    58. Renzo Pardo Figueroa & Gabriel Rodríguez, 2014. "Distinguishing between True and Spurious Long Memory in the Volatility of Stock Market Returns in Latin America," Documentos de Trabajo / Working Papers 2014-395, Departamento de Economía - Pontificia Universidad Católica del Perú.
    59. 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.
    60. Luis A. Gil-Alana & Sakiru Adebola Solarin & Mehmet Balcilar & Rangan Gupta, 2023. "Productivity and GDP: international evidence of persistence and trends over 130 years of data," Empirical Economics, Springer, vol. 64(3), pages 1219-1246, March.
    61. Pierre Perron & Wendong Shi, 2020. "Temporal Aggregation and Long Memory for Asset Price Volatility," JRFM, MDPI, vol. 13(8), pages 1-18, August.
    62. Luisa Bisaglia & Margherita Gerolimetto, 2009. "Testing structural breaks versus long memory with the Box–Pierce statistics: a Monte Carlo study," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(4), pages 543-553, November.
    63. Chen, Shengming & Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "The Russia–Ukraine war and energy market volatility: A novel application of the volatility ratio in the context of natural gas," Resources Policy, Elsevier, vol. 85(PA).
    64. Gilles Dufrénot & Dominique Guegan & Anne Peguin-Feissolle, 2005. "Long-memory dynamics in a SETAR model - Applications to stock markets," Post-Print halshs-00179339, HAL.
    65. Fischer, Christian & Gil-Alana, Luis A., 2007. "The nature of the relationship between international tourism and international trade: the case of German imports of Spanish wine," Discussion Papers 57033, University of Bonn, Institute for Food and Resource Economics.
    66. Pierre Perron & Zhongjun Qu, 2008. "Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices," Boston University - Department of Economics - Working Papers Series wp2008-004, Boston University - Department of Economics.
    67. Chen, Runquan, 2009. "Regime switching in volatilities and correlation between stock and bond markets," LSE Research Online Documents on Economics 29306, London School of Economics and Political Science, LSE Library.
    68. Young Wook Han, 2010. "The Effects of US Macroeconomic Surprises on the Intraday Movements of Foreign Exchange Rates: Cases of USD-EUR and USD-JPY Exchange Rates," International Economic Journal, Taylor & Francis Journals, vol. 24(3), pages 375-396.
    69. Barros, Carlos Pestana & Gil-Alana, Luis A. & Payne, James E., 2011. "An analysis of oil production by OPEC countries: Persistence, breaks, and outliers," Energy Policy, Elsevier, vol. 39(1), pages 442-453, January.
    70. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    71. Heejoon Han & Shen Zhang, 2012. "Non‐stationary non‐parametric volatility model," Econometrics Journal, Royal Economic Society, vol. 15(2), pages 204-225, June.
    72. Haldrup, Niels & Nielsen, Morten Oe., "undated". "Estimation of Fractional Integration in the Presence of Data Noise," Economics Working Papers 2003-10, Department of Economics and Business Economics, Aarhus University.
    73. Valadkhani, Abbas, 2023. "Asymmetric downside risk across different sectors of the US equity market," Global Finance Journal, Elsevier, vol. 57(C).
    74. 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.
    75. Barros, Carlos Pestana & Gil-Alana, Luis A. & Payne, James E., 2012. "Comovements among U.S. state housing prices: Evidence from fractional cointegration," Economic Modelling, Elsevier, vol. 29(3), pages 936-942.
    76. Becker, Janis & Leschinski, Christian & Sibbertsen, Philipp, 2019. "Robust Multivariate Local Whittle Estimation and Spurious Fractional Cointegration," Hannover Economic Papers (HEP) dp-660, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    77. 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.
    78. Maria Caporale, Guglielmo & A. Gil-Alana, Luis, 2011. "Multi-Factor Gegenbauer Processes and European Inflation Rates," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 26, pages 386-409.
    79. Dark Jonathan Graeme, 2010. "Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-50, March.
    80. Gil-Alana, Luis Alberiko & Poza, Carlos, 2024. "Volatility persistence in metal prices," Resources Policy, Elsevier, vol. 88(C).
    81. Chaim, Pedro & Laurini, Márcio P., 2018. "Volatility and return jumps in bitcoin," Economics Letters, Elsevier, vol. 173(C), pages 158-163.
    82. Tobias Hartl & Rolf Tschernig & Enzo Weber, 2020. "Fractional trends and cycles in macroeconomic time series," Papers 2005.05266, arXiv.org, revised May 2020.
    83. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    84. Gil-Alana, Luis A. & Moreno, Antonio, 2012. "Uncovering the US term premium: An alternative route," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1181-1193.
    85. Torben G. Andersen & Rasmus T. Varneskov, 2021. "Testing for Parameter Instability and Structural Change in Persistent Predictive Regressions," NBER Working Papers 28570, National Bureau of Economic Research, Inc.
    86. 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).
    87. Claudio Morana & Fabio Cesare Bagliano, 2007. "Inflation and monetary dynamics in the USA: a quantity-theory approach," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 229-244.
    88. Mstislav Elagin, 2008. "Locally adaptive estimation methods with application to univariate time series," Papers 0812.0449, arXiv.org.
    89. Mccloskey, Adam & Perron, Pierre, 2013. "Memory Parameter Estimation In The Presence Of Level Shifts And Deterministic Trends," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1196-1237, December.
    90. Luis Alberiko Gil-Alaña & Olanrewaju L. Shittu & OlaOluwa S. Yaya, 2011. "Long memory, strcutural breaks and mean shifts in the inflation rates in Nigeria," NCID Working Papers 04/2011, Navarra Center for International Development, University of Navarra.
    91. A. M. M. Shahiduzzaman Quoreshi & Reaz Uddin & Viroj Jienwatcharamongkhol, 2019. "Equity Market Contagion in Return Volatility during Euro Zone and Global Financial Crises: Evidence from FIMACH Model," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    92. Belkhouja, Mustapha & Mootamri, Imene, 2016. "Long memory and structural change in the G7 inflation dynamics," Economic Modelling, Elsevier, vol. 54(C), pages 450-462.
    93. Tommaso Proietti, 2014. "Exponential Smoothing, Long Memory and Volatility Prediction," CEIS Research Paper 319, Tor Vergata University, CEIS, revised 30 Jul 2014.
    94. Walter Kraemer, 2008. "Long Memory with Markov-Switching GARCH," CESifo Working Paper Series 2225, CESifo.
    95. Luis A. Gil-Alana & Juncal Cunado & Rangan Gupta, 2015. "Persistence, Mean-Reversion and Non-Linearities in Infant Mortality Rates," Working Papers 201574, University of Pretoria, Department of Economics.
    96. Claudio Morana & Andrea Beltratti, 2006. "Structural breaks and common factors in the volatility of the Fama-French factor portfolios," Applied Financial Economics, Taylor & Francis Journals, vol. 16(14), pages 1059-1073.
    97. Mohamed Boutahar & Gilles Dufrénot & Anne Péguin-Feissolle, 2008. "A Simple Fractionally Integrated Model with a Time-varying Long Memory Parameter d t," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 225-241, April.
    98. Joseph P. Byrne & Roger Perman, 2006. "Unit Roots and Structural Breaks: A Survey of the Literature," Working Papers 2006_10, Business School - Economics, University of Glasgow.
    99. João Ricardo Faria & Juan Carlos Cuestas & Luis Gil-Alana & Estefania Mourelle, 2021. "Self-employment by gender in the EU: convergence and clusters," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(3), pages 717-741, August.
    100. Gürtler, Marc & Rauh, Ronald, 2009. "Shortcomings of a parametric VaR approach and nonparametric improvements based on a non-stationary return series model," Working Papers IF32V2, Technische Universität Braunschweig, Institute of Finance.
    101. Pal, Debdatta, 2022. "Does hospitality industry stock volatility react asymmetrically to health and economic crises?," Economic Modelling, Elsevier, vol. 108(C).
    102. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    103. 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.
    104. Daniela Osterrieder, 2013. "Interest Rates with Long Memory: A Generalized Affine Term-Structure Model," CREATES Research Papers 2013-17, Department of Economics and Business Economics, Aarhus University.
    105. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
    106. Claudio Morana & Giacomo Sbrana, 2017. "Temperature Anomalies, Radiative Forcing and ENSO," Working Papers 2017.09, Fondazione Eni Enrico Mattei.
    107. Juncal Cuñado & Luis A. Gil-Alaña, 2007. "Tourism in the Canary Islands: Forecasting Using Several Seasonal Time Series Models," Faculty Working Papers 02/07, School of Economics and Business Administration, University of Navarra.
    108. Guillaume Chevillon & Alain Hecq & Sébastien Laurent, 2018. "Generating Univariate Fractional Integration within a Large VAR(1)," AMSE Working Papers 1844, Aix-Marseille School of Economics, France.
    109. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2008. "Modelling the US, UK and Japanese unemployment rates: Fractional integration and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4998-5013, July.
    110. Bent Jesper Christensen & Nabanita Datta Gupta & Paolo Santucci de Magistris, 2021. "Measuring the impact of clean energy production on CO2 abatement in Denmark: Upper bound estimation and forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 118-149, January.
    111. Luis A. Gil-Alana, 2009. "Warming break trends and fractional integration in the northern, southern and global temperature anomaly series," Faculty Working Papers 09/09, School of Economics and Business Administration, University of Navarra.
    112. 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.
    113. Jiawen Xu & Pierre Perron, 2013. "Forecasting Return Volatility: Level Shifts with Varying Jump Probability and Mean Reversion," Boston University - Department of Economics - Working Papers Series 2013-021, Boston University - Department of Economics.
    114. Mohamed El Hedi Arouri & Duc Khuong Nguyen & Amine Lahiani, 2010. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Working Papers hal-00507831, HAL.
    115. Massimiliano Caporin & Rangan Gupta, 2014. "Time-Varying Persistence in US Inflation," Working Papers 201457, University of Pretoria, Department of Economics.
    116. 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.
    117. Abanto-Valle, Carlos A. & Rodríguez, Gabriel & Garrafa-Aragón, Hernán B., 2021. "Stochastic Volatility in Mean: Empirical evidence from Latin-American stock markets using Hamiltonian Monte Carlo and Riemann Manifold HMC methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 272-286.
    118. Chen, Ying & Han, Qian & Niu, Linlin, 2018. "Forecasting the Term Structure of Option Implied Volatility: The Power of an Adaptive Method," IRTG 1792 Discussion Papers 2018-046, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    119. Gbaguidi, David Sedo, 2011. "Expectations Impact on the Effectiveness of the Inflation-Real Activity Trade-Off," MPRA Paper 35482, University Library of Munich, Germany.
    120. Shaher Al-Gounmeein Remal & Ismail Mohd Tahir, 2021. "Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach," Statistics in Transition New Series, Statistics Poland, vol. 22(1), pages 29-54, March.
    121. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    122. Luis A. Gil-Alana, 2015. "Linear and segmented trends in sea surface temperature data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1531-1546, July.
    123. Kreye, Tom Jannik & Sibbertsen, Philipp, 2024. "Testing for a Forecast Accuracy Breakdown under Long Memory," Hannover Economic Papers (HEP) dp-729, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    124. Simone Alfarano & Thomas Lux, 2007. "A Minimal Noise Trader Model with Realistic Time Series Properties," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 345-361, Springer.
    125. Berna Kirkulak Uludag & Zorikto Lkhamazhapov, 2014. "Long memory and structural breaks in the returns and volatility of gold: evidence from Turkey," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3777-3787, November.
    126. Bos, Charles S. & Koopman, Siem Jan & Ooms, Marius, 2014. "Long memory with stochastic variance model: A recursive analysis for US inflation," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 144-157.
    127. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    128. Gil-Alana, Luis A., 2002. "Structural breaks and fractional integration in the US output and unemployment rate," Economics Letters, Elsevier, vol. 77(1), pages 79-84, September.
    129. Baillie, Richard T. & Morana, Claudio, 2009. "Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1577-1592, August.
    130. Sibbertsen, Philipp & Willert, Juliane, 2012. "Estimating the number of mean shifts under long memory," Hannover Economic Papers (HEP) dp-496, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    131. Lean, Hooi Hooi & Smyth, Russell, 2009. "Long memory in US disaggregated petroleum consumption: Evidence from univariate and multivariate LM tests for fractional integration," Energy Policy, Elsevier, vol. 37(8), pages 3205-3211, August.
    132. Fabrizio Iacone & Peter M Robinson, 2004. "Cointegration in Fractional Systems with Deterministic Trends," STICERD - Econometrics Paper Series 476, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    133. Yaya O. S. & Akintande O. J. & Ogbonna A. E. & Adegoke H. M., 2019. "Cpi Inflation In Africa: Fractional Persistence, Mean Reversion And Nonlinearity," Statistics in Transition New Series, Statistics Poland, vol. 20(3), pages 119-132, September.
    134. Alessandro Casini, 2021. "Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models," Papers 2103.02981, arXiv.org, revised Aug 2024.
    135. Silvestro Di Sanzo, 2007. "Forecasting Time Series with Long Memory and Level Shifts, A Bayesian Approach," Working Papers 2007_03, Department of Economics, University of Venice "Ca' Foscari".
    136. Jerry Coakley & Jian Dollery & Neil Kellard, 2011. "Long memory and structural breaks in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(11), pages 1076-1113, November.
    137. Million, N., 2008. "Test simultan de la non-stationnarit et de la non-lin arit : une application au taux d.int r t r el am ricain," Working papers 201, Banque de France.
    138. Elena Andreou & Eric Ghysels, 2001. "Detecting Multiple Breaks in Financial Market Volatility Dynamics," University of Cyprus Working Papers in Economics 0202, University of Cyprus Department of Economics.
    139. 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.
    140. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    141. Casini, Alessandro, 2023. "Theory of evolutionary spectra for heteroskedasticity and autocorrelation robust inference in possibly misspecified and nonstationary models," Journal of Econometrics, Elsevier, vol. 235(2), pages 372-392.
    142. Gil-Alana, Luis A. & Shittu, Olanrewaju I. & Yaya, OlaOluwa S., 2014. "On the persistence and volatility in European, American and Asian stocks bull and bear markets," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 149-162.
    143. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2013. "A Markov-switching multifractal inter-trade duration model, with application to US equities," Journal of Econometrics, Elsevier, vol. 177(2), pages 320-342.
    144. Juan J. Dolado & Heiko Rachinger & Carlos Velasco, 2022. "LM Tests for Joint Breaks in the Dynamics and Level of a Long-Memory Time Series," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 629-650, April.
    145. Guglielmo Maria Caporale & Luis A. Gil-Alana & Francisco Puertolas, 2022. "Modelling Profitability of Private Equity: A Fractional Integration Approach," CESifo Working Paper Series 9843, CESifo.
    146. Dietmar Bauer & Alex Maynard, 2010. "Persistence-robust Granger causality testing," Working Papers 1011, University of Guelph, Department of Economics and Finance.
    147. Laurent-Emmanuel Calvet & Adlai J. Fisher & Samuel B. Thompson, 2006. "Volatility Comovement: a multifrequency approach," Post-Print hal-00459667, HAL.
    148. Khurshid M. Kiani, 2009. "Asymmetries in Macroeconomic Time Series in Eleven Asian Economies," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 8(1), pages 37-54, April.
    149. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Pigorsch, Uta, 2008. "Measuring and modeling risk using high-frequency data," SFB 649 Discussion Papers 2008-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    150. Dominique Guegan & Philippe de Peretti, 2011. "Tests of structural changes in conditional distributions with unknown changepoints," Post-Print halshs-00611932, HAL.
    151. Lux, Thomas & Kaizoji, Taisei, 2006. "Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching," Economics Working Papers 2006-13, Christian-Albrechts-University of Kiel, Department of Economics.
    152. Robinson Kruse & Philipp Sibbertsen, 2010. "Long memory and changing persistence," CREATES Research Papers 2010-42, Department of Economics and Business Economics, Aarhus University.
    153. Bond, Derek & Harrison, Michael J & Hession, Niall & O’Brien, Edward J., 2006. "Some Empirical Observations on the Forward Exchange Rate Anomaly," Research Technical Papers 3/RT/06, Central Bank of Ireland.
    154. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "A Simple Test on Structural Change in Long-Memory Time Series," Hannover Economic Papers (HEP) dp-592, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    155. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    156. Guglielmo Maria Caporale & Hector Carcel & Luis A. Gil-Alana, 2015. "Modelling African inflation rates: nonlinear deterministic terms and long-range dependence," Applied Economics Letters, Taylor & Francis Journals, vol. 22(5), pages 421-424, March.
    157. Beylunioğlu Fuat C. & Stengos Thanasis & Yazgan M. Ege, 2018. "Regime switching with structural breaks in output convergence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(3), pages 1-17, June.
    158. John J. Seater & John W. Dawson, 2008. "The Macroeconomic Effects of Federal Regulation," 2008 Meeting Papers 1035, Society for Economic Dynamics.
    159. Härdle, Wolfgang Karl & López-Cabrera, Brenda & Ritter, Matthias, 2012. "Forecast based pricing of weather derivatives," SFB 649 Discussion Papers 2012-027, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    160. Gustavo Cabrera González, 2019. "Modeling and Projection of the Mexican Exchange Rate (Peso/Dollar): a Bayesian Approach for Model Selection," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(2), pages 203-219, Abril-Jun.
    161. Rehim Kilic, 2011. "A conditional variance tale from an emerging economy's freely floating exchange rate," Applied Economics, Taylor & Francis Journals, vol. 43(19), pages 2465-2480.
    162. Gil-Alana, Luis A. & Infante, Juan & Martín-Valmayor, Miguel Angel, 2023. "Persistence and long run co-movements across stock market prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 347-357.
    163. Chaker Aloui & Duc Khuong Nguyen, 2014. "On the detection of extreme movements and persistent behavior in Mediterranean stock markets: a wavelet-based approach," Working Papers 2014-184, Department of Research, Ipag Business School.
    164. Charfeddine, Lanouar, 2016. "Breaks or long range dependence in the energy futures volatility: Out-of-sample forecasting and VaR analysis," Economic Modelling, Elsevier, vol. 53(C), pages 354-374.
    165. Ye Li & Pierre Perron & Jiawen Xu, 2017. "Modelling exchange rate volatility with random level shifts," Applied Economics, Taylor & Francis Journals, vol. 49(26), pages 2579-2589, June.
    166. Surgailis, Donatas & Teyssière, Gilles & Vaiciulis, Marijus, 2008. "The increment ratio statistic," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 510-541, March.
    167. Chevillon, G. & Hecq, A.W. & Laurent, S.F.J.A., 2015. "Long memory through marginalization of large systems and hidden cross-section dependence," Research Memorandum 014, Maastricht University, Graduate School of Business and Economics (GSBE).
    168. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    169. Juncal Cunado & Luis Alberiko Gil-Alana & Fernando Perez de Gracia, 2008. "New Evidence on US Current Account Sustainability," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 7(1), pages 1-21, April.
    170. Yigit, Taner M., 2010. "Inflation targeting: An indirect approach to assess the direct impact," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1357-1368, November.
    171. McMillan, David G. & Wohar, Mark E., 2010. "Persistence and time-varying coefficients," Economics Letters, Elsevier, vol. 108(1), pages 85-88, July.
    172. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2015. "Testing for Level Shifts in Fractionally Integrated Processes: a State Space Approach," CREATES Research Papers 2015-30, Department of Economics and Business Economics, Aarhus University.
    173. Yanlin Shi & Lingbing Feng & Tong Fu, 2020. "Markov Regime-Switching in-Mean Model with Tempered Stable Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1275-1299, April.
    174. Davidson James E. H. & Peel David A & Byers J. David, 2006. "Support for Governments and Leaders: Fractional Cointegration Analysis of Poll Evidence from the UK, 1960-2004," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(1), pages 1-23, March.
    175. Dominique Guegan & Philippe de Peretti, 2011. "Tests of structural changes in conditional distributions with unknown changepoints," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00611932, HAL.
    176. Mulligan, Robert F., 2004. "Fractal analysis of highly volatile markets: an application to technology equities," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(1), pages 155-179, February.
    177. Altissimo, Filippo & Corradi, Valentina, 2003. "Strong rules for detecting the number of breaks in a time series," Journal of Econometrics, Elsevier, vol. 117(2), pages 207-244, December.
    178. Pascalau, Razvan & Thomann, Christian & Gregoriou, Greg N., 2010. "Unconditional mean, Volatility and the Fourier-Garch representation," MPRA Paper 35932, University Library of Munich, Germany.
    179. Goliński, Adam & Zaffaroni, Paolo, 2016. "Long memory affine term structure models," Journal of Econometrics, Elsevier, vol. 191(1), pages 33-56.
    180. Lima, Luiz Renato & Notini, Hilton Hostalácio & Reis Gomes, Fábio Augusto, 2010. "Empirical Evidence on Convergence Across Brazilian States," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 64(2), June.
    181. Dominique Guegan, 2007. "Global and local stationary modelling in finance: theory and empirical evidence," Post-Print halshs-00187875, HAL.
    182. 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.
    183. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    184. Gabriel Rodríguez, 2015. "Modeling Latin-American Stock Markets Volatility: Varying Probabilities and Mean Reversion in a Random Level Shifts Model," Documentos de Trabajo / Working Papers 2015-403, Departamento de Economía - Pontificia Universidad Católica del Perú.
    185. Veiga, Helena, 2006. "Are feedback factors important in modelling financial data?," DES - Working Papers. Statistics and Econometrics. WS ws060101, Universidad Carlos III de Madrid. Departamento de Estadística.
    186. Manmohan S. Kumar & Tatsuyoshi Okimoto, 2007. "Dynamics of Persistence in International Inflation Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(6), pages 1457-1479, September.
    187. Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "Crude oil price behaviour before and after military conflicts and geopolitical events," Energy, Elsevier, vol. 120(C), pages 79-91.
    188. Hou, Jie & Perron, Pierre, 2014. "Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations," Journal of Econometrics, Elsevier, vol. 182(2), pages 309-328.
    189. Luis A. Gil-Alana & Fernando Perez de Gracia & Rangan Gupta, 2015. "Modeling Persistence of Carbon Emission Allowance Prices," Working Papers 201515, University of Pretoria, Department of Economics.
    190. Dominique Guegan, 2007. "La persistance dans les marchés financiers," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00179269, HAL.
    191. John Geweke & Gianni Amisano, 2007. "Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns," Working Papers 0705, University of Brescia, Department of Economics.
    192. 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.
    193. Dominique Guegan, 2005. "How can we Define the Concept of Long Memory? An Econometric Survey," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 113-149.
    194. Ivelina Pavlova & Jang Hyung Cho & A.M. Parhizgari & William G. Hardin, 2014. "Long memory in REIT volatility and changes in the unconditional mean: a modified FIGARCH approach," Journal of Property Research, Taylor & Francis Journals, vol. 31(4), pages 315-332, December.
    195. Guglielmo Maria Caporale & Juncal Cunado & Luis A. Gil-Alana & Rangan Gupta, 2015. "The Relationship between Healthcare Expenditure and Disposable Personal Income in the US States: A Fractional Integration and Cointegration Analysis," Discussion Papers of DIW Berlin 1486, DIW Berlin, German Institute for Economic Research.
    196. Bertram, Philip & Sibbertsen, Philipp & Stahl, Gerhard, 2011. "About the Impact of Model Risk on Capital Reserves: A Quantitative Analysis," Hannover Economic Papers (HEP) dp-469, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    197. Kim, Moosup & Lee, Taewook & Noh, Jungsik & Baek, Changryong, 2014. "Quasi-maximum likelihood estimation for multiple volatility shifts," Statistics & Probability Letters, Elsevier, vol. 86(C), pages 50-60.
    198. 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).
    199. Luis Alberiko Gil-Alana & Antonio Moreno & Seonghoon Cho, 2011. "The Deaton paradox in a long memory context with structural breaks," Post-Print hal-00711450, HAL.
    200. 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.
    201. Guglielmo Maria Caporale & Silvia García Tapia & Luis Alberiko Gil-Alana, 2024. "Persistence in Tax Revenues: Evidence from Some OECD Countries," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(2), pages 475-491, June.
    202. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," Harvard Institute of Economic Research Working Papers 1999, Harvard - Institute of Economic Research.
    203. Fabrizio Iacone & Stephen J. Leybourne & A. M. Robert Taylor, 2014. "A FIXED- b TEST FOR A BREAK IN LEVEL AT AN UNKNOWN TIME UNDER FRACTIONAL INTEGRATION," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 40-54, January.
    204. Charfeddine, Lanouar & Ajmi, Ahdi Noomen, 2013. "The Tunisian stock market index volatility: Long memory vs. switching regime," Emerging Markets Review, Elsevier, vol. 16(C), pages 170-182.
    205. Soosung Hwang & Steve E. Satchell & Pedro L. Valls Pereira, 2004. "How Persistent is Volatility? An Answer with Stochastic Volatility Models with Markov Regime Switching State Equations," Econometric Society 2004 Latin American Meetings 198, Econometric Society.
    206. Katarzyna Lasak & Carlos Velasco, 2014. "Fractional Cointegration Rank Estimation," Tinbergen Institute Discussion Papers 14-021/III, Tinbergen Institute.
    207. Hwang, Eunju & Shin, Dong Wan, 2013. "A CUSUM test for a long memory heterogeneous autoregressive model," Economics Letters, Elsevier, vol. 121(3), pages 379-383.
    208. Sabiou Inoua, 2016. "The Random Walk behind Volatility Clustering," Papers 1612.09344, arXiv.org.
    209. David E. Allen & Michael McAleer & Marcel Scharth, 2013. "Realized Volatility Risk," Tinbergen Institute Discussion Papers 13-092/III, Tinbergen Institute.
    210. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2022. "Long-memory and volatility spillovers across petroleum futures," Energy, Elsevier, vol. 243(C).
    211. Mulligan, Robert F. & Lombardo, Gary A., 2004. "Maritime businesses: volatile stock prices and market valuation inefficiencies," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(2), pages 321-336, May.
    212. Lutz, Benjamin Johannes & Pigorsch, Uta & Rotfuß, Waldemar, 2013. "Nonlinearity in cap-and-trade systems: The EUA price and its fundamentals," ZEW Discussion Papers 13-001, ZEW - Leibniz Centre for European Economic Research.
    213. Emmanuel Joel Aikins Abakah & Guglielmo Maria Caporale & Luis A. Gil-Alana, 2020. "Economic Policy Uncertainty: Persistence and Cross-Country Linkages," CESifo Working Paper Series 8289, CESifo.
    214. Kyongwook Choi & Eric Zivot, 2003. "Long Memory and Structural Changes in the Forward Discount: An Empirical Investigation," EERI Research Paper Series EERI_RP_2003_02, Economics and Econometrics Research Institute (EERI), Brussels.
    215. Aikins Abakah, Emmanuel Joel & Gil-Alana, Luis A. & Tripathy, Trilochan, 2022. "Stochastic structure of metal prices: Evidence from fractional integration non-linearities and breaks," Resources Policy, Elsevier, vol. 78(C).
    216. Philippe Charlot & Vêlayoudom Marimoutou, 2014. "On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree," Working Papers hal-00980125, HAL.
    217. Ferrara, L. & Marcellino, M. & Mogliani, M., 2012. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," Working papers 383, Banque de France.
    218. Philip Hans Franses & Namwon Hyung, 2005. "Forecasting time series with long memory and level shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 1-16.
    219. Wang, Cindy Shin-Huei & Bauwens, Luc & Hsiao, Cheng, 2013. "Forecasting a long memory process subject to structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 171-184.
    220. Susanne M. Schennach, 2013. "Long memory via networking," CeMMAP working papers CWP13/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    221. Ying Chen & Linlin Niu, 2013. "Adaptive Dynamic Nelson-Siegel Term Structure Model with Applications," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    222. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    223. Abi Morshed, Alaa & Andreou, E. & Boldea, Otilia, 2016. "Structural Break Tests Robust to Regression Misspecification," Other publications TiSEM 3b21f21c-2cef-49d7-bb9b-a, Tilburg University, School of Economics and Management.
    224. Koubaa, Yosra & Slim, Skander, 2019. "The relationship between trading activity and stock market volatility: Does the volume threshold matter?," Economic Modelling, Elsevier, vol. 82(C), pages 168-184.
    225. Katsumi Shimotsu, 2006. "Simple (but Effective) Tests Of Long Memory Versus Structural Breaks," Working Paper 1101, Economics Department, Queen's University.
    226. Adam Goliński & João Madeira & Dooruj Rambaccussing, 2015. "Fractional Integration of the Price-Dividend Ratio in a Present-Value Model of Stock Prices," Dundee Discussion Papers in Economics 284, Economic Studies, University of Dundee.
    227. Luigi Spezia & Andy Vinten & Roberta Paroli & Marc Stutter, 2021. "An evolutionary Monte Carlo method for the analysis of turbidity high‐frequency time series through Markov switching autoregressive models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    228. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & Poza, Carlos, 2020. "Persistence, non-linearities and structural breaks in European stock market indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 50-61.
    229. Marcel Scharth & Marcelo Cunha Medeiros, 2006. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," Textos para discussão 532, Department of Economics PUC-Rio (Brazil).
    230. Cizek, P., 2010. "Modelling Conditional Heteroscedasticity in Nonstationary Series," Discussion Paper 2010-84, Tilburg University, Center for Economic Research.
    231. Hadhri, Sinda & Ftiti, Zied, 2019. "Commonality in liquidity among Middle East and North Africa emerging stock markets: Does it really matter?," Economic Systems, Elsevier, vol. 43(3).
    232. Benjamin M. Tabak, 2007. "Estimating the Fractional Order of Integration of Yields in the Brazilian Fixed Income Market," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 36(3), pages 231-246, November.
    233. Juan Carlos Cuestas & Luis A. Gil-Alana & Karl Taylor, 2012. "Inflation Convergence in Central and Eastern Europe with a View to Adopting the Euro," Working Papers 2012005, The University of Sheffield, Department of Economics.
    234. Ralf Becker & Adam Clements, 2007. "Forecasting stock market volatility conditional on macroeconomic conditions," NCER Working Paper Series 18, National Centre for Econometric Research.
    235. Dominique Guegan & Philippe de Peretti, 2011. "An Omnibus Test to Detect Time-Heterogeneity in Time Series," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00560221, HAL.
    236. Marie Busch & Philipp Sibbertsen, 2018. "An Overview of Modified Semiparametric Memory Estimation Methods," Econometrics, MDPI, vol. 6(1), pages 1-21, March.
    237. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2007. "A Multivariate Long-Memory Model with Structural Breaks," CESifo Working Paper Series 1950, CESifo.
    238. Choi, Kyongwook & Zivot, Eric, 2007. "Long memory and structural changes in the forward discount: An empirical investigation," Journal of International Money and Finance, Elsevier, vol. 26(3), pages 342-363, April.
    239. Fabrizio Iacone, 2009. "A Semiparametric Analysis of the Term Structure of the US Interest Rates," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(4), pages 475-490, August.
    240. Yanlin Shi, 2023. "Long memory and regime switching in the stochastic volatility modelling," Annals of Operations Research, Springer, vol. 320(2), pages 999-1020, January.
    241. Kruse, Robinson, 2015. "A modified test against spurious long memory," Economics Letters, Elsevier, vol. 135(C), pages 34-38.
    242. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
    243. 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.
    244. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2012. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(2), pages 207-218.
    245. Kuswanto, Heri & Sibbertsen, Philipp, 2008. "A Study on "Spurious Long Memory in Nonlinear Time Series Models"," Hannover Economic Papers (HEP) dp-410, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    246. Han, Heejoon & Park, Joon Y., 2006. "Time series properties of ARCH processes with persistent covariates," MPRA Paper 5199, University Library of Munich, Germany.
    247. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & Poza, Carlos, 2020. "High and low prices and the range in the European stock markets: A long-memory approach," Research in International Business and Finance, Elsevier, vol. 52(C).
    248. Jérôme Coulon & Yannick Malevergne, 2010. "Heterogeneous expectations and long range correlation of the volatility of asset returns," Working Papers halshs-00541953, HAL.
    249. Mohamed Boutahar & Mustapha Belkhouja, 2007. "Le Changement Structurel Dans Un Environnement Mémoire Longue," Working Papers halshs-00352610, HAL.
    250. Phillips, Peter C.B. & Lee, Ji Hyung, 2016. "Robust econometric inference with mixed integrated and mildly explosive regressors," Journal of Econometrics, Elsevier, vol. 192(2), pages 433-450.
    251. Caporale, Guglielmo Maria & Gil-Alana, Luis A. & You, Kefei, 2016. "Exhange rate linkages between the Asean currencies, the US dollar and the Chinese RMB," Bank of Finland Research Discussion Papers 20/2016, Bank of Finland.
    252. Nikseresht, Ali & Amindavar, Hamidreza, 2024. "Energy demand forecasting using adaptive ARFIMA based on a novel dynamic structural break detection framework," Applied Energy, Elsevier, vol. 353(PA).
    253. Mark J. Jensen, 2006. "The long-run Fisher effect: can it be tested?," FRB Atlanta Working Paper 2006-11, Federal Reserve Bank of Atlanta.
    254. Juan Carlos Cuestas & Luis A. Gil-Alana, 2012. "A Non-Linear Approach with Long Range Dependence Based on Chebyshev Polynomials," Working Papers 2012013, The University of Sheffield, Department of Economics.
    255. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2009. "Long Memory in US Real Output per Capita," Discussion Papers of DIW Berlin 891, DIW Berlin, German Institute for Economic Research.
    256. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    257. Golinski, Adam & Madeira, Joao & Rambaccussing, Dooruj, 2014. "Fractional Integration of the Price-Dividend Ratio in a Present-Value Model," MPRA Paper 58554, University Library of Munich, Germany.
    258. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute.
    259. Martin J. Lenardon & Anna Amirdjanova, 2006. "Interaction between stock indices via changepoint analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 22(5‐6), pages 573-586, September.
    260. Derek Bond & Kenneth A. Dyson, 2008. "Long memory and nonlinearity in stock markets," Applied Financial Economics Letters, Taylor & Francis Journals, vol. 4(1), pages 45-48.
    261. Alfarano, Simone & Lux, Thomas, 2007. "A Noise Trader Model As A Generator Of Apparent Financial Power Laws And Long Memory," Macroeconomic Dynamics, Cambridge University Press, vol. 11(S1), pages 80-101, November.
    262. Cho, Dooyeon & Chun, Sungju, 2019. "Can structural changes in the persistence of the forward premium explain the forward premium anomaly?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 58(C), pages 225-235.
    263. Arnaud Dufays & Maciej Augustyniak & Luc Bauwens, 2016. "A new approach to volatility modeling: the High-Dimensional Markov model," Cahiers de recherche 1609, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    264. Elena Andreou & Andros Kourtellos, 2018. "Scoring rules for simple forecasting models: The case of Cyprus GDP and its sectors," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(1), pages 59-73, June.
    265. Georgios KOURETAS & Mark E. WOHAR, 2010. "The Dynamics of Inflation: A Study of a Large Number of Countries," EcoMod2010 259600096, EcoMod.
    266. 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.
    267. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2020. "Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis," JRFM, MDPI, vol. 13(10), pages 1-18, October.
    268. Zhang, Yue-Jun & Zhang, Han, 2023. "Volatility forecasting of crude oil futures market: Which structural change-based HAR models have better performance?," International Review of Financial Analysis, Elsevier, vol. 85(C).
    269. Juan J. Dolado & Jesús Gonzalo & Laura Mayoral, 2005. "What is what?: A simple time-domain test of long-memory vs. structural breaks," Economics Working Papers 954, Department of Economics and Business, Universitat Pompeu Fabra.
    270. Luis Alberiko Gil-Alana, 2024. "All Road User Casualties (Killed) in Great Britain from 1926. Linear and Nonlinear Trends with Persistent Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(3), pages 631-640, September.
    271. Robinson Kruse & Christoph Wegener, 2019. "Explosive behaviour and long memory with an application to European bond yield spreads," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 139-153, February.
    272. Simeon Coleman & Vitor Leone, 2015. "An investigation of regime shifts in UK commercial property returns: a time series analysis," Applied Economics, Taylor & Francis Journals, vol. 47(60), pages 6479-6492, December.
    273. Pestana Barros, Carlos & Gil-Alana, Luis A. & Payne, James E., 2012. "Evidence of long memory behavior in U.S. renewable energy consumption," Energy Policy, Elsevier, vol. 41(C), pages 822-826.
    274. Ying Chen & Bo Li & Linlin Niu, 2013. "A Local Vector Autoregressive Framework and its Applications to Multivariate Time Series Monitoring and Forecasting," Working Papers 2013-12-05, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    275. 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.
    276. Charfeddine, Lanouar, 2014. "True or spurious long memory in volatility: Further evidence on the energy futures markets," Energy Policy, Elsevier, vol. 71(C), pages 76-93.
    277. Beltratti, A. & Morana, C., 2006. "Breaks and persistency: macroeconomic causes of stock market volatility," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 151-177.
    278. Cătălin Stărică & Clive Granger, 2005. "Nonstationarities in Stock Returns," The Review of Economics and Statistics, MIT Press, vol. 87(3), pages 503-522, August.
    279. Christophe Andre & Mehmet Balcilar & Tsangyao Chang & Luis Alberiko Gil-Alana & Rangan Gupta, 2018. "Current account sustainability in G7 and BRICS: Evidence from a long-memory model with structural breaks," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 27(6), pages 638-654, August.
    280. Barros, Carlos Pestana & Gil-Alana, Luis A. & Payne, James E., 2013. "U.S. Disaggregated renewable energy consumption: Persistence and long memory behavior," Energy Economics, Elsevier, vol. 40(C), pages 425-432.
    281. Alessandro Casini & Taosong Deng & Pierre Perron, 2021. "Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference," Papers 2103.01604, arXiv.org, revised Sep 2024.
    282. Härdle, Wolfgang Karl & Mungo, Julius, 2007. "Long memory persistence in the factor of Implied volatility dynamics," SFB 649 Discussion Papers 2007-027, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    283. Shi, Yanlin & Ho, Kin-Yip, 2015. "Modeling high-frequency volatility with three-state FIGARCH models," Economic Modelling, Elsevier, vol. 51(C), pages 473-483.
    284. Carlos A. Abanto-Valle & Gabriel Rodríguez & Hernán B. Garrafa-Aragón, 2020. "Stochastic Volatility in Mean: Empirical Evidence from Stock Latin American Markets," Documentos de Trabajo / Working Papers 2020-481, Departamento de Economía - Pontificia Universidad Católica del Perú.
    285. de Figueiredo, Erik Alencar, 2010. "Dynamics of regional unemployment rates in Brazil: Fractional behavior, structural breaks, and Markov switching," Economic Modelling, Elsevier, vol. 27(5), pages 900-908, September.
    286. Per Frederiksen & Frank S. Nielsen, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-59, Department of Economics and Business Economics, Aarhus University.
    287. Les Oxley & Chris Price & William Rea & Marco Reale, 2008. "A New Procedure to Test for H Self-Similarity," Working Papers in Economics 08/16, University of Canterbury, Department of Economics and Finance.
    288. Stoyan V. Stoyanov & Yong Shin Kim & Svetlozar T. Rachev & Frank J. Fabozzi, 2017. "Option pricing for Informed Traders," Papers 1711.09445, arXiv.org.
    289. Kellard, Neil & Sarantis, Nicholas, 2008. "Can exchange rate volatility explain persistence in the forward premium?," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 714-728, September.
    290. Fleming, Jeff & Kirby, Chris, 2011. "Long memory in volatility and trading volume," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1714-1726, July.
    291. Yaya, OlaOluwa A & Gil-Alana, Luis A., 2018. "Modelling Long Range Dependence and Non-linearity in the Infant Mortality Rates of Africa Countries," MPRA Paper 88752, University Library of Munich, Germany.
    292. OlaOluwa S. Yaya & Luis A. Gil-Alana & Acheampong Y. Amoateng, 2019. "Under-5 Mortality Rates in G7 Countries: Analysis of Fractional Persistence, Structural Breaks and Nonlinear Time Trends," European Journal of Population, Springer;European Association for Population Studies, vol. 35(4), pages 675-694, October.
    293. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    294. Ioanna-Yvonni Tsaknaki & Fabrizio Lillo & Piero Mazzarisi, 2023. "Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods," Papers 2307.02375, arXiv.org, revised May 2024.
    295. Haldrup, Niels & Nielsen, Morten Orregaard, 2006. "A regime switching long memory model for electricity prices," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 349-376.
    296. Gil-Alana, Luis Alberiko & Abakah, Emmanuel Joel Aikins & Rojo, María Fátima Romero, 2020. "Cryptocurrencies and stock market indices. Are they related?," Research in International Business and Finance, Elsevier, vol. 51(C).
    297. Gündüz, Yalin & Kaya, Orcun, 2013. "Sovereign default swap market efficiency and country risk in the eurozone," Discussion Papers 08/2013, Deutsche Bundesbank.
    298. Slim Chaouachi & Zied Ftiti & Frederic Teulon, 2014. "Explaining the Tunisian Real Exchange: Long Memory versus Structural Breaks," Working Papers 2014-147, Department of Research, Ipag Business School.
    299. Zied Ftiti & Slim Chaouachi, 2018. "What Can We Learn About the Real Exchange Rate Behavior in the Case of a Peripheral Country?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(3), pages 681-707, September.
    300. Marinko Skare & Luis A. Gil-Alana & Gloria Claudio-Quiroga & Romina Pržiklas Družeta, 2021. "Income inequality in China 1952–2017: persistence and main determinants," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 863-888, December.
    301. Guglielmo D’Amico & Ada Lika & Filippo Petroni, 2019. "Change point dynamics for financial data: an indexed Markov chain approach," Annals of Finance, Springer, vol. 15(2), pages 247-266, June.
    302. Ulrich K. Müller & Mark W. Watson, 2008. "Testing Models of Low-Frequency Variability," Econometrica, Econometric Society, vol. 76(5), pages 979-1016, September.
    303. Carlos Pestana Barros & Luis A. Gil-Alana, 2011. "Oil Prices: Persistence and Breaks," Faculty Working Papers 09/11, School of Economics and Business Administration, University of Navarra.
    304. Gary Biglaiser & Ching-to Albert Ma, 2006. "Moonlighting: Public Service and Private Practice," Boston University - Department of Economics - Working Papers Series WP2006-015, Boston University - Department of Economics.
    305. Solarin, Sakiru Adebola & Gil-Alana, Luis A. & Lafuente, Carmen, 2020. "An investigation of long range reliance on shale oil and shale gas production in the U.S. market," Energy, Elsevier, vol. 195(C).
    306. Chevillon, Guillaume & Mavroeidis, Sophocles, 2011. "Learning generates Long Memory," ESSEC Working Papers WP1113, ESSEC Research Center, ESSEC Business School.
    307. 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.
    308. Gil-Alana, Luis A. & Cunado, Juncal & de Gracia, Fernando Perez, 2013. "Salient features of dependence in daily US stock market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3198-3212.
    309. Luis Alberiko Gil-Alaña & Juan C. Cuestas & Estefania Mourelle, 2011. "Is there asymmetric behaviour in African inflation? A non-linear approach," NCID Working Papers 03/2011, Navarra Center for International Development, University of Navarra.
    310. Vygintas Gontis, 2023. "Discrete $q$-exponential limit order cancellation time distribution," Papers 2306.00093, arXiv.org, revised Oct 2023.
    311. 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.
    312. Harry-Paul Vander Elst, 2015. "FloGARCH: Realizing Long Memory and Asymmetries in Returns Valitility," Working Papers ECARES ECARES 2015-12, ULB -- Universite Libre de Bruxelles.
    313. Seong Yeon Chang & Pierre Perron, 2017. "Fractional Unit Root Tests Allowing for a Structural Change in Trend under Both the Null and Alternative Hypotheses," Econometrics, MDPI, vol. 5(1), pages 1-26, January.
    314. Adnen Ben Nasr & Ahdi N. Ajmi & Rangan Gupta, 2013. "Modeling the Volatility of the Dow Jones Islamic Market World Index Using a Fractionally Integrated Time Varying GARCH (FITVGARCH) Model," Working Papers 201357, University of Pretoria, Department of Economics.
    315. Diebold, F.X. & Kilian, L. & Nerlove, Marc, 2006. "Time Series Analysis," Working Papers 28556, University of Maryland, Department of Agricultural and Resource Economics.
    316. Pierre Perron & Wendong Shi, 2014. "Temporal Aggregation, Bandwidth Selection and Long Memory for Volatility Models," Boston University - Department of Economics - Working Papers Series wp2014-009, Boston University - Department of Economics.
    317. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2007. "Long Run and Cyclical Dynamics in the US Stock Market," CESifo Working Paper Series 2046, CESifo.
    318. 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.
    319. Di Sanzo, Silvestro, 2018. "A Markov switching long memory model of crude oil price return volatility," Energy Economics, Elsevier, vol. 74(C), pages 351-359.
    320. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Trinity Economics Papers tep20021, Trinity College Dublin, Department of Economics.
    321. Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
    322. Muğaloğlu, Erhan & Kuşkaya, Sevda & Aldieri, Luigi & Alnour, Mohammed & Hoque, Mohammad Enamul & Magazzino, Cosimo & Bilgili, Faik, 2023. "Dynamic regime differences in the market behavior of primary natural resources in response to geopolitical risk and economic policy uncertainty," Resources Policy, Elsevier, vol. 87(PB).
    323. Yoon, Gawon, 2005. "Long-memory property of nonlinear transformations of break processes," Economics Letters, Elsevier, vol. 87(3), pages 373-377, June.
    324. Pierre Perron & Zhongjun Qu, 2007. "An Analytical Evaluation of the Log-periodogram Estimate in the Presence of Level Shifts," Boston University - Department of Economics - Working Papers Series wp2007-044, Boston University - Department of Economics.
    325. Marco R Barassi & Dayong Zhang, 2009. "Fractional Integration and Cointegration: Testing the Term Structure of Interest Rates," Discussion Papers 09-17, Department of Economics, University of Birmingham.
    326. Nicolas Million, 2010. "Test simultané de la non-stationnarité et de la non-linéarité : une application au taux d’intérêt réel américain," Économie et Prévision, Programme National Persée, vol. 192(1), pages 83-95.
    327. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.
    328. Leipus, Remigijus & Viano, Marie-Claude, 2003. "Long memory and stochastic trend," Statistics & Probability Letters, Elsevier, vol. 61(2), pages 177-190, January.
    329. Gilles Dufrénot & William Ginn & Marc Pourroy, 2021. "The Effect of ENSO Shocks on Commodity Prices: A Multi-Time Scale Approach," AMSE Working Papers 2130, Aix-Marseille School of Economics, France.
    330. Carlos Pestana Barros & Luis Gil-Alana, 2006. "Eta: A Persistent Phenomenon," Defence and Peace Economics, Taylor & Francis Journals, vol. 17(2), pages 95-116.
    331. Sakiru Adebola Solarin & Luis A. Gil-Alana & Maria Jesus Gonzalez-Blanch, 2021. "Fractional persistence in income poverty in Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 563-581, June.
    332. 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.
    333. Abderrazak Ben Maatoug & Rim Lamouchi & Russell Davidson & Ibrahim Fatnassi, 2018. "Modelling Foreign Exchange Realized Volatility Using High Frequency Data: Long Memory versus Structural Breaks," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(1), pages 1-25, March.
    334. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
    335. Do, Hung Xuan & Brooks, Robert & Treepongkaruna, Sirimon & Wu, Eliza, 2014. "The effects of sovereign rating drifts on financial return distributions: Evidence from the European Union," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 5-20.
    336. Junior Ojeda & Gabriel Rodriguez, 2014. "An Application of a Random Level Shifts Model to the Volatility of Peruvian Stock and Exchange Rates Returns," Documentos de Trabajo / Working Papers 2014-383, Departamento de Economía - Pontificia Universidad Católica del Perú.
    337. Okou, Cédric & Jacquier, Éric, 2016. "Horizon effect in the term structure of long-run risk-return trade-offs," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 445-466.
    338. Xiao-Ming Li, 2014. "Rethinking Long Memory and Structural Breaks in the Forward Premium," Scottish Journal of Political Economy, Scottish Economic Society, vol. 61(4), pages 455-485, September.
    339. Davidson, James & Sibbertsen, Philipp, 2002. "Generating schemes for long memory processes: Regimes, aggregation and linearity," Technical Reports 2002,46, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    340. Charfeddine, Lanouar & Khediri, Karim Ben & Mrabet, Zouhair, 2019. "The forward premium anomaly in the energy futures markets: A time-varying approach," Research in International Business and Finance, Elsevier, vol. 47(C), pages 600-615.
    341. 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.
    342. Rodríguez, Gabriel & Tramontana, Roxana, 2015. "An Application of a Short Memory Model with Random Level Shifts to the Volatility of Latin American Stock Market Returns," Working Papers 2015-004, Banco Central de Reserva del Perú.
    343. Frank S. Nielsen, 2008. "Local polynomial Whittle estimation covering non-stationary fractional processes," CREATES Research Papers 2008-28, Department of Economics and Business Economics, Aarhus University.
    344. 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).
    345. Haldrup, Niels & Vera Valdés, J. Eduardo, 2017. "Long memory, fractional integration, and cross-sectional aggregation," Journal of Econometrics, Elsevier, vol. 199(1), pages 1-11.
    346. Ryan S. Mattson & Philippe de Peretti, 2014. "Investigating the Role of Real Divisia Money in Persistence-Robust Econometric Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00984827, HAL.
    347. Yoon, Gawon, 2009. "Is high real interest rate persistence an intrinsic characteristic of industrialized economies?," Economic Modelling, Elsevier, vol. 26(2), pages 359-363, March.
    348. Gilles Dufrénot & Valérie Mignon & Théo Naccache, 2012. "Testing Catching-Up Between The Developing Countries: “Growth Resistance” And Sometimes “Growth Tragedy”," Bulletin of Economic Research, Wiley Blackwell, vol. 64(4), pages 470-508, October.
    349. Taewook Lee & Moosup Kim & Changryong Baek, 2015. "Tests for Volatility Shifts in Garch Against Long-Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 127-153, March.
    350. Guglielmo Caporale & Luis Gil-Alana, 2009. "Multiple shifts and fractional integration in the US and UK unemployment rates," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 33(4), pages 364-375, October.
    351. Luis Alberiko & OlaOluwa S. Yaya & Olarenwaju I. Shittu, 2015. "Fractional integration and asymmetric volatility in european, asian and american bull and bear markets. Applications to high frequency stock data," NCID Working Papers 07/2015, Navarra Center for International Development, University of Navarra.
    352. Chevillon, Guillaume & Mavroeidis, Sophocles, 2017. "Learning can generate long memory," Journal of Econometrics, Elsevier, vol. 198(1), pages 1-9.
    353. A. C. -L. Chian & E. L. Rempel & C. Rogers, 2007. "Crisis-induced intermittency in non-linear economic cycles," Applied Economics Letters, Taylor & Francis Journals, vol. 14(3), pages 211-218.
    354. Le, Van & Zurbruegg, Ralf, 2010. "The role of trading volume in volatility forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(5), pages 533-555, December.
    355. Walther, Thomas & Klein, Tony & Thu, Hien Pham & Piontek, Krzysztof, 2017. "True or spurious long memory in European non-EMU currencies," Research in International Business and Finance, Elsevier, vol. 40(C), pages 217-230.
    356. Zhicheng Li & Haipeng Xing & Xinyun Chen, 2019. "A multifactor regime-switching model for inter-trade durations in the limit order market," Papers 1912.00764, arXiv.org.
    357. Yanlin Shi & Yang Yang, 2018. "Modeling High Frequency Data with Long Memory and Structural Change: A-HYEGARCH Model," Risks, MDPI, vol. 6(2), pages 1-28, March.
    358. Luis Gil-Alana & Antonio Moreno, 2012. "Fractional integration and structural breaks in U.S. macro dynamics," Empirical Economics, Springer, vol. 43(1), pages 427-446, August.
    359. 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.
    360. Bauer, Dietmar & Maynard, Alex, 2012. "Persistence-robust surplus-lag Granger causality testing," Journal of Econometrics, Elsevier, vol. 169(2), pages 293-300.
    361. 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.
    362. Samet Günay & Yanlin Shi, 2016. "Long-Memory in Volatilities of CDS Spreads: Evidences from the Emerging Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 122-137, March.
    363. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2018. "Persistence and Cyclical Dynamics of US and UK House Prices: Evidence from Over 150 Years of Data," Working Papers 201838, University of Pretoria, Department of Economics.
    364. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
    365. Mwasi Paza Mboya & Philipp Sibbertsen, 2023. "Optimal forecasts in the presence of discrete structural breaks under long memory," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1889-1908, November.
    366. Li, Zhicheng & Chen, Xinyun & Xing, Haipeng, 2023. "A multifactor regime-switching model for inter-trade durations in the high-frequency limit order market," Economic Modelling, Elsevier, vol. 118(C).
    367. Manuel Monge & Luis A. Gil-Alana, 2020. "The Lithium Industry and Analysis of the Beta Term Structure of Oil Companies," Risks, MDPI, vol. 8(4), pages 1-17, December.
    368. Pierre Perron & Zhongjun Qu, 2006. "An Analytical Evaluation of the Log-periodogram Estimate in the Presence of Level Shifts and its Implications for Stock Returns Volatility," Boston University - Department of Economics - Working Papers Series WP2006-016, Boston University - Department of Economics.
    369. David G. McMillan, 2010. "Level‐shifts and non‐linearity in US financial ratios," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 9(2), pages 189-207, May.
    370. Beran, Jan, 2007. "On parameter estimation for locally stationary long-memory processes," CoFE Discussion Papers 07/13, University of Konstanz, Center of Finance and Econometrics (CoFE).
    371. Chen, Cathy Yi-Hsuan & Chiang, Thomas C. & Härdle, Wolfgang Karl, 2016. "Downside risk and stock returns: An empirical analysis of the long-run and short-run dynamics from the G-7 Countries," SFB 649 Discussion Papers 2016-001, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    372. Cizek, P., 2010. "Modelling Conditional Heteroscedasticity in Nonstationary Series," Other publications TiSEM a5a7b05f-5f1f-46ed-8ce8-5, Tilburg University, School of Economics and Management.
    373. Luis A. Gil-Alana & Rangan Gupta & Fernando Perez de Gracia, 2016. "Persistence, mean reversion and non-linearities in the US housing prices over 1830--2013," Applied Economics, Taylor & Francis Journals, vol. 48(34), pages 3244-3252, July.
    374. Willert, Juliane, 2010. "Mean Shift detection under long-range dependencies with ART," Hannover Economic Papers (HEP) dp-437, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    375. Ying Chen & Bo Li, 2011. "Forecasting Yield Curves in an Adaptive Framework," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 3(4), pages 237-259, December.
    376. Rasmus T. Varneskov & Pierre Perron, 2017. "Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns," Boston University - Department of Economics - Working Papers Series WP2017-006, Boston University - Department of Economics.
    377. Luis A. Gil-Alana, 2004. "Modelling the Japanese Exchange Rate in Terms of I(d) Statistical Models with Parametric and Semiparametric Techniques," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 3(2), pages 123-138, August.
    378. Davidson, James & Terasvirta, Timo, 2002. "Long memory and nonlinear time series," Journal of Econometrics, Elsevier, vol. 110(2), pages 105-112, October.
    379. Garvey, John & Gallagher, Liam A., 2013. "The economics of data: Using simple model-free volatility in a high-frequency world," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 370-379.
    380. Ghysels, Eric & Sohn, Bumjean, 2009. "Which power variation predicts volatility well?," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 686-700, September.
    381. Cizek, P. & Haerdle, W. & Spokoiny, V., 2007. "Adaptive Pointwise Estimation in Time-Inhomogeneous Time-Series Models," Discussion Paper 2007-35, Tilburg University, Center for Economic Research.
    382. Matei, Marius, 2011. "Non-Linear Volatility Modeling of Economic and Financial Time Series Using High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 116-141, June.
    383. Ben-zhang Yang & Xinjiang He & Nan-jing Huang, 2019. "Equilibrium price and optimal insider trading strategy under stochastic liquidity with long memory," Papers 1901.00345, arXiv.org, revised Jan 2019.
    384. Krämer, Walter & Tameze, Baudouin & Christou, Konstantinos, 2012. "On the origin of high persistence in GARCH-models," Economics Letters, Elsevier, vol. 114(1), pages 72-75.
    385. Kang, Sang Hoon & Yoon, Seong-Min, 2013. "Modeling and forecasting the volatility of petroleum futures prices," Energy Economics, Elsevier, vol. 36(C), pages 354-362.
    386. Barros, Carlos P. & Gil-Alana, Luis A. & Wanke, Peter, 2016. "Energy production in Brazil: Empirical facts based on persistence, seasonality and breaks," Energy Economics, Elsevier, vol. 54(C), pages 88-95.
    387. Azamo, Baudouin Tameze & Krämer, Walter, 2006. "Structural Change and long memory in the GARCH(1,1)-model," Technical Reports 2006,33, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    388. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2007. "Demand for Money: A Study in Testing Time Series for Long Memory and Nonlinearity," The Economic and Social Review, Economic and Social Studies, vol. 38(1), pages 1-24.
    389. Carina Gerstenberger, 2021. "Robust discrimination between long‐range dependence and a change in mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 34-62, January.
    390. C.S. Bos & S.J. Koopman & M. Ooms, 2007. "Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks," Tinbergen Institute Discussion Papers 07-099/4, Tinbergen Institute.
    391. Luis A. Gil‐Alana, 2008. "Fractional integration and structural breaks at unknown periods of time," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(1), pages 163-185, January.
    392. Gil-Alana, Luis A., 2004. "Modelling the U.S. interest rate in terms of I(d) statistical models," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(4), pages 475-486, September.
    393. Kai Wenger & Christian Leschinski & Philipp Sibbertsen, 2019. "Change-in-mean tests in long-memory time series: a review of recent developments," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(2), pages 237-256, June.
    394. Khurshid M. Kiani & Prasad V. Bidarkota, 2004. "On Business Cycle Asymmetries in G7 Countries," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(3), pages 333-351, July.
    395. Rangan Gupta & Christophe André & Luis Gil-Alana, 2015. "Comovement in Euro area housing prices: A fractional cointegration approach," Urban Studies, Urban Studies Journal Limited, vol. 52(16), pages 3123-3143, December.
    396. Laura Mayoral, 2006. "Is the Observed Persistence Spurious? A Test for Fractional Integration versus Short Memory and Structural Breaks," Working Papers 260, Barcelona School of Economics.
    397. Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "Amplitude-Duration-Persistence Trade-off Relationship for Long Term Bear Stock Markets," MPRA Paper 54177, University Library of Munich, Germany.
    398. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2014. "How do OPEC news and structural breaks impact returns and volatility in crude oil markets? Further evidence from a long memory process," Energy Economics, Elsevier, vol. 42(C), pages 343-354.
    399. Mateo Isoardi & Luis A. Gil-Alana, 2019. "Inflation in Argentina: Analysis of Persistence Using Fractional Integration," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 45(2), pages 204-223, April.
    400. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Kiel Working Papers 1426, Kiel Institute for the World Economy (IfW Kiel).
    401. Luis A. Gil-Alana & Jiang Liang, 2011. "The PPP hypothesis in the US/China relationship. Fractional integration, time variation and data frequency," Faculty Working Papers 13/11, School of Economics and Business Administration, University of Navarra.
    402. 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.
    403. Feng Lingbing & Shi Yanlin, 2020. "Markov regime-switching autoregressive model with tempered stable distribution: simulation evidence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(1), pages 1-27, February.
    404. Dark, Jonathan, 2024. "An adaptive long memory conditional correlation model," Journal of Empirical Finance, Elsevier, vol. 75(C).
    405. Abdul Aziz Karia & Imbarine Bujang & Ismail Ahmad, 2013. "Fractionally integrated ARMA for crude palm oil prices prediction: case of potentially overdifference," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2735-2748, December.
    406. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    407. Nazlioglu, Saban & Gupta, Rangan & Bouri, Elie, 2020. "Movements in international bond markets: The role of oil prices," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 47-58.
    408. Härdle, Wolfgang Karl & López Cabrera, Brenda & Okhrin, Ostap & Wang, Weining, 2010. "Localising temperature risk," SFB 649 Discussion Papers 2011-001, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    409. Leipus, Remigijus & Paulauskas, Vygantas & Surgailis, Donatas, 2005. "Renewal regime switching and stable limit laws," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 299-327.
    410. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2012. "Persistence and Cycles in the US Federal Funds Rate," CESifo Working Paper Series 4035, CESifo.
    411. 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.
    412. Baillie, Richard T. & Morana, Claudio, 2012. "Adaptive ARFIMA models with applications to inflation," Economic Modelling, Elsevier, vol. 29(6), pages 2451-2459.
    413. Seuk Wai Phoong & Seuk Yen Phoong & Shi Ling Khek, 2022. "Systematic Literature Review With Bibliometric Analysis on Markov Switching Model: Methods and Applications," SAGE Open, , vol. 12(2), pages 21582440221, April.
    414. Bob Nobay & Ivan Paya & David A. Peel, 2010. "Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 135-150, February.
    415. Kuswanto, Heri, 2009. "A New Simple Test Against Spurious Long Memory Using Temporal Aggregation," Hannover Economic Papers (HEP) dp-425, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    416. 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.
    417. Duran, Esra Akdeniz & Guo, Mengmeng & Härdle, Wolfgang Karl, 2010. "A confidence corridor for expectile functions," SFB 649 Discussion Papers 2011-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    418. Solarin, Sakiru Adebola & Gil-Alana, Luis A., 2021. "The persistence of economic policy uncertainty: Evidence of long range dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    419. Shi, Yanlin & Ho, Kin-Yip, 2021. "News sentiment and states of stock return volatility: Evidence from long memory and discrete choice models," Finance Research Letters, Elsevier, vol. 38(C).
    420. 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.
    421. 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.
    422. Sakoulis, Georgios & Zivot, Eric & Choi, Kyongwook, 2010. "Structural change in the forward discount: Implications for the forward rate unbiasedness hypothesis," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 957-966, December.
    423. Bent Jesper Christensen & Morten Ørregaard Nielsen, 2007. "The Effect of Long Memory in Volatility on Stock Market Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 684-700, November.
    424. Sanjeeta Shirodkar & Guntur Anjana Raju, 2021. "Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 230-239.
    425. Villanueva, O. Miguel, 2007. "Spot-forward cointegration, structural breaks and FX market unbiasedness," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 17(1), pages 58-78, February.
    426. Allen, David E. & McAleer, Michael & Scharth, Marcel, 2011. "Monte Carlo option pricing with asymmetric realized volatility dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1247-1256.
    427. D. Delpini & G. Bormetti, 2015. "Stochastic volatility with heterogeneous time scales," Quantitative Finance, Taylor & Francis Journals, vol. 15(10), pages 1597-1608, October.
    428. Rea, William & Reale, Marco & Brown, Jennifer & Oxley, Les, 2011. "Long memory or shifting means in geophysical time series?," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1441-1453.
    429. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2014. "Predicting BRICS stock returns using ARFIMA models," Applied Financial Economics, Taylor & Francis Journals, vol. 24(17), pages 1159-1166, September.
    430. Luis A. Gil-Alana & Goodness C. Aye & Rangan Gupta, 2012. "Testing for Persistence with Breaks and Outliers in South African House Prices," Faculty Working Papers 20/12, School of Economics and Business Administration, University of Navarra.
    431. Daniela Osterrieder & Daniel Ventosa-Santaulària & J. Eduardo Vera-Valdés, 2015. "Unbalanced Regressions and the Predictive Equation," CREATES Research Papers 2015-09, Department of Economics and Business Economics, Aarhus University.
    432. Granger, Clive W.J., 2005. "The past and future of empirical finance: some personal comments," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 35-40.
    433. Fulvio Corsi & Francesco Audrino, 2008. "Modeling Tick-by-Tick Realized Correlations," University of St. Gallen Department of Economics working paper series 2008 2008-05, Department of Economics, University of St. Gallen.
    434. Gürtler, Marc & Kreiss, Jens-Peter & Rauh, Ronald, 2009. "A non-stationary approach for financial returns with nonparametric heteroscedasticity," Working Papers IF31V2, Technische Universität Braunschweig, Institute of Finance.
    435. Gil-Alana, Luis Alberiko & Trani, Tommaso, 2019. "An examination of trade-weighted real exchange rates based on fractional integration," International Economics, Elsevier, vol. 158(C), pages 64-76.
    436. Abbritti, Mirko & Carcel, Hector & Gil-Alana, Luis & Moreno, Antonio, 2023. "Term premium in a fractionally cointegrated yield curve," Journal of Banking & Finance, Elsevier, vol. 149(C).
    437. Nima Nonejad, 2013. "Long Memory and Structural Breaks in Realized Volatility: An Irreversible Markov Switching Approach," CREATES Research Papers 2013-26, Department of Economics and Business Economics, Aarhus University.
    438. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    439. Luis A. Gil-Alana & Trilochan Tripathy, 2016. "Long Range Dependence in the Indian Stock Market: Evidence of Fractional Integration, Non-Linearities and Breaks," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 199-215, December.
    440. Elie Bouri & Luis A. Gil‐Alana & Rangan Gupta & David Roubaud, 2019. "Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 412-426, January.
    441. Bisaglia, Luisa & Gerolimetto, Margherita, 2008. "Forecasting long memory time series when occasional breaks occur," Economics Letters, Elsevier, vol. 98(3), pages 253-258, March.
    442. Cuestas Juan Carlos & Gil-Alana Luis Alberiko, 2016. "Testing for long memory in the presence of non-linear deterministic trends with Chebyshev polynomials," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 57-74, February.
    443. Gil-Alana, L.A., 2008. "Testing of seasonal integration and cointegration with fractionally integrated techniques: An application to the Danish labour demand," Economic Modelling, Elsevier, vol. 25(2), pages 326-339, March.
    444. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    445. 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.
    446. Guglielmo Maria Caporale & Luis A. Gil-Alana & Robert Mudida, 2012. "Testing the Marshall-Lerner Condition in Kenya," Discussion Papers of DIW Berlin 1247, DIW Berlin, German Institute for Economic Research.
    447. Dominique Guegan & Philippe de Peretti, 2012. "An Omnibus Test to Detect Time-Heterogeneity in Time Series," Working Papers halshs-00721327, HAL.
    448. 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.
    449. Tan, Zhengxun & Xiao, Binuo & Huang, Yilong & Zhou, Li, 2021. "Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    450. Aue, Alexander & Horváth, Lajos & Steinebach, Josef, 2007. "Rescaled range analysis in the presence of stochastic trend," Statistics & Probability Letters, Elsevier, vol. 77(12), pages 1165-1175, July.
    451. Zhongjun Qu & Pierre Perron, 2008. "A Stochastic Volatility Model with Random Level Shifts: Theory and Applications to S&P 500 and NASDAQ Return Indices," Boston University - Department of Economics - Working Papers Series wp2008-007, Boston University - Department of Economics.
    452. Andres Herrera & Gabriel Rodríguez, 2014. "Volatility of Stock Market and Exchange Rate Returns in Peru: Long Memory or Short Memory with Level Shifts?," Documentos de Trabajo / Working Papers 2014-393, Departamento de Economía - Pontificia Universidad Católica del Perú.
    453. Saâdaoui, Foued, 2023. "Skewed multifractal scaling of stock markets during the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    454. Hyung, N. & Franses, Ph.H.B.F., 2002. "Inflation rates; long-memoray, level shifts, or both?," Econometric Institute Research Papers EI 2002-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    455. Goliński, Adam & Spencer, Peter, 2017. "The advantages of using excess returns to model the term structure," Journal of Financial Economics, Elsevier, vol. 125(1), pages 163-181.
    456. Cleomar Gomes da Silva & Flávio Vilela Vieira, 2014. "BRICS countries: real interest rates and long memory," Economics Bulletin, AccessEcon, vol. 34(1), pages 409-419.
    457. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2020. "The Behavior of Real Interest Rates: New Evidence from a ``Suprasecular" Perspective," Working Papers 202093, University of Pretoria, Department of Economics.
    458. Charfeddine, Lanouar & Maouchi, Youcef, 2019. "Are shocks on the returns and volatility of cryptocurrencies really persistent?," Finance Research Letters, Elsevier, vol. 28(C), pages 423-430.
    459. Gil-Alana, Luis A. & Yaya, OlaOluwa S. & Shittu, Olanrewaju I., 2014. "Global temperatures and sunspot numbers. Are they related?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 42-50.
    460. Lai, Kon S., 2008. "The puzzling unit root in the real interest rate and its inconsistency with intertemporal consumption behavior," Journal of International Money and Finance, Elsevier, vol. 27(1), pages 140-155, February.
    461. Gabriel Rodríguez & José Carlos Gonzáles Tanaka, 2016. "An Empirical Application of a Random Level Shifts Model with Time-Varying Probability and Mean Reversion to the Volatility of Latin-American Forex Markets Returns [Una aplicación empírica de un modelo," Documentos de Trabajo / Working Papers 2016-415, Departamento de Economía - Pontificia Universidad Católica del Perú.
    462. Kobayashi, Kiyoshi & Kaito, Kiyoyuki & Lethanh, Nam, 2012. "A statistical deterioration forecasting method using hidden Markov model for infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 544-561.
    463. Juan Hoyo & Guillermo Llorente & Carlos Rivero, 2020. "A Testing Procedure for Constant Parameters in Stochastic Volatility Models," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 163-186, June.
    464. Augustine Arize & John Malindretos & Kiseok Nam, 2005. "Inflation and Structural Change in 50 Developing Countries," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 33(4), pages 461-471, December.
    465. Carlos A. Abanto-Valle & Gabriel Rodríguez & Luis M. Castro Cepero & Hernán B. Garrafa-Aragón, 2024. "Approximate Bayesian Estimation of Stochastic Volatility in Mean Models Using Hidden Markov Models: Empirical Evidence from Emerging and Developed Markets," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1775-1801, September.
    466. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.
    467. Andreou, Elena & Ghysels, Eric, 2006. "Monitoring disruptions in financial markets," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 77-124.
    468. Huthaifa Alqaralleh & Alaa Adden Abuhommous & Ahmad Alsaraireh, 2020. "Modelling and Forecasting the Volatility of Cryptocurrencies: A Comparison of Nonlinear GARCH-Type Models," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(4), pages 346-356, July.
    469. Gil-Alana, Luis A. & Mudida, Robert & Zerbo, Eleazar, 2021. "GDP per capita IN SUB-SAHARAN Africa: A time series approach using long memory," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 175-190.
    470. 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.
    471. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    472. Walter Kramer & Philipp Sibbertsen, 2002. "Testing for Structural Changes in the Presence of Long Memory," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(3), pages 235-242, December.
    473. Fabio Trojani & Francesco Audrino, 2006. "Estimating and predicting multivariate volatility thresholds in global stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 345-369.
    474. Kellard, Neil M. & Jiang, Ying & Wohar, Mark, 2015. "Spurious long memory, uncommon breaks and the implied–realized volatility puzzle," Journal of International Money and Finance, Elsevier, vol. 56(C), pages 36-54.
    475. Paulo M.M. Rodrigues & Uwe Hassler, 2014. "Persistence in the Banking Industry: Fractional integration and breaks in memory," Working Papers w201406, Banco de Portugal, Economics and Research Department.
    476. Jonathan Dark & Xin Gao & Thijs van der Heijden & Federico Nardari, 2022. "Forecasting variance swap payoffs," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2135-2164, December.
    477. Juan Carlos Cuestas & Luis A. Gil-Alana & Karl Taylor, 2016. "Inflation convergence in Central and Eastern Europe vs. the Eurozone: Non-linearities and long memory," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(5), pages 519-538, November.
    478. Daniela Osterrieder & Peter C. Schotman, 2012. "The Volatility of Long-term Bond Returns: Persistent Interest Shocks and Time-varying Risk Premiums," CREATES Research Papers 2012-35, Department of Economics and Business Economics, Aarhus University.
    479. Gil-Alana, Luis A. & Fischer, Christian, 2007. "International traveling and trade: further evidence for the case of Spanish wine based on fractional VAR specifications," 105th Seminar, March 8-10, 2007, Bologna, Italy 7859, European Association of Agricultural Economists.
    480. Soosung Hwang & Steve E. Satchell & Pedro L. Valls Pereira, 2007. "How Persistent is Stock Return Volatility? An Answer with Markov Regime Switching Stochastic Volatility Models," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 34(5‐6), pages 1002-1024, June.
    481. Davis, Richard A. & Hancock, Stacey A. & Yao, Yi-Ching, 2016. "On consistency of minimum description length model selection for piecewise autoregressions," Journal of Econometrics, Elsevier, vol. 194(2), pages 360-368.
    482. Maheu John, 2005. "Can GARCH Models Capture Long-Range Dependence?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-43, December.
    483. Luis Alberiko Gil-Alana, 2005. "Unit and Fractional Roots in the Presence of Abrupt Changes with an Application to the Brazilian Inf," Faculty Working Papers 19/05, School of Economics and Business Administration, University of Navarra.
    484. Niels Haldrup & Robinson Kruse, 2014. "Discriminating between fractional integration and spurious long memory," CREATES Research Papers 2014-19, Department of Economics and Business Economics, Aarhus University.
    485. 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.
    486. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    487. Luis A. Gil‐Alana, 2004. "A joint test of fractional integration and structural breaks at a known period of time," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 691-700, September.
    488. McMillan, David G. & Ruiz, Isabel, 2009. "Volatility persistence, long memory and time-varying unconditional mean: Evidence from 10 equity indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 578-595, May.
    489. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.
    490. Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.
    491. Al-Shboul, Mohammad & Anwar, Sajid, 2016. "Fractional integration in daily stock market indices at Jordan's Amman stock exchange," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 16-37.
    492. Nazlioglu, Saban & Gupta, Rangan & Gormus, Alper & Soytas, Ugur, 2020. "Price and volatility linkages between international REITs and oil markets," Energy Economics, Elsevier, vol. 88(C).
    493. 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).
    494. Cunado, J. & Gil-Alana, L. A. & Perez de Gracia, F., 2004. "Is the US fiscal deficit sustainable?: A fractionally integrated approach," Journal of Economics and Business, Elsevier, vol. 56(6), pages 501-526.
    495. Feng, Lingbing & Fu, Tong & Shi, Yanlin, 2022. "How does news sentiment affect the states of Japanese stock return volatility?," International Review of Financial Analysis, Elsevier, vol. 84(C).
    496. Laura Mayoral, 2003. "Further Evidence on the Uncertain (Fractional) Unit Root in Real GNP," Working Papers 82, Barcelona School of Economics.
    497. Mengmeng Guo & Wolfgang Härdle, 2012. "Simultaneous confidence bands for expectile functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(4), pages 517-541, October.
    498. Gadea, Maria Dolores & Sabate, Marcela & Serrano, Jose Maria, 2004. "Structural breaks and their trace in the memory: Inflation rate series in the long-run," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(2), pages 117-134, April.
    499. Agata Kliber, 2013. "Influence of the Greek Crisis on the Risk Perception of European Economies," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(2), pages 125-161, June.
    500. 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.
    501. Cheong, Chin Wen, 2008. "Time-varying volatility in Malaysian stock exchange: An empirical study using multiple-volatility-shift fractionally integrated model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 889-898.
    502. Omane-Adjepong, Maurice & Boako, Gideon, 2017. "Long-range dependence in returns and volatility of global gold market amid financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 188-202.
    503. 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.
    504. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    505. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    506. Kyongwook Choi & Eric Zivot, 2003. "Long Memory and Structural Changes in the Forward Discount: An Empirical Investigation," Econometrics 0307001, University Library of Munich, Germany, revised 23 Jul 2003.
    507. Haldrup Niels & Nielsen Morten Ø., 2006. "Directional Congestion and Regime Switching in a Long Memory Model for Electricity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-24, September.
    508. Paul De Grauwe & Zhaoyong Zhang & Kin-Yip Ho & Yanlin Shi & Zhaoyong Zhang, 2016. "It takes two to tango: A regime-switching analysis of the correlation dynamics between the mainland Chinese and Hong Kong stock markets," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(1), pages 41-65, February.
    509. Francis Ahking, 2010. "Non-parametric tests of real exchange rates in the post-Bretton Woods era," Empirical Economics, Springer, vol. 39(2), pages 439-456, October.
    510. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2022. "Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers," Journal of Econometrics, Elsevier, vol. 227(1), pages 285-304.
    511. Dufrénot, Gilles & Lardic, Sandrine & Mathieu, Laurent & Mignon, Valérie & Péguin-Feissolle, Anne, 2008. "Explaining the European exchange rates deviations: Long memory or non-linear adjustment?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 207-215, July.
    512. Beran, Jan & Shumeyko, Yevgen, 2012. "Bootstrap testing for discontinuities under long-range dependence," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 322-347.
    513. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2018. "Monetary policy shocks, inflation persistence, and long memory," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 117-127.
    514. Christian Fischer & Luis Alberiko Gil-Alana, 2005. "The Nature of the Relationship between International Tourism and International Trade: The Case of Ge," Faculty Working Papers 15/05, School of Economics and Business Administration, University of Navarra.
    515. Dooruj Rambaccussing & Murat Mazibas, 2020. "True versus Spurious Long Memory in Cryptocurrencies," JRFM, MDPI, vol. 13(9), pages 1-11, August.
    516. Matei Demetrescu & Mehdi Hosseinkouchack, 2022. "Autoregressive spectral estimates under ignored changes in the mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(2), pages 329-340, March.
    517. Chaim, Pedro & Laurini, Márcio P., 2019. "Nonlinear dependence in cryptocurrency markets," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 32-47.
    518. Canarella, Giorgio & Miller, Stephen M., 2017. "Inflation targeting and inflation persistence: New evidence from fractional integration and cointegration," Journal of Economics and Business, Elsevier, vol. 92(C), pages 45-62.
    519. Baek, Changryong & Fortuna, Natércia & Pipiras, Vladas, 2014. "Can Markov switching model generate long memory?," Economics Letters, Elsevier, vol. 124(1), pages 117-121.
    520. 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.
    521. Giorgio Canarella & Luis A. Gil‐Alana & Rangan Gupta & Stephen M. Miller, 2022. "The behaviour of real interest rates: New evidence from a 'suprasecular' perspective," International Finance, Wiley Blackwell, vol. 25(1), pages 46-64, April.
    522. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2017. "The Memory of Volatility," Hannover Economic Papers (HEP) dp-601, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    523. Tapas K. Mishra, 2006. "A Further Look into the Demography-based GDP Forecasting Method," Working Papers of BETA 2006-17, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    524. Gawon Yoon, 2010. "Long memory in return volatility," Applied Economics Letters, Taylor & Francis Journals, vol. 17(4), pages 345-349.
    525. Leschinski, Christian & Sibbertsen, Philipp, 2017. "Origins of Spurious Long Memory," Hannover Economic Papers (HEP) dp-595, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    526. Jose Maria Fernandez-Crehuet & Luis Alberiko Gil-Alana & Cristina Martí Barco, 2020. "Unemployment and Fertility: A Long Run Relationship," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(3), pages 1177-1196, December.
    527. Richard Paap & Philip Hans Franses & Marco Van Der Leij, 2002. "Modelling and forecasting level shifts in absolute returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 601-616.
    528. Kang, Sang Hoon & Cho, Hwan-Gue & Yoon, Seong-Min, 2009. "Modeling sudden volatility changes: Evidence from Japanese and Korean stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3543-3550.
    529. Duan, Kun & Gao, Yang & Mishra, Tapas & Satchell, Stephen, 2023. "Efficiency dynamics across segmented Bitcoin Markets: Evidence from a decomposition strategy," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).
    530. Huang, Yirong & Luo, Yi, 2024. "Forecasting conditional volatility based on hybrid GARCH-type models with long memory, regime switching, leverage effect and heavy-tail: Further evidence from equity market," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    531. Oomen, Roel C. A., 2004. "Modelling realized variance when returns are serially correlated [Modellierung realisierter Varianz bei autokorrelierten Erträgen]," Discussion Papers, Research Unit: Market Processes and Governance SP II 2004-11, WZB Berlin Social Science Center.
    532. Leschinski, Christian & Sibbertsen, Philipp, 2018. "The Periodogram of Spurious Long-Memory Processes," Hannover Economic Papers (HEP) dp-632, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    533. Biage, Milton & Nelcide, Pierre Joseph, 2020. "Effects of asset frequency components on value-at-risk in emerging and developed markets," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 40(1), August.
    534. Lanouar Charfeddine & Dominique Guegan, 2007. "Which is the best model for the US inflation rate: a structural changes model or a long memory process?," Post-Print halshs-00188309, HAL.
    535. José M. Belbute & Alfredo Marvão Pereira, 2016. "Updated Reference Forecasts for Global CO2 Emissions from Fossil-Fuel Consumption," Working Papers 170, Department of Economics, College of William and Mary.
    536. Apergis, Nicholas & Tsoumas, Chris, 2011. "Integration properties of disaggregated solar, geothermal and biomass energy consumption in the U.S," Energy Policy, Elsevier, vol. 39(9), pages 5474-5479, September.
    537. Vasco J. Gabriel & Martin Sola & Zacharias Psaradakis, 2002. "Residual-based tests for cointegration and multiple regime shifts," NIPE Working Papers 7/2002, NIPE - Universidade do Minho.
    538. Dominique Guegan & Zhiping Lu, 2007. "A note on self-similarity for discrete time series," Post-Print halshs-00187910, HAL.
    539. Leonardo Ieracitano Vieira & Márcio Poletti Laurini, 2023. "Time-varying higher moments in Bitcoin," Digital Finance, Springer, vol. 5(2), pages 231-260, June.
    540. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    541. Terence C. Mills, 2007. "Time series modelling of two millennia of northern hemisphere temperatures: long memory or shifting trends?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 83-94, January.
    542. Achour Maha & Trabelsi Abdelwahed, 2011. "Markov Switching and State-Space Approaches for Investigating the Link between Egyptian Inflation Level and Uncertainty," Review of Middle East Economics and Finance, De Gruyter, vol. 6(3), pages 46-62, February.
    543. Guglielmo Maria Caporale & Juncal Cunado & Luis A. Gil-Alana, 2007. "Deterministic versus Stochastic Seasonal Fractional Integration and Structural Breaks," CESifo Working Paper Series 1989, CESifo.
    544. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, September.
    545. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2006. "Purchasing Power Parity: The Irish Experience Re-visited," Trinity Economics Papers tep200615, Trinity College Dublin, Department of Economics.
    546. Eric Hillebrand, 2003. "Overlaying Time Scales and Persistence Estimation in GARCH(1,1) Models," Econometrics 0301003, University Library of Munich, Germany.
    547. Juan Carlos Cuestas & Luis A. Gil-Alana & Paulo José Regis, 2015. "The Sustainability of European External Debt: What have We Learned?," Review of International Economics, Wiley Blackwell, vol. 23(3), pages 445-468, August.
    548. Dark, Jonathan, 2018. "Multivariate models with long memory dependence in conditional correlation and volatility," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 162-180.
    549. Axioglou, Christos & Skouras, Spyros, 2011. "Markets change every day: Evidence from the memory of trade direction," Journal of Empirical Finance, Elsevier, vol. 18(3), pages 423-446, June.
    550. Zegadło, Piotr, 2022. "Identifying bull and bear market regimes with a robust rule-based method," Research in International Business and Finance, Elsevier, vol. 60(C).
    551. Burcu Kiran, 2010. "The Structure of Tourism Revenues in Turkey: Evidence from Fractional Integration under Multiple Structural Breaks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 85-96.
    552. Cedric Okou & Eric Jacquier, 2014. "Horizon Effect in the Term Structure of Long-Run Risk-Return Trade-Offs," CIRANO Working Papers 2014s-36, CIRANO.
    553. Steven Clark & T. Coggin, 2011. "Are U.S. stock prices mean reverting? Some new tests using fractional integration models with overlapping data and structural breaks," Empirical Economics, Springer, vol. 40(2), pages 373-391, April.

  39. Atsushi Inoue & Lutz Kilian, 2000. "Bootstrapping Autoregressive Processes with Possible Unit Roots," Econometric Society World Congress 2000 Contributed Papers 0401, Econometric Society.

    Cited by:

    1. Wendy Nyakabawo & Stephen M. Miller & Mehmet Balcilar & Sonali Das & Rangan Gupta, 2013. "Temporal Causality between House Prices and Output in the U.S.: A Bootstrap Rolling-Window Approach," Working papers 2013-14, University of Connecticut, Department of Economics.
    2. 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.
    3. DUFOUR, Jean-Marie & JOUINI, Tarek, 2005. "Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing," Cahiers de recherche 16-2005, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    4. 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.
    5. Francis X. Diebold & Lutz Kilian, "undated". "Measuring Predictability: Theory and Macroeconomic Applications," CARESS Working Papres 97-19, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    6. Kilian, Lutz & Gonçalves, Sílvia, 2002. "Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form," Discussion Paper Series 1: Economic Studies 2002,26, Deutsche Bundesbank.
    7. Schusser, Sandra & Jaraite, Jurate, 2016. "Explaining the Interplay of Three Markets: Green Certificates, Carbon Emissions and Electricity," CERE Working Papers 2016:10, CERE - the Center for Environmental and Resource Economics.
    8. Hwang, Eunju & Shin, Dong Wan, 2015. "Stationary bootstrapping for semiparametric panel unit root tests," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 14-25.
    9. Lopez, Claude & Murray, Chris & Papell, David, 2009. "Median-Unbiased Estimation in DF-GLS Regressions and the PPP Puzzle," MPRA Paper 26091, University Library of Munich, Germany.
    10. Quentin Giai Gianetto & Hamdi Raïssi, 2015. "Testing Instantaneous Causality in Presence of Nonconstant Unconditional Covariance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 46-53, January.
    11. Ozdemir, Zeynel Abidin & Cakan, Esin, 2010. "The persistence in real exchange rate: Evidence from East Asian countries," Economic Modelling, Elsevier, vol. 27(5), pages 891-895, September.
    12. Atsushi Inoue & `Oscar Jord`a & Guido M. Kuersteiner, 2023. "Inference for Local Projections," Papers 2306.03073, arXiv.org, revised Aug 2024.
    13. Helmut Luetkepohl, 2011. "Vector Autoregressive Models," Economics Working Papers ECO2011/30, European University Institute.
    14. Park, Joon, 2003. "A Bootstrap Theory for Weakly Integrated Processes," Working Papers 2003-16, Rice University, Department of Economics.
    15. DUFOUR, Jean-Marie, 2005. "Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics," Cahiers de recherche 2005-03, Universite de Montreal, Departement de sciences economiques.
    16. Claude Lopez & Christian J. Murray & David H. Papell, 2004. "State of the Art Unit Root Tests and Purchasing Power Parity," University of Cincinnati, Economics Working Papers Series 2004-04, University of Cincinnati, Department of Economics.
    17. Corradi, Valentina & Iglesias, Emma M., 2008. "Bootstrap refinements for QML estimators of the GARCH(1,1) parameters," Journal of Econometrics, Elsevier, vol. 144(2), pages 500-510, June.
    18. Bulat Gafarov & Madina Karamysheva & Andrey Polbin & Anton Skrobotov, 2024. "Wild inference for wild SVARs with application to heteroscedasticity-based IV," Papers 2407.03265, arXiv.org, revised Nov 2024.
    19. Kim, Jae H., 2003. "Forecasting autoregressive time series with bias-corrected parameter estimators," International Journal of Forecasting, Elsevier, vol. 19(3), pages 493-502.
    20. Amilcar Velez, 2023. "The Local Projection Residual Bootstrap for AR(1) Models," Papers 2309.01889, arXiv.org, revised Mar 2025.
    21. Juan F. Rubio-Ramirez & Daniel F. Waggoner & Tao Zha, 2008. "Structural vector autoregressions: theory of identification and algorithms for inference," FRB Atlanta Working Paper 2008-18, Federal Reserve Bank of Atlanta.
    22. Kilian, Lutz & Baumeister, Christiane, 2013. "Do Oil Price Increases Cause Higher Food Prices?," CEPR Discussion Papers 9689, C.E.P.R. Discussion Papers.
    23. Dong Jin Lee, 2021. "Bootstrap tests for structural breaks when the regressors and the serially correlated error term are unstable," Bulletin of Economic Research, Wiley Blackwell, vol. 73(2), pages 212-229, April.
    24. Rossi, Barbara, 2005. "Confidence Intervals for Half-Life Deviations From Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 432-442, October.
    25. Daniel Grabowski & Anna Staszewska-Bystrova & Peter Winker, 2020. "Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 5-32, March.
    26. Yuriy Gorodnichenko, 2005. "Reduced-Rank Identification of Structural Shocks in VARs," Macroeconomics 0512011, University Library of Munich, Germany.
    27. Pesavento, Elena, 2004. "Analytical evaluation of the power of tests for the absence of cointegration," Journal of Econometrics, Elsevier, vol. 122(2), pages 349-384, October.
    28. Choi, In, 2005. "Inconsistency of bootstrap for nonstationary, vector autoregressive processes," Statistics & Probability Letters, Elsevier, vol. 75(1), pages 39-48, November.
    29. Giuseppe Cavaliere & Anders Rahbek & A. M. Robert Taylor, 2008. "Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility," Discussion Papers 08-34, University of Copenhagen. Department of Economics.
    30. van Giersbergen, Noud P. A., 2003. "A note on bootstrapping unit root tests in the presence of a non-zero drift," Economics Letters, Elsevier, vol. 78(2), pages 259-265, February.
    31. Yuriy Gorodnichenko & Linda Tesar, 2005. "A Re-Examination of the Border Effect," Working Papers 546, Research Seminar in International Economics, University of Michigan.
    32. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Arslanturk, Yalcin, 2010. "Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window," Energy Economics, Elsevier, vol. 32(6), pages 1398-1410, November.
    33. Carlos Usabiaga & Diego Romero-Ávila, 2012. "New Disaggregate Evidence on Spanish Inflation Persistence," EcoMod2012 3800, EcoMod.
    34. Donald W. K. Andrews & Patrik Guggenberger, 2014. "A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 376-381, May.
    35. Ruxandra Prodan, 2004. "Potential Pitfalls in Determining Multiple Structural Changes with an Application to Purchasing Power Parity," Econometric Society 2004 North American Summer Meetings 90, Econometric Society.
    36. Bruggeman, Annick & Donati, Paola & Warne, Anders, 2003. "Is the demand for euro area M3 stable?," Working Paper Series 255, European Central Bank.
    37. Sevan Gulesserian & Mohitosh Kejriwal, 2014. "On the power of bootstrap tests for stationarity: a Monte Carlo comparison," Empirical Economics, Springer, vol. 46(3), pages 973-998, May.
    38. Jui-Chung Yang & Ke-Li Xu, 2013. "Estimation and Inference under Weak Identi cation and Persistence: An Application on Forecast-Based Monetary Policy Reaction Function," 2013 Papers pya307, Job Market Papers.
    39. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    40. Shin, Dong Wan & Hwang, Eunju, 2013. "Stationary bootstrapping for cointegrating regressions," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 474-480.
    41. Dufour, Jean-Marie & Jouini, Tarek, 2006. "Finite-sample simulation-based inference in VAR models with application to Granger causality testing," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 229-254.
    42. Pesavento, Elena & Rossi, Barbara, 2007. "Impulse response confidence intervals for persistent data: What have we learned?," Journal of Economic Dynamics and Control, Elsevier, vol. 31(7), pages 2398-2412, July.
    43. Diego Romero-Ávila & Carlos Usabiaga, 2012. "Disaggregate evidence on Spanish inflation persistence," Applied Economics, Taylor & Francis Journals, vol. 44(23), pages 3029-3046, August.
    44. Omtzigt Pieter & Fachin Stefano, 2002. "Bootstrapping and Bartlett corrections in the cointegrated VAR model," Economics and Quantitative Methods qf0212, Department of Economics, University of Insubria.
    45. Giuseppe Cavaliere & Anders Rahbek & A.M.Robert Taylor, 2009. "Co-integration Rank Testing under Conditional Heteroskedasticity," CREATES Research Papers 2009-22, Department of Economics and Business Economics, Aarhus University.
    46. Murray, Christian J. & Papell, David H., 2002. "The purchasing power parity persistence paradigm," Journal of International Economics, Elsevier, vol. 56(1), pages 1-19, January.
    47. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    48. Bob Nobay & Ivan Paya & David A. Peel, 2010. "Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 135-150, February.
    49. Lieb, Lenard & Smeekes, Stephan, 2017. "Inference for Impulse Responses under Model Uncertainty," Research Memorandum 022, Maastricht University, Graduate School of Business and Economics (GSBE).
    50. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    51. Kruse, Yves Robinson & Kaufmann, Hendrik, 2015. "Bias-corrected estimation in mildly explosive autoregressions," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112897, Verein für Socialpolitik / German Economic Association.
    52. Christis Katsouris, 2023. "Bootstrapping Nonstationary Autoregressive Processes with Predictive Regression Models," Papers 2307.14463, arXiv.org.
    53. Kruse, Robinson & Kaufmann, Hendrik & Wegener, Christoph, 2018. "Bias-corrected estimation for speculative bubbles in stock prices," Economic Modelling, Elsevier, vol. 73(C), pages 354-364.
    54. Òscar Jordà & Alan M. Taylor, 2024. "Local Projections," NBER Working Papers 32822, National Bureau of Economic Research, Inc.
    55. Diego Romero-Avila & Carlos Usabiaga, 2007. "Unit root tests and persistence of unemployment: Spain vs. the United States," Applied Economics Letters, Taylor & Francis Journals, vol. 14(6), pages 457-461.
    56. Lei Pan & Vinod Mishra, 2019. "International Portfolio Diversification Possibilities: Could BRICS become a Destination for G7 Invesments," Monash Economics Working Papers 11-18, Monash University, Department of Economics.
    57. Diego Romero‐Ávila & Carlos Usabiaga, 2007. "Unit Root Tests, Persistence, and the Unemployment Rate of the U.S. States," Southern Economic Journal, John Wiley & Sons, vol. 73(3), pages 698-716, January.
    58. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    59. Giuseppe Cavaliere & Anders Rahbek & A. M. Robert Taylor, 2009. "Co-integration rank tests under conditional heteroskedasticity," Discussion Papers 09/02, University of Nottingham, Granger Centre for Time Series Econometrics.
    60. Jan J J Groen & Clare Lombardelli, 2004. "Real exchange rates and the relative prices of non-traded and traded goods: an empirical analysis," Bank of England working papers 223, Bank of England.
    61. Burridge, Peter & Robert Taylor, A. M., 2004. "Bootstrapping the HEGY seasonal unit root tests," Journal of Econometrics, Elsevier, vol. 123(1), pages 67-87, November.
    62. Clements, Michael P. & Kim, Jae H., 2007. "Bootstrap prediction intervals for autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3580-3594, April.
    63. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    64. Stanislav Anatolyev, 2007. "The basics of bootstrapping (in Russian)," Quantile, Quantile, issue 3, pages 1-12, September.
    65. Gafarov, Bulat & Meier, Matthias & Montiel Olea, José Luis, 2018. "Delta-method inference for a class of set-identified SVARs," Journal of Econometrics, Elsevier, vol. 203(2), pages 316-327.
    66. 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.
    67. Mehmet Balcilar & Zeynel Ozdemir, 2013. "The export-output growth nexus in Japan: a bootstrap rolling window approach," Empirical Economics, Springer, vol. 44(2), pages 639-660, April.

  40. Peter Christoffersen & Jinyong Hahn & Atsushi Inoue, 1999. "Testing, Comparing, and Combining Value at Risk Measures," Center for Financial Institutions Working Papers 99-44, Wharton School Center for Financial Institutions, University of Pennsylvania.

    Cited by:

    1. Victor Chernozhukov & Ivan Fernandez-Val, 2011. "Inference for extremal conditional quantile models, with an application to market and birthweight risks," CeMMAP working papers CWP40/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Shcherba, Alexandr, 2011. "Comparison of VaR estimation methods for different forecasting samples for Russian stocks," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 24(4), pages 58-70.
    3. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    4. Mauro Bernardi & Leopoldo Catania & Lea Petrella, 2014. "Are news important to predict large losses?," Papers 1410.6898, arXiv.org, revised Oct 2014.
    5. Dany Rogers Silva & Karem Cristina de Sousa Ribeiro & Hsia Hua Sheng, 2011. "Trade credit profitability measurement: application in a wholesalerdistributor case," Brazilian Business Review, Fucape Business School, vol. 8(2), pages 22-41, April.
    6. Parente, Paulo M.D.C. & Smith, Richard J., 2011. "Gel Methods For Nonsmooth Moment Indicators," Econometric Theory, Cambridge University Press, vol. 27(1), pages 74-113, February.
    7. Kilic, Ekrem, 2006. "Violation duration as a better way of VaR model evaluation : evidence from Turkish market portfolio," MPRA Paper 5610, University Library of Munich, Germany.
    8. Chen, Xiaohong & Hong, Han & Shum, Matthew, 2007. "Nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models," Journal of Econometrics, Elsevier, vol. 141(1), pages 109-140, November.

  41. Francis X. Diebold & Andrew Hickman & Atsushi Inoue & Til Schuermann, 1997. "Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think," Center for Financial Institutions Working Papers 97-34, Wharton School Center for Financial Institutions, University of Pennsylvania.

    Cited by:

    1. Kam Fong Chan & Christopher Gan & Patricia A. McGraw, 2003. "A Hedging Strategy for New Zealand’s Exporters in Transaction Exposure to Currency Risk," Multinational Finance Journal, Multinational Finance Journal, vol. 7(1-2), pages 25-54, March-Jun.
    2. Odening, Martin & Hinrichs, Jan, 2003. "Die Quantifizierung von Marktrisiken in der Tierproduktion mittels Value-at-Risk und Extreme-Value-Theory," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 52(02), pages 1-11.
    3. J. Q. Smith & António Santos, 2003. "Second Order Filter Distribution Approximations for Financial Time Series with Extreme Outlier," GEMF Working Papers 2003-03, GEMF, Faculty of Economics, University of Coimbra.
    4. Peter F. Christoffersen & Francis X. Diebold, 1997. "How Relevant is Volatility Forecasting for Financial Risk Management?," Center for Financial Institutions Working Papers 97-45, Wharton School Center for Financial Institutions, University of Pennsylvania.
    5. Wang, Jying-Nan & Du, Jiangze & Hsu, Yuan-Teng, 2018. "Measuring long-term tail risk: Evaluating the performance of the square-root-of-time rule," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 120-138.
    6. Wolff, Christian & Lehnert, Thorsten, 2001. "Modelling Scale-Consistent VaR with the Truncated Lévy Flight," CEPR Discussion Papers 2711, C.E.P.R. Discussion Papers.
    7. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2014. "VaR performance during the subprime and sovereign debt crises: An application to emerging markets," Emerging Markets Review, Elsevier, vol. 20(C), pages 23-41.
    8. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Practical volatility and correlation modeling for financial market risk management," CFS Working Paper Series 2005/02, Center for Financial Studies (CFS).
    9. Peter F. Christoffersen & Francis X. Diebold & Til Schuermann, 1998. "Horizon problems and extreme events in financial risk management," Economic Policy Review, Federal Reserve Bank of New York, vol. 4(Oct), pages 109-118.
    10. Wang, Jying-Nan & Yeh, Jin-Huei & Cheng, Nick Ying-Pin, 2011. "How accurate is the square-root-of-time rule in scaling tail risk: A global study," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1158-1169, May.
    11. Danielsson, Jon & Zigrand, Jean-Pierre, 2006. "On time-scaling of risk and the square-root-of-time rule," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2701-2713, October.
    12. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    13. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
    14. Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024. "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, vol. 158(C).
    15. Saadi, Samir & Rahman, Abdul, 2008. "Evidence of non-stationary bias in scaling by square root of time: Implications for Value-at-Risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 272-289, July.
    16. Kavussanos, Manolis G. & Dimitrakopoulos, Dimitris N., 2011. "Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 258-268.
    17. Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman, 2019. "Bayesian Risk Forecasting for Long Horizons," Tinbergen Institute Discussion Papers 19-018/III, Tinbergen Institute.
    18. Amy S. K. Wong, 2006. "Basel II and the Risk Management of Basket Options with Time-Varying Correlations," International Journal of Central Banking, International Journal of Central Banking, vol. 2(4), December.
    19. Odening, Martin & Hinrichs, Jan, 2002. "Assessment Of Market Risk In Hog Production Using Value-At-Risk And Extreme Value Theory," 2002 Annual meeting, July 28-31, Long Beach, CA 19907, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    20. Mark R. Manfredo & Raymond M. Leuthold, 1998. "Agricultural Applications of Value-at-Risk Analysis: A Perspective," Finance 9805002, University Library of Munich, Germany.
    21. Th'eophile Griveau-Billion & Ben Calderhead, 2019. "A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour," Papers 1904.08153, arXiv.org, revised Jan 2020.
    22. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.

  42. Atsushi Inoue, "undated". "Testing Change in Time Series," Computing in Economics and Finance 1997 7, Society for Computational Economics.

    Cited by:

    1. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.
    2. Bücher, Axel & Ruppert, Martin, 2013. "Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 208-229.
    3. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 290-318.
    4. Tatevik Sekhposyan & Barbara Rossi, 2015. "Alternative Tests for Correct Specification of Conditional Predictive Densities," Working Papers 758, Barcelona School of Economics.
    5. Chen, Bin & Hong, Yongmiao, 2012. "Testing For The Markov Property In Time Series," Econometric Theory, Cambridge University Press, vol. 28(1), pages 130-178, February.
    6. Qu, Zhongjun, 2008. "Testing for structural change in regression quantiles," Journal of Econometrics, Elsevier, vol. 146(1), pages 170-184, September.
    7. Rossi, Barbara & Sekhposyan, Tatevik, 2013. "Conditional predictive density evaluation in the presence of instabilities," Journal of Econometrics, Elsevier, vol. 177(2), pages 199-212.
    8. Valentina Corradi & Norman R. Swanson, 2003. "Bootstrap Specification Tests for Diffusion Processes," Departmental Working Papers 200321, Rutgers University, Department of Economics.
    9. Fabio Busetti, 2012. "On detecting end-of-sample instabilities," Temi di discussione (Economic working papers) 881, Bank of Italy, Economic Research and International Relations Area.
    10. Jonas Dovern & Geoff Kenny, 2020. "Anchoring Inflation Expectations in Unconventional Times: Micro Evidence for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 16(5), pages 309-347, October.
    11. Bücher, Axel & Kojadinovic, Ivan & Rohmer, Tom & Segers, Johan, 2014. "Detecting changes in cross-sectional dependence in multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 111-128.
    12. Corradi, V. & Swanson, N.R., 2000. "A Consistent Test for Nonlinear Out of Sample Predictive Accuracy," Discussion Papers 0012, University of Exeter, Department of Economics.
    13. Corradi, Valentina & Swanson, Norman R., 2004. "A test for the distributional comparison of simulated and historical data," Economics Letters, Elsevier, vol. 85(2), pages 185-193, November.
    14. Valentina Corradi & Norman R. Swanson, 2003. "The Effect of Data Transformation on Common Cycle, Cointegration and Unit Root Tests: Monte Carlo Results and a Simple Test," Departmental Working Papers 200322, Rutgers University, Department of Economics.
    15. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    16. Diep Duong & Norman Swanson, 2013. "Density and Conditional Distribution Based Specification Analysis," Departmental Working Papers 201312, Rutgers University, Department of Economics.
    17. Holmes, Mark & Kojadinovic, Ivan & Quessy, Jean-François, 2013. "Nonparametric tests for change-point detection à la Gombay and Horváth," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 16-32.
    18. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    19. Su, Liangjun & Xiao, Zhijie, 2008. "Testing for parameter stability in quantile regression models," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2768-2775, November.
    20. Bucher, Axel, 2013. "A note on weak convergence of the sequential multivariate empirical process under strong mixing," LIDAM Discussion Papers ISBA 2013028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    21. Wied, Dominik & Dehling, Herold & van Kampen, Maarten & Vogel, Daniel, 2014. "A fluctuation test for constant Spearman’s rho with nuisance-free limit distribution," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 723-736.
    22. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    23. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2012. "A new fluctuation test for constant variances with applications to finance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1111-1127, November.
    24. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics.
    25. Jean-François Quessy, 2019. "Consistent nonparametric tests for detecting gradual changes in the marginals and the copula of multivariate time series," Statistical Papers, Springer, vol. 60(3), pages 717-746, June.
    26. Leonie Selk & Natalie Neumeyer, 2013. "Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 770-788, December.
    27. Rohmer, Tom, 2016. "Some results on change-point detection in cross-sectional dependence of multivariate data with changes in marginal distributions," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 45-54.

Articles

  1. Inoue, Atsushi & Rossi, Barbara & Wang, Yiru, 2024. "Local projections in unstable environments," Journal of Econometrics, Elsevier, vol. 244(2).

    Cited by:

    1. Saadaoui, Jamel & Smyth, Russell & Vespignani, Joaquin, 2024. "Ensuring the security of the clean energy transition: Examining the impact of geopolitical risk on the price of critical minerals," MPRA Paper 122858, University Library of Munich, Germany.
    2. Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2024. "Machine Learning the Macroeconomic Effects of Financial Shocks," Papers 2412.07649, arXiv.org.

  2. Inoue, Atsushi & Kilian, Lutz, 2022. "Joint Bayesian inference about impulse responses in VAR models," Journal of Econometrics, Elsevier, vol. 231(2), pages 457-476.
    See citations under working paper version above.
  3. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2021. "Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 307-324, January.
    See citations under working paper version above.
  4. Atsushi Inoue & Lu Jin & Denis Pelletier, 2021. "Local-Linear Estimation of Time-Varying-Parameter GARCH Models and Associated Risk Measures [Modelling Volatility by Variance Decomposition]," Journal of Financial Econometrics, Oxford University Press, vol. 19(1), pages 202-234.

    Cited by:

    1. Cai, Zongwu & Juhl, Ted, 2023. "The distribution of rolling regression estimators," Journal of Econometrics, Elsevier, vol. 235(2), pages 1447-1463.
    2. Armin Pourkhanali & Jonathan Keith & Xibin Zhang, 2021. "Conditional Heteroscedasticity Models with Time-Varying Parameters: Estimation and Asymptotics," Monash Econometrics and Business Statistics Working Papers 15/21, Monash University, Department of Econometrics and Business Statistics.
    3. Zongwu Cai & Ted Juhl, 2020. "The Distribution Of Rolling Regression Estimators," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202218, University of Kansas, Department of Economics, revised Dec 2022.

  5. Atsushi Inoue & Barbara Rossi, 2021. "A new approach to measuring economic policy shocks, with an application to conventional and unconventional monetary policy," Quantitative Economics, Econometric Society, vol. 12(4), pages 1085-1138, November.
    See citations under working paper version above.
  6. Inoue, Atsushi & Kuo, Chun-Hung & Rossi, Barbara, 2020. "Identifying the sources of model misspecification," Journal of Monetary Economics, Elsevier, vol. 110(C), pages 1-18.
    See citations under working paper version above.
  7. Inoue, Atsushi & Kilian, Lutz, 2020. "The uniform validity of impulse response inference in autoregressions," Journal of Econometrics, Elsevier, vol. 215(2), pages 450-472.
    See citations under working paper version above.
  8. Inoue, Atsushi & Rossi, Barbara, 2019. "The effects of conventional and unconventional monetary policy on exchange rates," Journal of International Economics, Elsevier, vol. 118(C), pages 419-447.
    See citations under working paper version above.
  9. Atsushi Inoue & Mototsugu Shintani, 2018. "Quasi‐Bayesian model selection," Quantitative Economics, Econometric Society, vol. 9(3), pages 1265-1297, November.
    See citations under working paper version above.
  10. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    See citations under working paper version above.
  11. 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.
    See citations under working paper version above.
  12. Inoue, Atsushi & Kilian, Lutz, 2016. "Joint confidence sets for structural impulse responses," Journal of Econometrics, Elsevier, vol. 192(2), pages 421-432.
    See citations under working paper version above.
  13. Yasuo Hirose & Atsushi Inoue, 2016. "The Zero Lower Bound and Parameter Bias in an Estimated DSGE Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 630-651, June.
    See citations under working paper version above.
  14. Emily Anderson & Atsushi Inoue & Barbara Rossi, 2016. "Heterogeneous Consumers and Fiscal Policy Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(8), pages 1877-1888, December.
    See citations under working paper version above.
  15. Han, Xu & Inoue, Atsushi, 2015. "Tests For Parameter Instability In Dynamic Factor Models," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1117-1152, October.
    See citations under working paper version above.
  16. Inoue, Atsushi & Kilian, Lutz, 2013. "Inference on impulse response functions in structural VAR models," Journal of Econometrics, Elsevier, vol. 177(1), pages 1-13.
    See citations under working paper version above.
  17. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    See citations under working paper version above.
  18. Atsushi Inoue, 2012. "Mean-Plus-Noise Factor Models: An Empirical Exploration," The Japanese Economic Review, Japanese Economic Association, vol. 63(3), pages 289-309, September.

    Cited by:

    1. Eo, Yunjong & Kim, Chang-Jin, 2012. "Markov-Switching Models with Evolving Regime-Specific Parameters: Are Post-War Booms or Recessions All Alike?," Working Papers 2012-04, University of Sydney, School of Economics.

  19. Hall, Alastair R. & Inoue, Atsushi & Nason, James M. & Rossi, Barbara, 2012. "Information criteria for impulse response function matching estimation of DSGE models," Journal of Econometrics, Elsevier, vol. 170(2), pages 499-518.
    See citations under working paper version above.
  20. Atsushi Inoue & Barbara Rossi, 2011. "Identifying the Sources of Instabilities in Macroeconomic Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 93(4), pages 1186-1204, November.

    Cited by:

    1. Pesaran, M.H. & Pick, A. & Pranovich, M., 2011. "Optimal Forecasts in the Presence of Structural Breaks (Updated 14 November 2011)," Cambridge Working Papers in Economics 1163, Faculty of Economics, University of Cambridge.
    2. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    3. Kulish, Mariano & Pagan, Adrian, 2014. "Estimation and Solution of Models with Expectations and Structural Changes," Dynare Working Papers 34, CEPREMAP.
    4. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    5. 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.
    6. Keating, John W. & Valcarcel, Victor J., 2017. "What's so great about the Great Moderation?," Journal of Macroeconomics, Elsevier, vol. 51(C), pages 115-142.
    7. 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).
    8. Miguel Casares & Jesús Vázquez, 2018. "The Swings Of U.S. Inflation And The Gibson Paradox," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 799-820, April.
    9. Refet S. Gürkaynak & Zeynep Kantur & M. Anil Tas & Seçil Yildirim, 2015. "Monetary Policy in Turkey after Central Bank Independence," CESifo Working Paper Series 5582, CESifo.
    10. 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.
    11. Bayar, Omer, 2018. "Weak instruments and estimated monetary policy rules," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 308-317.
    12. 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.
    13. Andrew C. Chang & Phillip Li, 2015. "Measurement Error in Macroeconomic Data and Economics Research: Data Revisions, Gross Domestic Product, and Gross Domestic Income," Finance and Economics Discussion Series 2015-102, Board of Governors of the Federal Reserve System (U.S.).
    14. In-Koo Cho & Kenneth Kasa, 2016. "Gresham’S Law Of Model Averaging," Discussion Papers dp16-06, Department of Economics, Simon Fraser University.
    15. Imane El Ouadghiri & Remzi Uctum, 2020. "Macroeconomic expectations and time varying heterogeneity: Evidence from individual survey data," Post-Print hal-03319091, HAL.
    16. Ayse Kabukcuoglu & Enrique Martínez-García, 2016. "What Helps Forecast U.S. Inflation?—Mind the Gap!," Koç University-TUSIAD Economic Research Forum Working Papers 1615, Koc University-TUSIAD Economic Research Forum.
    17. Paul Hubert & Harun Mirza, 2014. "Inflation expectation dynamics:the role of past, present and forward looking information," Documents de Travail de l'OFCE 2014-07, Observatoire Francais des Conjonctures Economiques (OFCE).
    18. 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.
    19. Paul Hubert & Harun Mirza, 2019. "The role of forward- and backward-looking information for inflation expectations formation," SciencePo Working papers Main hal-03403616, HAL.
    20. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    21. Jürgen Jerger & Oke Röhe, 2012. "Testing for Parameter Stability in DSGE Models. The Cases of France, Germany, Italy, and Spain," Working Papers 118, Bavarian Graduate Program in Economics (BGPE).
    22. Baele, L.T.M. & Bekaert, G.R.J. & Cho, S. & Inghelbrecht, K. & Moreno, A., 2015. "Macroeconomic regimes," Other publications TiSEM e92a1993-778e-4ce2-b603-6, Tilburg University, School of Economics and Management.
    23. Lieven Baele & et al., 2012. "Macroeconomic Regimes," Faculty Working Papers 03/12, School of Economics and Business Administration, University of Navarra.
    24. Aguirre, Idoia & Vázquez, Jesús, 2020. "Learning, parameter variability, and swings in US macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 66(C).
    25. Ana gomez-Loscos & M. Dolores Gadea (Universidad de Zaragoza) & Gabriel Perez-Quiros (Bank of Spain), 2015. "Great Moderation and Great Recession. From plain sailing to stormy seas?," EcoMod2015 8267, EcoMod.
    26. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2020. "Uncertainty and Monetary Policy during Extreme Events," Economics Working Papers 2020-11, Department of Economics and Business Economics, Aarhus University.
    27. Tommaso Ferraresi & Andrea Roventini & Willi Semmler, 2016. "Macroeconomic regimes, technological shocks and employment dynamics," Documents de Travail de l'OFCE 2016-19, Observatoire Francais des Conjonctures Economiques (OFCE).
    28. Erdenebat Bataa & Marwan Izzeldin & Denise Osborn, 2015. "Changes in the global oil market," Working Papers 75761696, Lancaster University Management School, Economics Department.
    29. Gulan, Adam, 2018. "Paradise lost? A brief history of DSGE macroeconomics," Bank of Finland Research Discussion Papers 22/2018, Bank of Finland.
    30. Martínez-García Enrique, 2018. "Modeling time-variation over the business cycle (1960–2017): an international perspective," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-25, December.
    31. Yuelin Liu & James Morley, 2013. "Structural Evolution of the Postwar U.S. Economy," Discussion Papers 2013-15A, School of Economics, The University of New South Wales.
    32. Alessandro Casini, 2018. "Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework," Papers 1803.10883, arXiv.org, revised Dec 2018.
    33. De Lipsis Vincenzo, 2021. "Dating Structural Changes in UK Monetary Policy," The B.E. Journal of Macroeconomics, De Gruyter, vol. 21(2), pages 509-539, June.
    34. John W. Keating & Victor J. Valcarcel, 2012. "What's so Great about the Great Moderation? A Multi-Country Investigation of Time-Varying Volatilities of Output Growth and Inflation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201204, University of Kansas, Department of Economics.
    35. Jerger, Jürgen & Röhe, Oke, 2009. "Testing for Parameter Stability in DSGE Models. The Cases of France, Germany and Spain," University of Regensburg Working Papers in Business, Economics and Management Information Systems 453, University of Regensburg, Department of Economics.
    36. Marcellino, Massimiliano & Galvão, Ana Beatriz, 2010. "Endogenous Monetary Policy Regimes and the Great Moderation," CEPR Discussion Papers 7827, C.E.P.R. Discussion Papers.
    37. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    38. Mwasi Paza Mboya & Philipp Sibbertsen, 2023. "Optimal forecasts in the presence of discrete structural breaks under long memory," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1889-1908, November.
    39. Harun Mirza & Lidia Storjohann, 2014. "Making Weak Instrument Sets Stronger: Factor‐Based Estimation of Inflation Dynamics and a Monetary Policy Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 643-664, June.
    40. M Hashem Pesaran & Ron P Smith, 2017. "Tests of Policy Interventions in DSGE Models," BCAM Working Papers 1706, Birkbeck Centre for Applied Macroeconomics.
    41. Andrew C. Chang & Phillip Li, 2015. "Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals Say \"Usually Not\"," Finance and Economics Discussion Series 2015-83, Board of Governors of the Federal Reserve System (U.S.).
    42. Givens, Gregory & Salemi, Michael, 2012. "Inferring monetary policy objectives with a partially observed state," MPRA Paper 39353, University Library of Munich, Germany.
    43. Yuelin Liu & James Morley, 2013. "Structural Evolution of the Postwar U.S. Economy," Discussion Papers 2013-15, School of Economics, The University of New South Wales.
    44. 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).
    45. Pesaran, M. Hashem & Pick, Andreas & Pranovich, Mikhail, 2013. "Optimal forecasts in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 134-152.
    46. Likai Chen & Ekaterina Smetanina & Wei Biao Wu, 2022. "Estimation of nonstationary nonparametric regression model with multiplicative structure [Income and wealth distribution in macroeconomics: A continuous-time approach]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 176-214.
    47. 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.
    48. Omer Bayar, 2022. "Reducing large datasets to improve the identification of estimated policy rules," Empirical Economics, Springer, vol. 63(1), pages 113-140, July.
    49. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.
    50. Castelnuovo, Efrem, 2013. "Monetary policy shocks and financial conditions: A Monte Carlo experiment," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 282-303.
    51. Francesca Marino, 2016. "The Italian productivity slowdown in a Real Business Cycle perspective," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 63(2), pages 171-193, June.

  21. Inoue, Atsushi & Rossi, Barbara, 2011. "Testing for weak identification in possibly nonlinear models," Journal of Econometrics, Elsevier, vol. 161(2), pages 246-261, April.
    See citations under working paper version above.
  22. Atsushi Inoue & Gary Solon, 2010. "Two-Sample Instrumental Variables Estimators," The Review of Economics and Statistics, MIT Press, vol. 92(3), pages 557-561, August.
    See citations under working paper version above.
  23. Atsushi Inoue & Lutz Kilian & Fatma Burcu Kiraz, 2009. "Do Actions Speak Louder Than Words? Household Expectations of Inflation Based on Micro Consumption Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(7), pages 1331-1363, October.
    See citations under working paper version above.
  24. Inoue, Atsushi, 2008. "Efficient estimation and inference in linear pseudo-panel data models," Journal of Econometrics, Elsevier, vol. 142(1), pages 449-466, January.

    Cited by:

    1. Emrehan Aktug & Tolga Umut Kuzubas & Orhan Torul, 2018. "Heterogeneity in Labor Income Profiles: Evidence from Turkey," Working Papers 2018/10, Bogazici University, Department of Economics.
    2. Nadja Dwenger & Viktor Steiner, 2014. "Financial leverage and corporate taxation: evidence from German corporate tax return data," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 21(1), pages 1-28, February.
    3. Gerard Ferrer-Esteban & Mauro Mediavilla, 2017. "The more educated, the more engaged? An analysis of social capital and education," Working Papers 2017/13, Institut d'Economia de Barcelona (IEB).
    4. Lavin, Felipe Vasquez & Bratti, Luna & Orrego, Sergio & Barrientos, Manuel, 2020. "Assessing the Use of Pseudo-panels to Estimate the Value of Statistical Life in Developing Countries," EfD Discussion Paper 20-20, Environment for Development, University of Gothenburg.
    5. Emrehan Aktug & Tolga Umut Kuzubas & Orhan Torul, 2017. "An Investigation of Labor Income Profiles in Turkey," Working Papers 2017/04, Bogazici University, Department of Economics.
    6. D'Amato, Alessio & Giaccherini, Matilde & Zoli, Mariangela, 2019. "The Role of Information Sources and Providers in Shaping Green Behaviors. Evidence from Europe," Ecological Economics, Elsevier, vol. 164(C), pages 1-1.
    7. Hai-Anh Dang & Peter Lanjouw, 2022. "Measuring Poverty Dynamics with Synthetic Panels Based on Repeated Cross-Sections," Working Papers 632, ECINEQ, Society for the Study of Economic Inequality.
    8. Guarini, Giulio & Laureti, Tiziana & Garofalo, Giuseppe, 2018. "Territorial and individual educational inequality: A Capability Approach analysis for Italy," Economic Modelling, Elsevier, vol. 71(C), pages 247-262.
    9. Beatriz Muriel & Horacio Vera, 2015. "The Effects of Economic Growth on Earnings in Bolivia," Development Research Working Paper Series 08/2015, Institute for Advanced Development Studies.
    10. Morley K. Gunderson & Byron Y. Lee & Hui Wang, 2024. "Worker Congresses in China: Do they matter?," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 63(1), pages 43-58, January.
    11. Aart Kraay & Roy Weide, 2022. "Measuring intragenerational mobility using aggregate data," Journal of Economic Growth, Springer, vol. 27(2), pages 273-314, June.
    12. Dang,Hai-Anh H. & Lanjouw,Peter F., 2013. "Measuring poverty dynamics with synthetic panels based on cross-sections," Policy Research Working Paper Series 6504, The World Bank.
    13. Artūras Juodis, 2018. "Pseudo Panel Data Models With Cohort Interactive Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 47-61, January.

  25. Inoue, Atsushi & Kilian, Lutz, 2008. "How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 511-522, June.

    Cited by:

    1. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    2. KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," LIDAM Discussion Papers CORE 2011021, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. 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.
    4. David Rapach & Jack Strauss, 2010. "Bagging or Combining (or Both)? An Analysis Based on Forecasting U.S. Employment Growth," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 511-533.
    5. Gogas, Periklis & Papadimitriou, Theophilos & Plakandaras, Vasilios & Gupta, Rangan, 2019. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," DUTH Research Papers in Economics 3-2016, Democritus University of Thrace, Department of Economics.
    6. Shawn Ni & Antonello Loddo & Dongchu Sun, 2009. "Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search," Working Papers 0911, Department of Economics, University of Missouri.
    7. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    8. Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
    9. Granziera, Eleonora & Sekhposyan, Tatevik, 2019. "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
    10. Krüger Fabian & Pohlmeier Winfried & Mokinski Frieder, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 63-81, February.
    11. 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.
    12. Apergis Nicholas, 2021. "Forecasting US overseas travelling with univariate and multivariate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 963-976, September.
    13. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    14. Michael McAleer & Marcelo Cunha Medeiros, 2010. "Forecasting Realized Volatility with Linear and Nonlinear Models," Textos para discussão 568, Department of Economics PUC-Rio (Brazil).
    15. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
    16. 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.
    17. Ádám Csápai, 0000. "Macroeconomic Forecasting Using Machine Learning: A Case of Slovakia," Proceedings of Economics and Finance Conferences 14115967, International Institute of Social and Economic Sciences.
    18. Alessandro Giovannelli & Tommaso Proietti, 2015. "On the Selection of Common Factors for Macroeconomic Forecasting," CEIS Research Paper 332, Tor Vergata University, CEIS, revised 12 Mar 2015.
    19. Andreas Karatahansopoulos & Georgios Sermpinis & Jason Laws & Christian Dunis, 2014. "Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 596-610, December.
    20. Daniel Gianola & Kent A Weigel & Nicole Krämer & Alessandra Stella & Chris-Carolin Schön, 2014. "Enhancing Genome-Enabled Prediction by Bagging Genomic BLUP," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-18, April.
    21. Maehashi, Kohei & Shintani, Mototsugu, 2020. "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    22. 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.
    23. Eric Hillebrand & Tae-Hwy Lee & Marcelo C. Medeiros, 2012. "Let's Do It Again: Bagging Equity Premium Predictors," CREATES Research Papers 2012-41, Department of Economics and Business Economics, Aarhus University.
    24. 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.
    25. 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.
    26. Giacomini, Raffaella & Ragusa, Giuseppe, 2011. "Incorporating theoretical restrictions into forecasting by projection methods," CEPR Discussion Papers 8604, C.E.P.R. Discussion Papers.
    27. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," CEPR Discussion Papers 18298, C.E.P.R. Discussion Papers.
    28. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
    29. 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.
    30. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    31. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    32. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    33. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    34. Yuyi Zhang & Ruimin Ma & Jing Liu & Xiuxiu Liu & Ovanes Petrosian & Kirill Krinkin, 2021. "Comparison and Explanation of Forecasting Algorithms for Energy Time Series," Mathematics, MDPI, vol. 9(21), pages 1-12, November.
    35. Rapach, David E. & Strauss, Jack K., 2012. "Forecasting US state-level employment growth: An amalgamation approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 315-327.
    36. 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.
    37. Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2016. "Forecasting macroeconomic variables in data-rich environments," Economics Letters, Elsevier, vol. 138(C), pages 50-52.
    38. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
    39. Christina Anderl & Guglielmo Maria Caporale, 2023. "Forecasting inflation with a zero lower bound or negative interest rates: Evidence from point and density forecasts," Manchester School, University of Manchester, vol. 91(3), pages 171-232, June.
    40. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
    41. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    42. Cheng, Xu & Hansen, Bruce E., 2015. "Forecasting with factor-augmented regression: A frequentist model averaging approach," Journal of Econometrics, Elsevier, vol. 186(2), pages 280-293.
    43. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    44. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    45. Lyu, Zhichong & Ma, Feng & Zhang, Jixiang, 2023. "Oil futures volatility prediction: Bagging or combination?," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 457-467.
    46. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    47. Kadir Özen & Dilem Yıldırım, 2021. "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers 2101, ERC - Economic Research Center, Middle East Technical University, revised Apr 2021.
    48. 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.
    49. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    50. Gang Cheng & Sicong Wang & Yuhong Yang, 2015. "Forecast Combination under Heavy-Tailed Errors," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    51. 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.
    52. Ribeiro, Pinho J., 2017. "Selecting exchange rate fundamentals by bootstrap," International Journal of Forecasting, Elsevier, vol. 33(4), pages 894-914.
    53. Tae-Hwy Lee & Eric Hillebrand & Marcelo Medeiros, 2014. "Bagging Constrained Equity Premium Predictors," Working Papers 201421, University of California at Riverside, Department of Economics, revised Feb 2013.
    54. Luca Brugnolini & Giuseppe Ragusa, 2022. "Euro Area Deflationary Pressure Index," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 883-900, October.
    55. Sebastiano Manzan, 2015. "Forecasting the Distribution of Economic Variables in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 144-164, January.
    56. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," CREATES Research Papers 2012-18, Department of Economics and Business Economics, Aarhus University.
    57. Tom Boot & Didier Nibbering, 2016. "Forecasting Using Random Subspace Methods," Tinbergen Institute Discussion Papers 16-073/III, Tinbergen Institute, revised 11 Aug 2017.
    58. 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.
    59. Rangan Gupta & Mampho P. Modise & Josine Uwilingiye, 2011. "Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors," Working Papers 201122, University of Pretoria, Department of Economics.
    60. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
    61. Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
    62. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Georgios Sermpinis & Charalampos Stasinakis & Konstantinos Theofilatos & Andreas Karathanasopoul, 2014. "Inflation and Unemployment Forecasting with Genetic Support Vector Regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 471-487, September.
    63. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    64. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.
    65. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
    66. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
    67. Pijush Kanti Das & Prabir Kumar Das, 2024. "Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(2), pages 493-517, June.
    68. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    69. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    70. Luo, Jiawen & Klein, Tony & Walther, Thomas & Ji, Qiang, 2021. "Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning," QBS Working Paper Series 2021/04, Queen's University Belfast, Queen's Business School.
    71. Francesco Audrino & Marcelo C. Medeiros, 2011. "Modeling and forecasting short‐term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 999-1022, September.
    72. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
    73. Samuels, Jon D. & Sekkel, Rodrigo M., 2017. "Model Confidence Sets and forecast combination," International Journal of Forecasting, Elsevier, vol. 33(1), pages 48-60.
    74. 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.
    75. Gargano, Antonio & Timmermann, Allan, 2014. "Forecasting commodity price indexes using macroeconomic and financial predictors," International Journal of Forecasting, Elsevier, vol. 30(3), pages 825-843.
    76. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    77. Ivașcu Codruț, 2023. "Can Machine Learning Models Predict Inflation?," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1748-1756, July.
    78. Jon D. Samuels & Rodrigo Sekkel, 2013. "Forecasting with Many Models: Model Confidence Sets and Forecast Combination," Staff Working Papers 13-11, Bank of Canada.
    79. 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.
    80. Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
    81. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
    82. In Choi & Seong Jin Hwang, 2012. "Forecasting Korean inflation," Working Papers 1202, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    83. Dufays, A. & Rombouts, V., 2015. "Sparse Change-Point Time Series Models," LIDAM Discussion Papers CORE 2015032, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    84. Wang, Lu & Wu, Rui & Ma, WeiChun & Xu, Weiju, 2023. "Examining the volatility of soybean market in the MIDAS framework: The importance of bagging-based weather information," International Review of Financial Analysis, Elsevier, vol. 89(C).
    85. 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.
    86. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    87. Hongwei Zhang & Qiang He & Ben Jacobsen & Fuwei Jiang, 2020. "Forecasting stock returns with model uncertainty and parameter instability," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 629-644, August.
    88. Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.
    89. Gilberto Boaretto & Marcelo C. Medeiros, 2023. "Forecasting inflation using disaggregates and machine learning," Papers 2308.11173, arXiv.org.
    90. Barrow, Devon K. & Crone, Sven F., 2016. "Cross-validation aggregation for combining autoregressive neural network forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1120-1137.
    91. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    92. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    93. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    94. Zhu, Yinchu & Timmermann, Allan, 2022. "Conditional rotation between forecasting models," Journal of Econometrics, Elsevier, vol. 231(2), pages 329-347.
    95. 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.
    96. Dbouk, Wassim & Jamali, Ibrahim, 2018. "Predicting daily oil prices: Linear and non-linear models," Research in International Business and Finance, Elsevier, vol. 46(C), pages 149-165.
    97. Timmermann, Allan & Zhu, Yinchu, 2021. "Conditional Rotation Between Forecasting Models," CEPR Discussion Papers 15917, C.E.P.R. Discussion Papers.
    98. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2016. "Can commodity returns forecast Canadian sector stock returns?," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 172-188.

  26. Atsushi Inoue & Barbara Rossi, 2008. "Monitoring and Forecasting Currency Crises," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2-3), pages 523-534, March.
    See citations under working paper version above.
  27. Hall, Alastair R. & Inoue, Atsushi & Jana, Kalidas & Shin, Changmock, 2007. "Information in generalized method of moments estimation and entropy-based moment selection," Journal of Econometrics, Elsevier, vol. 138(2), pages 488-512, June.

    Cited by:

    1. Tae-Hwy Lee & Tao Wang, 2023. "Estimation and Testing of Forecast Rationality with Many Moments," Working Papers 202307, University of California at Riverside, Department of Economics.
    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. Cizek, P. & Aquaro, M., 2015. "Robust Estimation and Moment Selection in Dynamic Fixed-effects Panel Data Models," Other publications TiSEM 39d0f613-007f-4d21-b1e2-b, Tilburg University, School of Economics and Management.
    4. Doko Tchatoka, Firmin, 2010. "Subset hypotheses testing and instrument exclusion in the linear IV regression," MPRA Paper 29611, University Library of Munich, Germany, revised 02 Feb 2012.
    5. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    6. Aman Ullah & Huansha Wang, 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging," Econometrics, MDPI, vol. 1(2), pages 1-23, September.
    7. Lutz Kilian & Simone Manganelli, 2008. "The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1103-1129, September.
    8. Masahiko Shibamoto, 2016. "Empirical Assessment of the Impact of Monetary Policy Communication on the Financial Market," Discussion Paper Series DP2016-19, Research Institute for Economics & Business Administration, Kobe University.
    9. Carlos Medel, 2015. "Inflation Dynamics and the Hybrid Neo Keynesian Phillips Curve: The Case of Chile," Working Papers Central Bank of Chile 769, Central Bank of Chile.
    10. Prosper Donovon & Alastair R. Hall, 2015. "GMM and Indirect Inference: An appraisal of their connections and new results on their properties under second order identification," Economics Discussion Paper Series 1505, Economics, The University of Manchester.
    11. Matteo Barigozzi & Roxana Halbleib & David Veredas, 2012. "Which model to match?," Working Papers 1229, Banco de España.
    12. Hall, Alastair & Inoue, Atsushi & Nason M, James & Rossi, Barbara, 2007. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Working Papers 07-04, Duke University, Department of Economics.
    13. Caner, Mehmet & Fan, Qingliang, 2015. "Hybrid generalized empirical likelihood estimators: Instrument selection with adaptive lasso," Journal of Econometrics, Elsevier, vol. 187(1), pages 256-274.
    14. Poghosyan, K. & Boldea, O., 2011. "Structural versus Matching Estimation : Transmission Mechanisms in Armenia," Discussion Paper 2011-104, Tilburg University, Center for Economic Research.
    15. Jondeau, Eric & Le Bihan, Hervé, 2008. "Examining bias in estimators of linear rational expectations models under misspecification," Journal of Econometrics, Elsevier, vol. 143(2), pages 375-395, April.
    16. Yiying Cheng & Yaozhong Hu & Hongwei Long, 2020. "Generalized moment estimators for $$\alpha $$α-stable Ornstein–Uhlenbeck motions from discrete observations," Statistical Inference for Stochastic Processes, Springer, vol. 23(1), pages 53-81, April.
    17. Yanli Ma & Jieyu Zhu & Gaofeng Gu & Ke Chen, 2020. "Freight Transportation and Economic Growth for Zones: Sustainability and Development Strategy in China," Sustainability, MDPI, vol. 12(24), pages 1-17, December.
    18. Scheufele, Rolf, 2008. "Evaluating the German (New Keynesian) Phillips Curve," IWH Discussion Papers 10/2008, Halle Institute for Economic Research (IWH).
    19. Chen, Weihao & Cizek, Pavel, 2023. "Bias-Corrected Instrumental Variable Estimation in Linear Dynamic Panel Data Models," Discussion Paper 2023-028, Tilburg University, Center for Economic Research.
    20. Martyn Andrews & Obbey Elamin & Alastair R. Hall & Kostas Kyriakoulis & Matthew Sutton, 2017. "Inference in the presence of redundant moment conditions and the impact of government health expenditure on health outcomes in England," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 23-41, March.
    21. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    22. Eryuruk, Gunce & Hall, Alastair R. & Jana, Kalidas, 2009. "Contemporaneous and long run canonical correlations in the linear IV model: Implications for instrument selection," Economics Letters, Elsevier, vol. 105(1), pages 83-85, October.
    23. De Lipsis, Vincenzo, 2021. "Is time preference different across incomes and countries?," Economics Letters, Elsevier, vol. 201(C).
    24. Cizek, P. & Aquaro, M., 2015. "Robust Estimation and Moment Selection in Dynamic Fixed-effects Panel Data Models," Discussion Paper 2015-002, Tilburg University, Center for Economic Research.
    25. Prosper Dovonon & Firmin Doko Tchatoka & Michael Aguessy, 2019. "Relevant moment selection under mixed identification strength," School of Economics and Public Policy Working Papers 2019-04, University of Adelaide, School of Economics and Public Policy.
    26. Rolando Einar Paz Rodriguez, 2019. "La función de emparejamiento agregada del mercado laboral chileno," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 34(1), pages 85-110, April.
    27. Tobias Rühl, 2015. "Taylor rules revisited: ECB and Bundesbank in comparison," Empirical Economics, Springer, vol. 48(3), pages 951-967, May.
    28. Karamysheva, Madina & Skrobotov, Anton, 2022. "Do we reject restrictions identifying fiscal shocks? identification based on non-Gaussian innovations," Journal of Economic Dynamics and Control, Elsevier, vol. 138(C).
    29. Chen, Weihao & Cizek, Pavel, 2023. "Bias-Corrected Instrumental Variable Estimation in Linear Dynamic Panel Data Models," Other publications TiSEM 9bf2c16c-522f-4223-8037-c, Tilburg University, School of Economics and Management.
    30. Poghosyan, K. & Boldea, O., 2011. "Structural versus Matching Estimation : Transmission Mechanisms in Armenia," Other publications TiSEM cbb75e20-8475-4f79-ba65-d, Tilburg University, School of Economics and Management.
    31. Jinyuan Chang & Zhentao Shi & Jia Zhang, 2021. "Culling the herd of moments with penalized empirical likelihood," Papers 2108.03382, arXiv.org, revised May 2022.
    32. Bolko, Anine E. & Christensen, Kim & Pakkanen, Mikko S. & Veliyev, Bezirgen, 2023. "A GMM approach to estimate the roughness of stochastic volatility," Journal of Econometrics, Elsevier, vol. 235(2), pages 745-778.
    33. Lina Zhang & David T. Frazier & Don S. Poskitt & Xueyan Zhao, 2021. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 21/21, Monash University, Department of Econometrics and Business Statistics.
    34. Okui, Ryo, 2009. "The optimal choice of moments in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 151(1), pages 1-16, July.
    35. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.
    36. Meijer, Erik & Spierdijk, Laura & Wansbeek, Tom, 2017. "Consistent estimation of linear panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 200(2), pages 169-180.
    37. Martins, Luis F. & Gabriel, Vasco J., 2014. "Linear instrumental variables model averaging estimation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 709-724.
    38. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    39. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    40. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
    41. Eryuruk, Gunce & Hall, Alastair R. & Jana, Kalidas, 2009. "A comparative study of three data-based methods of instrument selection," Economics Letters, Elsevier, vol. 105(3), pages 280-283, December.
    42. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.

  28. Inoue, Atsushi & Solon, Gary, 2006. "A Portmanteau Test For Serially Correlated Errors In Fixed Effects Models," Econometric Theory, Cambridge University Press, vol. 22(5), pages 835-851, October.
    See citations under working paper version above.
  29. Atsushi Inoue, 2006. "A bootstrap approach to moment selection," Econometrics Journal, Royal Economic Society, vol. 9(1), pages 48-75, March.

    Cited by:

    1. Tae-Hwy Lee & Tao Wang, 2023. "Estimation and Testing of Forecast Rationality with Many Moments," Working Papers 202307, University of California at Riverside, Department of Economics.
    2. Canay, Ivan A., 2010. "Simultaneous selection and weighting of moments in GMM using a trapezoidal kernel," Journal of Econometrics, Elsevier, vol. 156(2), pages 284-303, June.
    3. Marcelo J. Moreira & Jack R. Porter & Gustavo A. Suarez, 2004. "Bootstrap and Higher-Order Expansion Validity When Instruments May Be Weak," Harvard Institute of Economic Research Working Papers 2048, Harvard - Institute of Economic Research.
    4. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    5. Moreira, Marcelo J. & Porter, Jack R. & Suarez, Gustavo A., 2009. "Bootstrap validity for the score test when instruments may be weak," Journal of Econometrics, Elsevier, vol. 149(1), pages 52-64, April.
    6. Kuersteiner, Guido M., 2012. "Kernel-weighted GMM estimators for linear time series models," Journal of Econometrics, Elsevier, vol. 170(2), pages 399-421.
    7. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  30. Inoue, Atsushi & Shintani, Mototsugu, 2006. "Bootstrapping GMM estimators for time series," Journal of Econometrics, Elsevier, vol. 133(2), pages 531-555, August.
    See citations under working paper version above.
  31. Inoue, Atsushi & Kilian, Lutz, 2006. "On the selection of forecasting models," Journal of Econometrics, Elsevier, vol. 130(2), pages 273-306, February.
    See citations under working paper version above.
  32. Atsushi Inoue & Tomislav Vukina, 2006. "Testing for the principal’s monopsony power in agency contracts," Empirical Economics, Springer, vol. 31(3), pages 717-734, September.

    Cited by:

    1. Timothy A. Wise & Sarah E. Trist, "undated". "Buyer Power in U.S. Hog Markets: A Critical Review of the Literature," GDAE Working Papers 10-04, GDAE, Tufts University.

  33. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    See citations under working paper version above.
  34. Inoue, Atsushi & Rossi, Barbara, 2005. "Recursive Predictability Tests for Real-Time Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 336-345, July.
    See citations under working paper version above.
  35. Hall, Alastair R. & Inoue, Atsushi & Peixe, Fernanda P.M., 2003. "Covariance Matrix Estimation And The Limiting Behavior Of The Overidentifying Restrictions Test In The Presence Of Neglected Structural Instability," Econometric Theory, Cambridge University Press, vol. 19(6), pages 962-983, December.

    Cited by:

    1. Shin-Kun Peng & Takatoshi Tabuchi, 2005. "Spatial Competition in Variety and Number of Stores," CIRJE F-Series CIRJE-F-360, CIRJE, Faculty of Economics, University of Tokyo.
    2. Wei-Ming Lee & Chung-Ming Kuan, 2006. "Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix," IEAS Working Paper : academic research 06-A009, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    3. Alastair R. Hall & Sanggohn Han & Otilia Boldea, 2009. "Inference regarding multiple structural changes in linear models with endogenous regressors," Centre for Growth and Business Cycle Research Discussion Paper Series 125, Economics, The University of Manchester.
    4. Daniel Smith, 2008. "Testing for structural breaks in GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(10), pages 845-862.
    5. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.

  36. Inoue, Atsushi & Kilian, Lutz, 2003. "The Continuity Of The Limit Distribution In The Parameter Of Interest Is Not Essential For The Validity Of The Bootstrap," Econometric Theory, Cambridge University Press, vol. 19(6), pages 944-961, December.

    Cited by:

    1. DUFOUR, Jean-Marie & JOUINI, Tarek, 2005. "Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing," Cahiers de recherche 16-2005, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    2. DUFOUR, Jean-Marie, 2005. "Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics," Cahiers de recherche 2005-03, Universite de Montreal, Departement de sciences economiques.
    3. Daniel Grabowski & Anna Staszewska-Bystrova & Peter Winker, 2020. "Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 5-32, March.
    4. George Kapetanios, 2004. "Testing for Exogeneity in Nonlinear Threshold Models," Working Papers 515, Queen Mary University of London, School of Economics and Finance.
    5. Dufour, Jean-Marie & Jouini, Tarek, 2006. "Finite-sample simulation-based inference in VAR models with application to Granger causality testing," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 229-254.
    6. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    7. 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.
    8. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.

  37. Hall, Alastair R. & Inoue, Atsushi, 2003. "The large sample behaviour of the generalized method of moments estimator in misspecified models," Journal of Econometrics, Elsevier, vol. 114(2), pages 361-394, June.
    See citations under working paper version above.
  38. Atsushi Inoue & Lutz Kilian, 2002. "Bootstrapping Smooth Functions of Slope Parameters and Innovation Variances in VAR (∞) Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(2), pages 309-332, May.

    Cited by:

    1. Kyritsis, Evangelos & Serletis, Apostolos, 2017. "The Zero Lower Bound and Market Spillovers: Evidence from the G7 and Norway," Discussion Papers 2017/7, Norwegian School of Economics, Department of Business and Management Science.
    2. 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.
    3. Trenkler, Carsten & Weber, Enzo, 2012. "Identifying the Shocks behind Business Cycle Asynchrony in Euroland," University of Regensburg Working Papers in Business, Economics and Management Information Systems 466, University of Regensburg, Department of Economics.
    4. DUFOUR, Jean-Marie & JOUINI, Tarek, 2005. "Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing," Cahiers de recherche 16-2005, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    5. Jeremy Berkowitz & Ionel Birgean & Lutz Kilian, 1999. "On the finite-sample accuracy of nonparametric resampling algorithms for economic time series," Finance and Economics Discussion Series 1999-04, Board of Governors of the Federal Reserve System (U.S.).
    6. Pablo Guerron-Quintana & Atsushi Inoue & Lutz Kilian, 2016. "Impulse Response Matching Estimators for DSGE Models," CESifo Working Paper Series 5730, CESifo.
    7. 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.
    8. Francis X. Diebold & Lutz Kilian, "undated". "Measuring Predictability: Theory and Macroeconomic Applications," CARESS Working Papres 97-19, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    9. Phillips, Kerk L. & Spencer, David E., 2010. "Bootstrapping Structural VARs: Avoiding a Potential Bias in Confidence Intervals for Impulse Response Functions," MPRA Paper 23503, University Library of Munich, Germany.
    10. Karras, Georgios & Lee, Jin Man & Stokes, Houston, 2005. "Sources of exchange-rate volatility: Impulses or propagation?," International Review of Economics & Finance, Elsevier, vol. 14(2), pages 213-226.
    11. 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.
    12. Helmut Luetkepohl, 2011. "Vector Autoregressive Models," Economics Working Papers ECO2011/30, European University Institute.
    13. Cruz, Christopher John, 2022. "Reduced macroeconomic volatility after adoption of inflation targeting: Impulses or propagation?," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 759-770.
    14. Lamb, John D. & Tee, Kai-Hong, 2012. "Resampling DEA estimates of investment fund performance," European Journal of Operational Research, Elsevier, vol. 223(3), pages 834-841.
    15. Mikkel Plagborg‐Møller & Christian K. Wolf, 2021. "Local Projections and VARs Estimate the Same Impulse Responses," Econometrica, Econometric Society, vol. 89(2), pages 955-980, March.
    16. Enrique Martínez García, 2020. "A Matter of Perspective: Mapping Linear Rational Expectations Models into Finite-Order VAR Form," Globalization Institute Working Papers 389, Federal Reserve Bank of Dallas.
    17. Enrique Martínez García, 2016. "Finite-Order VAR Representation of Linear Rational Expectations Models: With Some Lessons for Monetary Policy," Globalization Institute Working Papers 285, Federal Reserve Bank of Dallas.
    18. Chaudourne, Jeremy & Fève, Patrick & Guay, Alain, 2012. "Understanding the Effect of Technology Shocks in SVARs with Long-Run Restrictions," IDEI Working Papers 738, Institut d'Économie Industrielle (IDEI), Toulouse.
    19. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    20. Brüggemann, Ralf & Jentsch, Carsten & Trenkler, Carsten, 2014. "Inference in VARs with Conditional Heteroskedasticity of Unknown Form," Working Papers 14-21, University of Mannheim, Department of Economics.
    21. Luca Sala, 2004. "The Fiscal Theory of the Price Level: Identifying Restrictions and Empirical Evidence," Working Papers 257, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    22. Karras, Georgios & Lee, Jin Man & Stokes, Houston, 2006. "Why are postwar cycles smoother? Impulses or propagation?," Journal of Economics and Business, Elsevier, vol. 58(5-6), pages 392-406.
    23. Dufour, Jean-Marie & Jouini, Tarek, 2006. "Finite-sample simulation-based inference in VAR models with application to Granger causality testing," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 229-254.
    24. Pesavento, Elena & Rossi, Barbara, 2007. "Impulse response confidence intervals for persistent data: What have we learned?," Journal of Economic Dynamics and Control, Elsevier, vol. 31(7), pages 2398-2412, July.
    25. Roberto Duncan & Enrique Martínez García, 2015. "Forecasting local inflation in Open Economies: What Can a NOEM Model Do?," Globalization Institute Working Papers 235, Federal Reserve Bank of Dallas, revised 21 Dec 2022.
    26. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    27. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.
    28. Andrés Alonso & Daniel Peña & Juan Romo, 2006. "Introducing model uncertainty by moving blocks bootstrap," Statistical Papers, Springer, vol. 47(2), pages 167-179, March.
    29. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    30. Ziadat, Salem Adel & McMillan, David G. & Herbst, Patrick, 2022. "Oil shocks and equity returns during bull and bear markets: The case of oil importing and exporting nations," Resources Policy, Elsevier, vol. 75(C).

  39. Inoue, Atsushi, 2002. "Identifying the sign of the slope of a monotonic function via OLS," Economics Letters, Elsevier, vol. 75(3), pages 419-424, May.

    Cited by:

    1. Vukina, Tomislav & Zheng, Xiaoyong & Marra, Michele & Levy, Armando, 2008. "Do farmers value the environment? Evidence from a conservation reserve program auction," International Journal of Industrial Organization, Elsevier, vol. 26(6), pages 1323-1332, November.

  40. Jinyong Hahn & Atsushi Inoue, 2002. "A Monte Carlo Comparison Of Various Asymptotic Approximations To The Distribution Of Instrumental Variables Estimators," Econometric Reviews, Taylor & Francis Journals, vol. 21(3), pages 309-336.

    Cited by:

    1. Paul A. Bekker & Jan van der Ploeg, 2000. "Instrumental Variable Estimation Based on Grouped Data," Econometric Society World Congress 2000 Contributed Papers 1862, Econometric Society.
    2. Charles Nelson & Richard Startz & Eric Zivot, 2000. "Improved Inference for the Instrumental Variables Estimator," Econometric Society World Congress 2000 Contributed Papers 1600, Econometric Society.
    3. Jerry A. Hausman & Whitney K. Newey & Tiemen Woutersen & John C. Chao & Norman R. Swanson, 2012. "Instrumental variable estimation with heteroskedasticity and many instruments," Quantitative Economics, Econometric Society, vol. 3(2), pages 211-255, July.
    4. John Chao & Norman R. Swanson, 2003. "Alternative Approximations of the Bias and MSE of the IV Estimator under Weak Identification with an Application to Bias Correction," Cowles Foundation Discussion Papers 1418, Cowles Foundation for Research in Economics, Yale University.
    5. Kazuhiko Hayakawa, 2006. "Efficient GMM Estimation of Dynamic Panel Data Models Where Large Heterogeneity May Be Present," Hi-Stat Discussion Paper Series d05-130, Institute of Economic Research, Hitotsubashi University.
    6. Bekker, Paul A. & Crudu, Federico, 2012. "Symmetric Jackknife Instrumental Variable Estimation," MPRA Paper 37853, University Library of Munich, Germany.
    7. John Chao & Norman Swanson, 2004. "Consistent Estimation with a Large Number of Weak Instruments," Departmental Working Papers 200421, Rutgers University, Department of Economics.
    8. Christian Hansen & Jerry Hausman & Whitney K. Newey, 2006. "Estimation with many instrumental variables," CeMMAP working papers CWP19/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Kiviet, Jan F. & Niemczyk, Jerzy, 2007. "The asymptotic and finite sample distributions of OLS and simple IV in simultaneous equations," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3296-3318, April.
    10. Murray Michael P., 2017. "Linear Model IV Estimation When Instruments Are Many or Weak," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-22, January.
    11. Michael Christl & Monika Köppl‐Turyna & Dénes Kucsera, 2018. "Revisiting the Employment Effects of Minimum Wages in Europe," German Economic Review, Verein für Socialpolitik, vol. 19(4), pages 426-465, November.
    12. Kweh, Qian Long & Tebourbi, Imen & Lo, Huai-Chun & Huang, Cheng-Tsu, 2022. "CEO compensation and firm performance: Evidence from financially constrained firms," Research in International Business and Finance, Elsevier, vol. 61(C).
    13. Carrasco, Marine & Tchuente, Guy, 2015. "Regularized LIML for many instruments," Journal of Econometrics, Elsevier, vol. 186(2), pages 427-442.
    14. Hall, Alastair R. & Han, Sanggohn & Boldea, Otilia, 2008. "Inference regarding multiple structural changes in linear models estimated via two stage least squares," MPRA Paper 9251, University Library of Munich, Germany, revised 20 Jun 2008.
    15. Bekker, Paul A. & Crudu, Federico, 2015. "Jackknife instrumental variable estimation with heteroskedasticity," Journal of Econometrics, Elsevier, vol. 185(2), pages 332-342.
    16. Park, Albert & Brandt, Loren & Giles, John, 2003. "Competition under credit rationing: theory and evidence from rural China," Journal of Development Economics, Elsevier, vol. 71(2), pages 463-495, August.
    17. Jan F. Kiviet & Jerzy Niemczyk, 2014. "On the Limiting and Empirical Distributions of IV Estimators When Some of the Instruments are Actually Endogenous," Advances in Econometrics, in: Essays in Honor of Peter C. B. Phillips, volume 33, pages 425-490, Emerald Group Publishing Limited.
    18. Neil Kellard & Denise Osborn & Jerry Coakley & Alastair R. Hall & Denise R. Osborn & Nikolaos Sakkas, 2015. "Structural Break Inference Using Information Criteria in Models Estimated by Two-Stage Least Squares," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 741-762, September.
    19. Hall, Alastair R. & Han, Sanggohn & Boldea, Otilia, 2008. "Asymptotic Distribution Theory for Break Point Estimators in Models Estimated via 2SLS," MPRA Paper 9472, University Library of Munich, Germany.
    20. Alastair R. Hall & Sanggohn Han & Otilia Boldea, 2009. "Inference regarding multiple structural changes in linear models with endogenous regressors," Centre for Growth and Business Cycle Research Discussion Paper Series 125, Economics, The University of Manchester.
    21. Otilia Boldea & Alastair Hall & Sanggohn Han, 2010. "Uncertainty, Entrepreneurship and the Organisation of Corruption," Centre for Growth and Business Cycle Research Discussion Paper Series 134, Economics, The University of Manchester.
    22. Joura, Essam & Xiao, Qin & Ullah, Subhan, 2021. "The impact of Say-on-Pay votes on firms' strategic policies: Insights from the Anglo-Saxon economy," International Review of Financial Analysis, Elsevier, vol. 73(C).
    23. Boldea, O. & Hall, A.R. & Han, S., 2012. "Asymptotic distribution theory for break point estimators in models estimated via 2SLS," Other publications TiSEM 2e2fbb75-c4ff-4279-8243-e, Tilburg University, School of Economics and Management.
    24. Abutaliev, Albert & Anatolyev, Stanislav, 2013. "Asymptotic variance under many instruments: Numerical computations," Economics Letters, Elsevier, vol. 118(2), pages 272-274.
    25. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.

  41. Atsushi Inoue & Lutz Kilian, 2002. "Bootstrapping Autoregressive Processes with Possible Unit Roots," Econometrica, Econometric Society, vol. 70(1), pages 377-391, January.
    See citations under working paper version above.
  42. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    See citations under working paper version above.
  43. Christoffersen, Peter & Hahn, Jinyong & Inoue, Atsushi, 2001. "Testing and comparing Value-at-Risk measures," Journal of Empirical Finance, Elsevier, vol. 8(3), pages 325-342, July.
    See citations under working paper version above.
  44. Inoue, Atsushi, 2001. "Testing For Distributional Change In Time Series," Econometric Theory, Cambridge University Press, vol. 17(1), pages 156-187, February.

    Cited by:

    1. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.
    2. Bücher, Axel & Ruppert, Martin, 2013. "Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 208-229.
    3. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 290-318.
    4. Tatevik Sekhposyan & Barbara Rossi, 2015. "Alternative Tests for Correct Specification of Conditional Predictive Densities," Working Papers 758, Barcelona School of Economics.
    5. Chen, Bin & Hong, Yongmiao, 2012. "Testing For The Markov Property In Time Series," Econometric Theory, Cambridge University Press, vol. 28(1), pages 130-178, February.
    6. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    7. Qu, Zhongjun, 2008. "Testing for structural change in regression quantiles," Journal of Econometrics, Elsevier, vol. 146(1), pages 170-184, September.
    8. Manner, Hans & Rodríguez, Gabriel & Stöckler, Florian, 2024. "A changepoint analysis of exchange rate and commodity price risks for Latin American stock markets," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1385-1403.
    9. Bucchia, Béatrice & Wendler, Martin, 2017. "Change-point detection and bootstrap for Hilbert space valued random fields," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 344-368.
    10. Rossi, Barbara & Sekhposyan, Tatevik, 2013. "Conditional predictive density evaluation in the presence of instabilities," Journal of Econometrics, Elsevier, vol. 177(2), pages 199-212.
    11. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," CEPR Discussion Papers 18298, C.E.P.R. Discussion Papers.
    12. Valentina Corradi & Norman R. Swanson, 2003. "Bootstrap Specification Tests for Diffusion Processes," Departmental Working Papers 200321, Rutgers University, Department of Economics.
    13. Fabio Busetti, 2012. "On detecting end-of-sample instabilities," Temi di discussione (Economic working papers) 881, Bank of Italy, Economic Research and International Relations Area.
    14. Jonas Dovern & Geoff Kenny, 2020. "Anchoring Inflation Expectations in Unconventional Times: Micro Evidence for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 16(5), pages 309-347, October.
    15. Bücher, Axel & Kojadinovic, Ivan & Rohmer, Tom & Segers, Johan, 2014. "Detecting changes in cross-sectional dependence in multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 111-128.
    16. Corradi, V. & Swanson, N.R., 2000. "A Consistent Test for Nonlinear Out of Sample Predictive Accuracy," Discussion Papers 0012, University of Exeter, Department of Economics.
    17. Corradi, Valentina & Swanson, Norman R., 2004. "A test for the distributional comparison of simulated and historical data," Economics Letters, Elsevier, vol. 85(2), pages 185-193, November.
    18. Valentina Corradi & Norman R. Swanson, 2003. "The Effect of Data Transformation on Common Cycle, Cointegration and Unit Root Tests: Monte Carlo Results and a Simple Test," Departmental Working Papers 200322, Rutgers University, Department of Economics.
    19. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    20. Diep Duong & Norman Swanson, 2013. "Density and Conditional Distribution Based Specification Analysis," Departmental Working Papers 201312, Rutgers University, Department of Economics.
    21. Holmes, Mark & Kojadinovic, Ivan & Quessy, Jean-François, 2013. "Nonparametric tests for change-point detection à la Gombay and Horváth," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 16-32.
    22. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    23. Axel Bücher, 2015. "A Note on Weak Convergence of the Sequential Multivariate Empirical Process Under Strong Mixing," Journal of Theoretical Probability, Springer, vol. 28(3), pages 1028-1037, September.
    24. Su, Liangjun & Xiao, Zhijie, 2008. "Testing for parameter stability in quantile regression models," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2768-2775, November.
    25. Bucher, Axel, 2013. "A note on weak convergence of the sequential multivariate empirical process under strong mixing," LIDAM Discussion Papers ISBA 2013028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    26. Wied, Dominik & Dehling, Herold & van Kampen, Maarten & Vogel, Daniel, 2014. "A fluctuation test for constant Spearman’s rho with nuisance-free limit distribution," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 723-736.
    27. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    28. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    29. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2012. "A new fluctuation test for constant variances with applications to finance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1111-1127, November.
    30. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics.
    31. Jean-François Quessy, 2019. "Consistent nonparametric tests for detecting gradual changes in the marginals and the copula of multivariate time series," Statistical Papers, Springer, vol. 60(3), pages 717-746, June.
    32. O‐Chia Chuang & Xiaojun Song & Abderrahim Taamouti, 2022. "Testing for Asymmetric Comovements," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 1153-1180, October.
    33. Leonie Selk & Natalie Neumeyer, 2013. "Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 770-788, December.
    34. Rohmer, Tom, 2016. "Some results on change-point detection in cross-sectional dependence of multivariate data with changes in marginal distributions," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 45-54.
    35. Ulrich Hounyo, 2014. "The wild tapered block bootstrap," CREATES Research Papers 2014-32, Department of Economics and Business Economics, Aarhus University.

  45. Inoue, Atsushi, 1999. "Tests of cointegrating rank with a trend-break," Journal of Econometrics, Elsevier, vol. 90(2), pages 215-237, June.

    Cited by:

    1. Kurozumi, Eiji & 黒住, 英司 & Arai, Yoichi & 荒井, 洋一, 2005. "Efficient Estimation and Inference in Cointegrating Regressions with Structural Change," Discussion Papers 2004-09, Graduate School of Economics, Hitotsubashi University.
    2. Chee Seng Cheong & Patrick J. Wilson & Ralf Zurbruegg, 2009. "An analysis of the long‐run impact of fixed income and equity market performance on Australian and UK securitised property markets," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 27(3), pages 259-276, April.
    3. Yoichi Arai & Eiji Kurozumi, 2005. "Testing for the Null Hypothesis of Cointegration with Structural Breaks," CIRJE F-Series CIRJE-F-319, CIRJE, Faculty of Economics, University of Tokyo.
    4. Perry, L. J. & Wilson, Patrick J., 2004. "Trends in work stoppages : a global perspective," ILO Working Papers 993742343402676, International Labour Organization.
    5. Antonio E. Noriega & Daniel Ventosa-Santaularia, 2012. "The effect of structural breaks on the Engle-Granger test for cointegration," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 27(1), pages 99-132.
    6. Søren Johansen & Rocco Mosconi & Bent Nielsen, 2000. "Cointegration analysis in the presence of structural breaks in the deterministic trend," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 216-249.
    7. Ozdemir, Zeynel Abidin & Cakan, Esin, 2010. "The persistence in real exchange rate: Evidence from East Asian countries," Economic Modelling, Elsevier, vol. 27(5), pages 891-895, September.
    8. Sébastien Morin, 2004. "Ruptures structurelles sur les marchés action et obligataire américains : preuve empirique à partir de la méthode de Saikkönen," Economie & Prévision, La Documentation Française, vol. 166(5), pages 87-98.
    9. Reza Anglingkusumo, 2005. "Stability of the Demand for Real Narrow Money in lndonesia," Tinbergen Institute Discussion Papers 05-051/4, Tinbergen Institute.
    10. Razvan Pascalau & Junsoo Lee & Saban Nazlioglu & Yan (Olivia) Lu, 2022. "Johansen‐type cointegration tests with a Fourier function," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 828-852, September.
    11. Khaled Chnaina & Farid Makhlouf, 2015. "Impact des Transferts de Fonds sur le Taux de Change Réel Effectif en Tunisie," African Development Review, African Development Bank, vol. 27(2), pages 145-160, June.
    12. Eriko Hoshino & Caleb Gardner & Sarah Jennings & Klaas Hartmann, 2015. "Examining the Long-Run Relationship between the Prices of Imported Abalone in Japan," Marine Resource Economics, University of Chicago Press, vol. 30(2), pages 179-192.
    13. Solarin, Sakiru Adebola & Ozturk, Ilhan, 2015. "On the causal dynamics between hydroelectricity consumption and economic growth in Latin America countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1857-1868.
    14. Patrick J. Wilson & Ralf Zurbruegg & Richard Gerlach, 2002. "Structural Breaks and Diversification: The Impact of the 1997 Asian Financial Crisis on the Integration of Asia Pacific Real Estate Markets," ERES eres2002_140, European Real Estate Society (ERES).
    15. Cheong, Chee Seng & Gerlach, Richard & Stevenson, Simon & Wilson, Patrick J. & Zurbruegg, Ralf, 2009. "Equity and fixed income markets as drivers of securitised real estate," Review of Financial Economics, Elsevier, vol. 18(2), pages 103-111, April.
    16. Patrick Wilson & Michael White & Neil Dunse & Chee Cheong & Ralf Zurbruegg, 2011. "Modelling Price Movements in Housing Micro Markets," Urban Studies, Urban Studies Journal Limited, vol. 48(9), pages 1853-1874, July.
    17. Gabriel, Vasco J. & Psaradakis, Zacharias & Sola, Martin, 2002. "A simple method of testing for cointegration subject to multiple regime changes," Economics Letters, Elsevier, vol. 76(2), pages 213-221, July.
    18. Salah A. Nusair & Naser I. Abumustafa, 2012. "Recursive Cointegration Analysis of Purchasing Power Parity: An Application to Asian Countries," The American Economist, Sage Publications, vol. 57(2), pages 196-209, November.
    19. Vasco J. Gabriel & Martin Sola & Zacharias Psaradakis, 2001. "A simple method for testing cointegration subject to regime changes," NIPE Working Papers 15/2001, NIPE - Universidade do Minho.
    20. Daiki Maki, 2013. "Detecting cointegration relationships under nonlinear models: Monte Carlo analysis and some applications," Empirical Economics, Springer, vol. 45(1), pages 605-625, August.
    21. Marco GALLEGATI, 2002. "Financial Constraints and the Balance Sheet Channel: a Re-Interpretation," Working Papers 161, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    22. Peter Reinhard Hansen, 2000. "Structural Changes in the Cointegrated Vector Autoregressive Model," Working Papers 2000-20, Brown University, Department of Economics.
    23. Patrick J. Wilson & Ralf Zurbruegg, 2008. "Big City Difference? Another Look at Factors Driving House Prices," Journal of Property Research, Taylor & Francis Journals, vol. 25(2), pages 157-177, November.
    24. Ana Norman-Lόpez & Sean Pascoe & Olivier Thébaud & Ingrid Putten & James Innes & Sarah Jennings & Alistair Hobday & Bridget Green & Eva Plaganyi, 2014. "Price integration in the Australian rock lobster industry: implications for management and climate change adaptation," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 58(1), pages 43-59, January.
    25. Harris, David & Leybourne, Stephen J. & Taylor, A.M. Robert, 2016. "Tests of the co-integration rank in VAR models in the presence of a possible break in trend at an unknown point," Journal of Econometrics, Elsevier, vol. 192(2), pages 451-467.
    26. Peter Reinhard Hansen, 2000. "Structural Breaks in the Cointegrated Vector Autoregressive Model," Econometric Society World Congress 2000 Contributed Papers 1240, Econometric Society.
    27. Hubrich, Kirstin & Lütkepohl, Helmut & Saikkonen, Pentti, 1998. "A review of systemscointegration tests," SFB 373 Discussion Papers 1998,101, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    28. Paulo M. M. Rodrigues & Philipp Sibbertsen & Michelle Voges, 2024. "The stability of government bond markets’ equilibrium and the interdependence of lending rates," Empirical Economics, Springer, vol. 67(6), pages 2503-2538, December.
    29. Takamitsu Kurita & Mototsugu Shintani, 2023. "Johansen Test with Fourier-Type Smooth Nonlinear Trends in Cointegrating Relations," CIRJE F-Series CIRJE-F-1216, CIRJE, Faculty of Economics, University of Tokyo.
    30. Trenkler, Carsten, 2002. "The effects of ignoring level shifts on systems cointegration tests," SFB 373 Discussion Papers 2002,68, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    31. Kose, Nezir & Emirmahmutoglu, Furkan & Aksoy, Sezgin, 2012. "The interest rate–inflation relationship under an inflation targeting regime: The case of Turkey," Journal of Asian Economics, Elsevier, vol. 23(4), pages 476-485.
    32. Helmut Luetkepohl & Pentti Saikkonen & Carsten Trenkler, 2000. "Comparison of Tests for the Cointegrating Rank of a VAR Process with a Structural Shift," Econometric Society World Congress 2000 Contributed Papers 0364, Econometric Society.
    33. Andrade, Philippe & Bruneau, Catherine & Gregoir, Stephane, 2005. "Testing for the cointegration rank when some cointegrating directions are changing," Journal of Econometrics, Elsevier, vol. 124(2), pages 269-310, February.
    34. Skrobotov, Anton, 2021. "Structural breaks in cointegration models: Multivariate case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 83-106.
    35. Rodrigues, Paulo M.M. & Sibbertsen, Philipp & Voges, Michelle, 2019. "Testing for breaks in the cointegrating relationship: On the stability of government bond markets' equilibrium," Hannover Economic Papers (HEP) dp-656, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    36. Víctor-Hugo Alcalá Ríos & Manuel Gómez Zaldívar & Daniel Ventosa-Santaulà ria, 2011. "Paradoja Feldstein-Horioka: el caso de México (1950-2007)," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 26(2), pages 293-313.
    37. Salah A. Nusair, 2008. "Purchasing Power Parity under Regime Shifts: An Application to Asian Countries," Asian Economic Journal, East Asian Economic Association, vol. 22(3), pages 241-266, September.
    38. Takamitsu Kurita & Bent Nielsen, 2019. "Partial Cointegrated Vector Autoregressive Models with Structural Breaks in Deterministic Terms," Econometrics, MDPI, vol. 7(4), pages 1-35, October.
    39. Perron, Pierre & Zhu, Xiaokang, 2005. "Structural breaks with deterministic and stochastic trends," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 65-119.
    40. Yoichi Arai & Eiji Kurozumi, 2005. "Testing for the Null Hypothesis of Cointegration with Structural Breaks (Subsequently published in "Econometric Reviews", Volume 26, Issue 6 November 2007, pages 705 - 739. )," CARF F-Series CARF-F-022, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    41. Zurbruegg, R. & Allsopp, L., 2004. "Purchasing power parity and the impact of the East Asian currency crisis," Journal of Asian Economics, Elsevier, vol. 15(4), pages 739-758, August.
    42. Kim Liow & Zhiwei Chen & Jingran Liu, 2011. "Multiple Regimes and Volatility Transmission in Securitized Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 42(3), pages 295-328, April.
    43. Sébastien Morin, 2004. "Ruptures structurelles sur les marchés action et obligataire américains : preuve empirique à partir de la méthode de Saikkönen," Économie et Prévision, Programme National Persée, vol. 166(5), pages 87-98.
    44. Patrick Wilson & Simon Stevenson & Ralf Zurbruegg, 2007. "Foreign Property Shocks and the Impact on Domestic Securitized Real Estate Markets: An Unobserved Components Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 34(3), pages 407-424, April.
    45. Domenico Sartore & Lucia Trevisan & Michele Trova & Francesca Volo, 2002. "US dollar/Euro exchange rate: a monthly econometric model for forecasting," The European Journal of Finance, Taylor & Francis Journals, vol. 8(4), pages 480-501.
    46. Chen, Cathy W.S. & Gerlach, Richard & Cheng, Nick Y.P. & Yang, Y.L., 2009. "The impact of structural breaks on the integration of the ASEAN-5 stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2654-2664.
    47. 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.
    48. 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.
    49. David E. Giles & Ryan T. Godwin, 2011. "Testing for Multivariate Cointegration in the Presence of Structural Breaks: p-Values and Critical Values," Econometrics Working Papers 1110, Department of Economics, University of Victoria.
    50. Takamitsu Kurita & B. Nielsen, 2018. "Partial cointegrated vector autoregressive models with structural breaks in deterministic terms," Economics Papers 2018-W03, Economics Group, Nuffield College, University of Oxford.

  46. Koehler, Anne & Diebold, Francis X. & Giogianni, Lorenzo & Inoue, Atsushi, 1996. "Software review," International Journal of Forecasting, Elsevier, vol. 12(2), pages 309-315, June.

    Cited by:

    1. Julie Tam & Heather Kirkham, 2000. "Automatic Fiscal Stabilisers: Implications for New Zealand," Treasury Working Paper Series 01/10, New Zealand Treasury, revised 2001.

  47. Yabushita Shiro & Inoue Atsushi, 1993. "The Stability of the Japanese Banking System: A Historical Perspective," Journal of the Japanese and International Economies, Elsevier, vol. 7(4), pages 387-407, December.

    Cited by:

    1. Masami Imai & Tetsuji Okazaki & Michiru Sawada, 2019. "The Effects of Lender of Last Resort on Financial Intermediation during the Great Depression in Japan," CIRJE F-Series CIRJE-F-1111, CIRJE, Faculty of Economics, University of Tokyo.
    2. Tetsuji Okazaki & Michiru Sawada, 2008. "Interbank Networks in Pre-war Japan:Structure and Implications," CARF F-Series CARF-F-142, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    3. Konishi, Masaru, 2002. "Bond underwriting by banks and conflicts of interest: Evidence from Japan during the pre-war period," Journal of Banking & Finance, Elsevier, vol. 26(4), pages 767-793, April.
    4. Konishi, Masaru, 2005. "Bond underwriting syndicates organized by commercial banks: evidence from prewar Japan," Journal of the Japanese and International Economies, Elsevier, vol. 19(3), pages 303-321, September.
    5. Uesugi, Iichiro & Hiraga, Kazuki & Manabe, Masashi & Yoshino, Naoyuki, 2022. "Measuring concentration in the Japanese loan and deposit markets," Japan and the World Economy, Elsevier, vol. 63(C).
    6. Okazaki, Tetsuji, 2007. "Micro-aspects of monetary policy: Lender of Last Resort and selection of banks in pre-war Japan," Explorations in Economic History, Elsevier, vol. 44(4), pages 657-679, October.
    7. Yokoyama, Kazuki, 2007. "Too Big to Fail: the Panic of 1927," MPRA Paper 2768, University Library of Munich, Germany.
    8. Tetsuji Okazaki & Michiru Sawada & Kazuki Yokoyama, 2003. "Measuring the Extent and Implications of Director Interlocking in the Pre-war Japanese Banking Industry," CIRJE F-Series CIRJE-F-241, CIRJE, Faculty of Economics, University of Tokyo.
    9. Kim Ristolainen & Tomi Roukka & Henri Nyberg, 2021. "A Thousand Words Tell More Than Just Numbers: Financial Crises and Historical Headlines," Discussion Papers 149, Aboa Centre for Economics.
    10. Kasuya, Makoto, 2007. "Bond markets and banks in inter-war Japan," LSE Research Online Documents on Economics 6873, London School of Economics and Political Science, LSE Library.
    11. Makoto Kasuya, 2007. "Bond Markets and Banks in Inter-War Japan," STICERD - International Studies Paper Series 521, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    12. Vitols, Sigurt, 2001. "The origins of bank-based and market-based financial systems: Germany, Japan, and the United States," Discussion Papers, Research Unit: Economic Change and Employment FS I 01-302, WZB Berlin Social Science Center.

Software components

    Sorry, no citations of software components recorded.

Chapters

  1. Atsushi Inoue & Barbara Rossi, 2018. "The Effects of Conventional and Unconventional Monetary Policy on Exchange Rates," NBER Chapters, in: NBER International Seminar on Macroeconomics 2018, pages 419-447, National Bureau of Economic Research, Inc.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.