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Marius Ooms

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.

Working papers

  1. Geert Mesters & Siem Jan Koopman & Marius Ooms, 2011. "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models," Tinbergen Institute Discussion Papers 11-090/4, Tinbergen Institute.

    Cited by:

    1. Hartl, Tobias & Weigand, Roland, 2019. "Multivariate Fractional Components Analysis," University of Regensburg Working Papers in Business, Economics and Management Information Systems 38283, University of Regensburg, Department of Economics.
    2. Antonello D'Agostino & Domenico Giannone & Michele Lenza & Michele Modugno, 2015. "Nowcasting Business Cycles: a Bayesian Approach to Dynamic Heterogeneous Factor Models," Finance and Economics Discussion Series 2015-66, Board of Governors of the Federal Reserve System (U.S.).
    3. Siem Jan Koopman & Marcel Scharth, 2011. "The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures," Tinbergen Institute Discussion Papers 11-132/4, Tinbergen Institute.
    4. Tobias Hartl & Roland Jucknewitz, 2022. "Approximate state space modelling of unobserved fractional components," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 75-98, January.
    5. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    6. Francisco Blasques & Meindert Heres Hoogerkamp & Siem Jan Koopman & Ilka van de Werve, 2020. "Dynamic Factor Models with Clustered Loadings: Forecasting Education Flows using Unemployment Data," Tinbergen Institute Discussion Papers 20-078/III, Tinbergen Institute, revised 21 Jan 2021.
    7. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.

  2. Irma Hindrayanto & John A.D. Aston & Siem Jan Koopman & Marius Ooms, 2010. "Modeling Trigonometric Seasonal Components for Monthly Economic Time Series," Tinbergen Institute Discussion Papers 10-018/4, Tinbergen Institute.

    Cited by:

    1. C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "Temperature in the Iberian Peninsula: Trend, seasonality, and heterogeneity," Papers 2406.14145, arXiv.org.
    2. Fausto Hern'andez Trillo & C. Vladimir Rodr'iguez-Caballero & Daniel Ventosa-Santaul`aria, 2024. "Monopoly Unveiled: Telecom Breakups in the US and Mexico," Papers 2407.09695, arXiv.org.
    3. Castillo-Manzano, José I. & Pedregal, Diego J. & Pozo-Barajas, Rafael, 2016. "An econometric evaluation of the management of large-scale transport infrastructure in Spain during the great recession: Lessons for infrastructure bubbles," Economic Modelling, Elsevier, vol. 53(C), pages 302-313.
    4. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.

  3. Ooms, M., 2008. "Trends in Applied Econometrics Software Development 1985-2008, an analysis of Journal of Applied Econometrics research articles, software reviews, data and code," Serie Research Memoranda 0021, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.

    Cited by:

    1. Rodolphe Buda, 2013. "SIMUL 3.2: An Econometric Tool for Multidimensional Modelling," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 517-524, April.
    2. Rodolphe Buda, 2015. "Data Checking and Econometric Software Development: A Technique of Traceability by Fictive Data Encoding," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 325-357, August.

  4. V. Dordonnat & S.J. Koopman & M. Ooms & A. Dessertaine & J. Collet, 2008. "An Hourly Periodic State Space Model for Modelling French National Electricity Load," Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute.

    Cited by:

    1. Chan, Kam Fong & Gray, Philip & van Campen, Bart, 2008. "A new approach to characterizing and forecasting electricity price volatility," International Journal of Forecasting, Elsevier, vol. 24(4), pages 728-743.
    2. Zawadzki Jan, 2023. "Comparative Analysis of Methods for Hourly Electricity Demand Forecasting in the Absence of Data – A Case Study," Economic and Regional Studies / Studia Ekonomiczne i Regionalne, Sciendo, vol. 16(1), pages 34-50, March.
    3. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    4. Bingtuan Gao & Xiaofeng Liu & Zhenyu Zhu, 2018. "A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents," Energies, MDPI, vol. 11(8), pages 1-16, August.
    5. Mestekemper, Thomas & Kauermann, Göran & Smith, Michael S., 2013. "A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting," International Journal of Forecasting, Elsevier, vol. 29(1), pages 1-12.
    6. Lisi, Francesco & Pelagatti, Matteo M., 2018. "Component estimation for electricity market data: Deterministic or stochastic?," Energy Economics, Elsevier, vol. 74(C), pages 13-37.
    7. Cho, Haeran & Goude, Yannig & Brossat, Xavier & Yao, Qiwei, 2013. "Modeling and forecasting daily electricity load curves: a hybrid approach," LSE Research Online Documents on Economics 49634, London School of Economics and Political Science, LSE Library.
    8. Tristan Launay & Anne Philippe & Sophie Lamarche, 2015. "Construction of an informative hierarchical prior for a small sample with the help of historical data and application to electricity load forecasting," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 361-385, June.
    9. Goia, Aldo & May, Caterina & Fusai, Gianluca, 2010. "Functional clustering and linear regression for peak load forecasting," International Journal of Forecasting, Elsevier, vol. 26(4), pages 700-711, October.
    10. Takeda, Hisashi & Tamura, Yoshiyasu & Sato, Seisho, 2016. "Using the ensemble Kalman filter for electricity load forecasting and analysis," Energy, Elsevier, vol. 104(C), pages 184-198.
    11. Angelica Gianfreda & Luigi Grossi, 2011. "Forecasting Italian Electricity Zonal Prices with Exogenous Variables," Working Papers 01/2011, University of Verona, Department of Economics.
    12. Frédéric Karamé & Yannick Fondeur, 2012. "Can Google Data Help Predict French Youth Unemployment?," Documents de recherche 12-03, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
    13. F. M. Andersen & H. V. Larsen & L. Kitzing & P. E. Morthorst, 2014. "Who gains from hourly time‐of‐use retail prices on electricity? An analysis of consumption profiles for categories of Danish electricity customers," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(6), pages 582-593, November.
    14. Eduardo Caro & Jesús Juan, 2020. "Short-Term Load Forecasting for Spanish Insular Electric Systems," Energies, MDPI, vol. 13(14), pages 1-26, July.
    15. Engeland, Kolbjørn & Borga, Marco & Creutin, Jean-Dominique & François, Baptiste & Ramos, Maria-Helena & Vidal, Jean-Philippe, 2017. "Space-time variability of climate variables and intermittent renewable electricity production – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 600-617.
    16. Marie Bessec & Julien Fouquau, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," Post-Print hal-01644930, HAL.
    17. Zafer Dilaver & Lester C Hunt, 2011. "Turkish Aggregate Electricity Demand: An Outlook to 2020," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 132, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
    18. Tawil, Tony El & Charpentier, Jean Frédéric & Benbouzid, Mohamed, 2018. "Sizing and rough optimization of a hybrid renewable-based farm in a stand-alone marine context," Renewable Energy, Elsevier, vol. 115(C), pages 1134-1143.
    19. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.
    20. Abdelmonaem Jornaz & V. A. Samaranayake, 2019. "A Multi-Step Approach to Modeling the 24-hour Daily Profiles of Electricity Load using Daily Splines," Energies, MDPI, vol. 12(21), pages 1-22, November.
    21. Charlton, Nathaniel & Singleton, Colin, 2014. "A refined parametric model for short term load forecasting," International Journal of Forecasting, Elsevier, vol. 30(2), pages 364-368.
    22. Verstraete, Gylian & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "A data-driven framework for predicting weather impact on high-volume low-margin retail products," Journal of Retailing and Consumer Services, Elsevier, vol. 48(C), pages 169-177.
    23. Kaneko, Nanae & Fujimoto, Yu & Kabe, Satoshi & Hayashida, Motonari & Hayashi, Yasuhiro, 2020. "Sparse modeling approach for identifying the dominant factors affecting situation-dependent hourly electricity demand," Applied Energy, Elsevier, vol. 265(C).
    24. Andersen, F.M. & Larsen, H.V. & Juul, N. & Gaardestrup, R.B., 2014. "Differentiated long term projections of the hourly electricity consumption in local areas. The case of Denmark West," Applied Energy, Elsevier, vol. 135(C), pages 523-538.
    25. Trapero, Juan R. & Pedregal, Diego J., 2009. "Frequency domain methods applied to forecasting electricity markets," Energy Economics, Elsevier, vol. 31(5), pages 727-735, September.
    26. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    27. Komi Nagbe & Jairo Cugliari & Julien Jacques, 2018. "Short-Term Electricity Demand Forecasting Using a Functional State Space Model," Energies, MDPI, vol. 11(5), pages 1-24, May.
    28. Keita Honjo & Hiroto Shiraki & Shuichi Ashina, 2018. "Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    29. Zafer Dilaver & Lester C Hunt, 2010. "Industrial Electricity Demand for Turkey: A Structural Time Series Analysis," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 129, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
    30. Masoud Sobhani & Allison Campbell & Saurabh Sangamwar & Changlin Li & Tao Hong, 2019. "Combining Weather Stations for Electric Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-11, April.
    31. Zawadzki, Jan, 2023. "Comparative Analysis Of Methods For Hourly Electricity Demand Forecasting In The Absence Of Data – A Case Study," Economic and Regional Studies (Studia Ekonomiczne i Regionalne), John Paul II University of Applied Sciences in Biala Podlaska, vol. 16(1), March.
    32. Wang, Yaoping & Bielicki, Jeffrey M., 2018. "Acclimation and the response of hourly electricity loads to meteorological variables," Energy, Elsevier, vol. 142(C), pages 473-485.
    33. Antoniadis, Anestis & Brossat, Xavier & Cugliari, Jairo & Poggi, Jean-Michel, 2016. "A prediction interval for a function-valued forecast model: Application to load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 939-947.
    34. Jose Juan Caceres-Hernandez & Gloria Martin-Rodriguez & Jonay Hernandez-Martin, 2022. "A proposal for measuring and comparing seasonal variations in hourly economic time series," Empirical Economics, Springer, vol. 62(4), pages 1995-2021, April.
    35. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
    36. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
    37. Vaz, Lucélia Viviane & Filho, Getulio Borges da Silveira, 2017. "Functional Autoregressive Models: An Application to Brazilian Hourly Electricity Load," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 37(2), November.
    38. Andersen, F.M. & Larsen, H.V. & Gaardestrup, R.B., 2013. "Long term forecasting of hourly electricity consumption in local areas in Denmark," Applied Energy, Elsevier, vol. 110(C), pages 147-162.
    39. Brabec, Marek & Konár, Ondrej & Pelikán, Emil & Malý, Marek, 2008. "A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers," International Journal of Forecasting, Elsevier, vol. 24(4), pages 659-678.
    40. Alfredo Nespoli & Emanuele Ogliari & Silvia Pretto & Michele Gavazzeni & Sonia Vigani & Franco Paccanelli, 2021. "Electrical Load Forecast by Means of LSTM: The Impact of Data Quality," Forecasting, MDPI, vol. 3(1), pages 1-11, February.
    41. Arora, Siddharth & Taylor, James W., 2018. "Rule-based autoregressive moving average models for forecasting load on special days: A case study for France," European Journal of Operational Research, Elsevier, vol. 266(1), pages 259-268.
    42. Dordonnat, Virginie & Koopman, Siem Jan & Ooms, Marius, 2012. "Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3134-3152.
    43. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    44. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.

  5. Siem Jan Koopman & André Lucas & Marius Ooms & Kees van Montfort & Victor van der Geest, 2007. "Estimating Systematic Continuous-time Trends in Recidivism using a Non-Gaussian Panel Data Model," Tinbergen Institute Discussion Papers 07-027/4, Tinbergen Institute.

    Cited by:

    1. Geert Mesters & Siem Jan Koopman, 2012. "Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time," Tinbergen Institute Discussion Papers 12-009/4, Tinbergen Institute, revised 18 Mar 2014.
    2. Vujić Sunčica & Koopman Siem Jan & Commandeur J.F., 2012. "Economic Trends and Cycles in Crime: A Study for England and Wales," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(6), pages 652-677, December.
    3. Suncica Vujic & Jacques Commandeur & Siem Jan Koopman, 2012. "Structural Intervention Time Series Analysis of Crime Rates: The Impact of Sentence Reform in Virginia," Tinbergen Institute Discussion Papers 12-007/4, Tinbergen Institute.
    4. Vujić, Sunčica & Commandeur, Jacques J.F. & Koopman, Siem Jan, 2016. "Intervention time series analysis of crime rates: The case of sentence reform in Virginia," Economic Modelling, Elsevier, vol. 57(C), pages 311-323.

  6. Charles S. Bos & Siem Jan Koopman & Marius Ooms, 2007. "Long memory modelling of inflation with stochastic variance and structural breaks," CREATES Research Papers 2007-44, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Josu Arteche, 2012. "Standard and seasonal long memory in volatility: an application to Spanish inflation," Empirical Economics, Springer, vol. 42(3), pages 693-712, June.
    2. Grassi, Stefano & Proietti, Tommaso, 2008. "Has the Volatility of U.S. Inflation Changed and How?," MPRA Paper 11453, University Library of Munich, Germany.
    3. Luis A. Gil-Alana & Yadollah Dadgar & Rouhollah Nazari, 2019. "Iranian inflation: peristence and structural breaks," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(2), pages 398-408, April.

  7. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2006. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Tinbergen Institute Discussion Papers 06-101/4, Tinbergen Institute.

    Cited by:

    1. Hindrayanto, Irma & Koopman, Siem Jan & Ooms, Marius, 2010. "Exact maximum likelihood estimation for non-stationary periodic time series models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2641-2654, November.
    2. Rodrigo Barbone Gonzalez & Joaquim Lima & Leonardo Marinho, 2015. "Business and Financial Cycles: an estimation of cycles’ length focusing on Macroprudential Policy," Working Papers Series 385, Central Bank of Brazil, Research Department.
    3. Sergey Seleznev & Natalia Turdyeva & Ramis Khabibullin & Anna Tsvetkova, 2020. "Seasonal adjustment of the Bank of Russia Payment System financial flows data," Bank of Russia Working Paper Series wps65, Bank of Russia.
    4. Uwe Blien & Oliver Ludewig & Anja Rossen, 2023. "Contradictory effects of technological change across developed countries," Review of International Economics, Wiley Blackwell, vol. 31(2), pages 580-608, May.
    5. Rodrigo Barbone Gonzalez & Joaquim Lima & Leonardo Marinho, 2015. "Countercyclical Capital Buffers: bayesian estimates and alternatives focusing on credit growth," Working Papers Series 384, Central Bank of Brazil, Research Department.

  8. Jurgen A. Doornik & Marius Ooms, 2005. "Outlier Detection in GARCH Models," Economics Papers 2005-W24, Economics Group, Nuffield College, University of Oxford.

    Cited by:

    1. Juraj Valachy & Ev??en Ko?enda, 2003. "Exchange Rate Regimes and Volatility: Comparison of the Snake and Visegrad," William Davidson Institute Working Papers Series 2003-622, William Davidson Institute at the University of Michigan.
    2. E. Ruiz & M.A. Carnero & D. Pereira, 2004. "Effects of Level Outliers on the Identification and Estimation of GARCH Models," Econometric Society 2004 Australasian Meetings 21, Econometric Society.
    3. Lisa Crosato & Luigi Grossi, 2019. "Correcting outliers in GARCH models: a weighted forward approach," Statistical Papers, Springer, vol. 60(6), pages 1939-1970, December.
    4. Koenig, P., 2011. "Modelling Correlation in Carbon and Energy Markets," Cambridge Working Papers in Economics 1123, Faculty of Economics, University of Cambridge.
    5. Amélie Charles & Olivier Darné, 2012. "Volatility Persistence in Crude Oil Markets," Working Papers hal-00719387, HAL.
    6. Charles, Amélie & Darné, Olivier, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 188-199.
    7. Beum-Jo Park, 2009. "Risk-return relationship in equity markets: using a robust GMM estimator for GARCH-M models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 93-104.
    8. Maria Eugenia Sanin & Francesco Violante & Maria Mansanet-Bataller, 2015. "Understanding volatility dynamics in the EU-ETS market," Post-Print hal-02878047, HAL.
    9. Behmiri, Niaz Bashiri & Manera, Matteo, 2015. "The role of outliers and oil price shocks on volatility of metal prices," Resources Policy, Elsevier, vol. 46(P2), pages 139-150.
    10. Olmo, J., 2009. "Extreme Value Theory Filtering Techniques for Outlier Detection," Working Papers 09/09, Department of Economics, City University London.
    11. Jurgen A. Doornik & Marius Ooms, 2003. "Multimodality in the GARCH Regression Model," Economics Papers 2003-W20, Economics Group, Nuffield College, University of Oxford.
    12. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
    13. Carnero, M. Angeles & Pérez, Ana, 2019. "Leverage effect in energy futures revisited," Energy Economics, Elsevier, vol. 82(C), pages 237-252.
    14. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2008. "Estimating and Forecasting GARCH Volatility in the Presence of Outiers," Working Papers. Serie AD 2008-13, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    15. Puigvert Gutiérrez, Josep Maria & Fortiana Gregori, Josep, 2008. "Clustering techniques applied to outlier detection of financial market series using a moving window filtering algorithm," Working Paper Series 948, European Central Bank.
    16. Amira Akl Ahmed & Doaa Akl Ahmed, 2016. "Modelling Conditional Volatility and Downside Risk for Istanbul Stock Exchange," Working Papers 1028, Economic Research Forum, revised Jul 2016.
    17. Zhang, Dayong & Dickinson, David & Barassi, Marco, 2008. "Volatility Switching in Shanghai Stock Exchange: Does regulation help reduce volatility?," MPRA Paper 70352, University Library of Munich, Germany.
    18. Veiga, Helena, 2009. "Wavelet-based detection of outliers in volatility models," DES - Working Papers. Statistics and Econometrics. WS ws090403, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Mora Galán, Alberto & Pérez, Ana, 2004. "Stochastic volatility models and the Taylor effect," DES - Working Papers. Statistics and Econometrics. WS ws046315, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Amélie Charles, 2008. "Forecasting volatility with outliers in GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 551-565.
    21. Grané, Aurea & Veiga, Helena, 2010. "Wavelet-based detection of outliers in financial time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2580-2593, November.
    22. Fagiani, Riccardo & Hakvoort, Rudi, 2014. "The role of regulatory uncertainty in certificate markets: A case study of the Swedish/Norwegian market," Energy Policy, Elsevier, vol. 65(C), pages 608-618.
    23. Maurício Yoshinori Une & Marcelo Savino Portugal, 2005. "Can fear beat hope? A story of GARCH-in-Mean-Level effects for Emerging Market Country Risks," Econometrics 0509006, University Library of Munich, Germany.
    24. Grané, Aurea & Veiga, Helena, 2010. "Outliers in Garch models and the estimation of risk measures," DES - Working Papers. Statistics and Econometrics. WS ws100502, Universidad Carlos III de Madrid. Departamento de Estadística.
    25. Mangold, Benedikt & Pleier, Thomas & Brug, Christoph & Nolzen, Jan & Stübinger, Johannes, 2014. "Verbesserung des Lernverhaltens durch Online-Tests: Ein Jahr später," Discussion Papers 91/2013, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.

  9. Siem Jan Koopman & Marius Ooms & M. Angeles Carnero, 2005. "Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 05-091/4, Tinbergen Institute.

    Cited by:

    1. Massimiliano Serati & Matteo Manera & Michele Plotegher, 2008. "Modelling electricity prices: from the state of the art to a draft of a new proposal," LIUC Papers in Economics 210, Cattaneo University (LIUC).
    2. Brusaferri, Alessandro & Matteucci, Matteo & Portolani, Pietro & Vitali, Andrea, 2019. "Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices," Applied Energy, Elsevier, vol. 250(C), pages 1158-1175.
    3. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2013. "Estimation and Inference in Univariate and Multivariate Log-GARCH-X Models When the Conditional Density is Unknown," MPRA Paper 49344, University Library of Munich, Germany.
    4. Marcel Aloy & Gilles Dufrenot & Charles Lai-Tong & Anne Peguin-Feissolle, 2012. "A Smooth Transition Long-Memory Model," Working Papers halshs-00793680, HAL.
    5. Eduardo Rossi & Dean Fantazzini, 2012. "Long memory and Periodicity in Intraday Volatility," DEM Working Papers Series 015, University of Pavia, Department of Economics and Management.
    6. João Pedro Pereira & Vasco Pesquita & Paulo M. M. Rodrigues & António Rua, 2019. "Market integration and the persistence of electricity prices," Empirical Economics, Springer, vol. 57(5), pages 1495-1514, November.
    7. Paraschiv, Florentina & Bunn, Derek & Westgaard, Sjur, 2016. "Estimation and Application of Fully Parametric Multifactor Quantile Regression with Dynamic Coefficients," Working Papers on Finance 1607, University of St. Gallen, School of Finance.
    8. Mawuli Segnon & Chi Keung Lau & Bernd Wilfling & Rangan Gupta, 2017. "Are Multifractal Processes Suited to Forecasting Electricity Price Volatility? Evidence from Australian Intraday Data," Working Papers 201739, University of Pretoria, Department of Economics.
    9. Sophie Bercu & Fr�d�ric Proïa, 2013. "A SARIMAX coupled modelling applied to individual load curves intraday forecasting," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(6), pages 1333-1348, June.
    10. 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.
    11. Ding, Shusheng & Cui, Tianxiang & Zhang, Yongmin, 2022. "Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect," International Review of Financial Analysis, Elsevier, vol. 83(C).
    12. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
    13. Yannick LE PEN & Benoît SEVI, 2008. "Volatility transmission and volatility impulse response functions in European electricity forward markets," Cahiers du CREDEN (CREDEN Working Papers) 08.09.77, CREDEN (Centre de Recherche en Economie et Droit de l'Energie), Faculty of Economics, University of Montpellier 1.
    14. Diongue, Abdou Kâ & Guégan, Dominique & Vignal, Bertrand, 2009. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Applied Energy, Elsevier, vol. 86(4), pages 505-510, April.
    15. Renato Fernandes & Isabel Soares, 2022. "Reviewing Explanatory Methodologies of Electricity Markets: An Application to the Iberian Market," Energies, MDPI, vol. 15(14), pages 1-17, July.
    16. Nazim Regnard & Jean‐Michel Zakoïan, 2010. "Structure and estimation of a class of nonstationary yet nonexplosive GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 348-364, September.
    17. Beatriz Martínez Martínez & Hipolit Torro Enguix, 2017. "Hedging spark spread risk with futures," Working Papers. Serie EC 2017-01, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    18. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    19. Sucarrat, Genaro, 2016. "Equation-by-Equation Estimation of Multivariate Periodic Electricity Price Volatility," UC3M Working papers. Economics 23436, Universidad Carlos III de Madrid. Departamento de Economía.
    20. Chelsey Hill & James Li & Matthew J. Schneider & Martin T. Wells, 2021. "The tensor auto‐regressive model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 636-652, July.
    21. Lisi, Francesco & Pelagatti, Matteo M., 2018. "Component estimation for electricity market data: Deterministic or stochastic?," Energy Economics, Elsevier, vol. 74(C), pages 13-37.
    22. Francq, Christian & Sucarrat, Genaro, 2013. "An Exponential Chi-Squared QMLE for Log-GARCH Models Via the ARMA Representation," MPRA Paper 51783, University Library of Munich, Germany.
    23. Ambach, Daniel & Schmid, Wolfgang, 2015. "Periodic and long range dependent models for high frequency wind speed data," Energy, Elsevier, vol. 82(C), pages 277-293.
    24. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity prices with factor models: Utilizing intra-day and inter-zone relationships," HSC Research Reports HSC/13/11, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    25. Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    26. Christian Huurman & Francesco Ravazzolo & Chen Zhou, 2008. "The power of weather. Some empirical evidence on predicting day-ahead power prices through weather forecasts," Working Paper 2008/08, Norges Bank.
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    67. Rodríguez Caballero, Carlos Vladimir, 2017. "Estimation of a Dynamic Multilevel Factor Model with possible long-range dependence," DES - Working Papers. Statistics and Econometrics. WS 24614, Universidad Carlos III de Madrid. Departamento de Estadística.
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    72. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions," International Journal of Forecasting, Elsevier, vol. 24(4), pages 710-727.
    73. Zanotti, Giovanna & Gabbi, Giampaolo & Geranio, Manuela, 2010. "Hedging with futures: Efficacy of GARCH correlation models to European electricity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(2), pages 135-148, April.
    74. Anne Opschoor & Dewi Peerlings & Luca Rossini & Andre Lucas, 2024. "Density Forecasting for Electricity Prices under Tail Heterogeneity with the t-Riesz Distribution," Tinbergen Institute Discussion Papers 24-049/III, Tinbergen Institute.
    75. 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.
    76. Torro, Hipolit, 2009. "Assessing the influence of spot price predictability on electricity futures hedging," MPRA Paper 18892, University Library of Munich, Germany.
    77. Ladislav KRISTOUFEK & Petra LUNACKOVA, 2013. "Long-term Memory in Electricity Prices: Czech Market Evidence," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 407-424, November.
    78. Ioannidis, Filippos & Kosmidou, Kyriaki & Savva, Christos & Theodossiou, Panayiotis, 2021. "Electricity pricing using a periodic GARCH model with conditional skewness and kurtosis components," Energy Economics, Elsevier, vol. 95(C).
    79. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    80. Kim, Myung Suk, 2013. "Modeling special-day effects for forecasting intraday electricity demand," European Journal of Operational Research, Elsevier, vol. 230(1), pages 170-180.
    81. Panagiotelis, Anastasios & Smith, Michael, 2010. "Bayesian skew selection for multivariate models," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1824-1839, July.
    82. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    83. Zorana Zoran Stanković & Milena Nebojsa Rajic & Zorana Božić & Peđa Milosavljević & Ancuța Păcurar & Cristina Borzan & Răzvan Păcurar & Emilia Sabău, 2024. "The Volatility Dynamics of Prices in the European Power Markets during the COVID-19 Pandemic Period," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    84. Sherzod N. Tashpulatov, 2022. "Modeling Electricity Price Dynamics Using Flexible Distributions," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    85. Duván Humberto Cataño & Carlos Vladimir Rodríguez-Caballero & Daniel Peña, 2019. "Wavelet Estimation for Dynamic Factor Models with Time-Varying Loadings," CREATES Research Papers 2019-23, Department of Economics and Business Economics, Aarhus University.
    86. Fell, Harrison, 2008. "EU-ETS and Nordic Electricity: A CVAR Approach," RFF Working Paper Series dp-08-31, Resources for the Future.
    87. Tashpulatov, Sherzod N., 2013. "Estimating the volatility of electricity prices: The case of the England and Wales wholesale electricity market," Energy Policy, Elsevier, vol. 60(C), pages 81-90.
    88. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, vol. 24(4), pages 764-785.
    89. Hipòlit Torró & Julio Lucia, 2008. "Short-term electricity futures prices: Evidence on the time-varying risk premium," Working Papers. Serie EC 2008-08, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    90. Sherzod N. Tashpulatov, 2021. "The Impact of Regulatory Reforms on Demand Weighted Average Prices," Mathematics, MDPI, vol. 9(10), pages 1-15, May.
    91. Alexopoulos, Thomas A., 2017. "The growing importance of natural gas as a predictor for retail electricity prices in US," Energy, Elsevier, vol. 137(C), pages 219-233.
    92. Liebl, Dominik, 2013. "Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective," MPRA Paper 50881, University Library of Munich, Germany.
    93. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    94. Sucarrat, Genaro, 2010. "The power log-GARCH model," UC3M Working papers. Economics we1013, Universidad Carlos III de Madrid. Departamento de Economía.
    95. Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2022. "A Dual Generalized Long Memory Modelling for Forecasting Electricity Spot Price: Neural Network and Wavelet Estimate," Papers 2204.08289, arXiv.org.
    96. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.
    97. Janczura, Joanna & Trück, Stefan & Weron, Rafał & Wolff, Rodney C., 2013. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," Energy Economics, Elsevier, vol. 38(C), pages 96-110.
    98. Lucia, Julio J. & Torró, Hipòlit, 2011. "On the risk premium in Nordic electricity futures prices," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 750-763, October.
    99. Niels Haldrup & Oskar Knapik & Tommaso Proietti, 2016. "A generalized exponential time series regression model for electricity prices," CREATES Research Papers 2016-08, Department of Economics and Business Economics, Aarhus University.
    100. Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino, 2023. "Weekly economic activity: Measurement and informational content," International Journal of Forecasting, Elsevier, vol. 39(1), pages 228-243.
    101. Shadi Tehrani & Jesús Juan & Eduardo Caro, 2022. "Electricity Spot Price Modeling and Forecasting in European Markets," Energies, MDPI, vol. 15(16), pages 1-23, August.
    102. Heidarpanah, Mohammadreza & Hooshyaripor, Farhad & Fazeli, Meysam, 2023. "Daily electricity price forecasting using artificial intelligence models in the Iranian electricity market," Energy, Elsevier, vol. 263(PE).
    103. Michel Culot & Valérie Goffin & Steve Lawford & Sébastien de Meten & Yves Smeers, 2013. "Practical stochastic modelling of electricity prices," Post-Print hal-01021603, HAL.
    104. Lisi, Francesco & Nan, Fany, 2014. "Component estimation for electricity prices: Procedures and comparisons," Energy Economics, Elsevier, vol. 44(C), pages 143-159.
    105. Aknouche, Abdelhakim, 2013. "Periodic autoregressive stochastic volatility," MPRA Paper 69571, University Library of Munich, Germany, revised 2015.
    106. Bruno Bosco & Lucia Parisio & Matteo Pelagatti, 2007. "Deregulated Wholesale Electricity Prices in Italy: An Empirical Analysis," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 13(4), pages 415-432, November.
    107. Daniel Ambach & Carsten Croonenbroeck, 2016. "Space-time short- to medium-term wind speed forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 5-20, March.
    108. de Frutos Cachorro, J. & Willeghems, G. & Buysse, J., 2019. "Strategic investment decisions under the nuclear power debate in Belgium," Resource and Energy Economics, Elsevier, vol. 57(C), pages 156-184.
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    110. Amaral, Luiz Felipe & Souza, Reinaldo Castro & Stevenson, Maxwell, 2008. "A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting," International Journal of Forecasting, Elsevier, vol. 24(4), pages 603-615.
    111. 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.
    112. Bruno Bosco & Lucia Parisio & Matteo Pelagatti & Fabio Baldi, 2010. "Long-run relations in european electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 805-832.
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    118. Martina Assereto & Julie Byrne, 2020. "The Implications of Policy Uncertainty on Solar Photovoltaic Investment," Energies, MDPI, vol. 13(23), pages 1-20, November.
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  10. Marius Ooms & M. Angeles Carnero & Siem Jan Koopman, 2004. "Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices," Econometric Society 2004 Australasian Meetings 158, Econometric Society.

    Cited by:

    1. Sandro Sapio, 2004. "Market Design, Bidding Rules, and Long Memory in Electricity Prices," LEM Papers Series 2004/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    3. Massimiliano Caporin & Juliusz Pres' & Hipolit Torro, 2010. "Model Based Monte Carlo Pricing of Energy and Temperature Quanto Options," "Marco Fanno" Working Papers 0123, Dipartimento di Scienze Economiche "Marco Fanno".
    4. Siem Jan Koopman & Marius Ooms, 2004. "Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models," Tinbergen Institute Discussion Papers 04-135/4, Tinbergen Institute.
    5. 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.
    6. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number hsbook0601, December.
    7. Torro, Hipolit, 2007. "Forecasting Weekly Electricity Prices at Nord Pool," International Energy Markets Working Papers 7437, Fondazione Eni Enrico Mattei (FEEM).
    8. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    9. S. Vijayalakshmi & G. P. Girish, 2015. "Artificial Neural Networks for Spot Electricity Price Forecasting: A Review," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 1092-1097.
    10. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, vol. 24(4), pages 764-785.
    11. Kosater, Peter, 2006. "On the impact of weather on German hourly power prices," Discussion Papers in Econometrics and Statistics 1/06, University of Cologne, Institute of Econometrics and Statistics.
    12. Malo, Pekka, 2009. "Modeling electricity spot and futures price dependence: A multifrequency approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(22), pages 4763-4779.
    13. Karakatsani Nektaria V & Bunn Derek W., 2010. "Fundamental and Behavioural Drivers of Electricity Price Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-42, September.
    14. 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.
    15. Kosater, Peter & Mosler, Karl, 2006. "Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices," Applied Energy, Elsevier, vol. 83(9), pages 943-958, September.
    16. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Intra-day and regime-switching dynamics in electricity price formation," Energy Economics, Elsevier, vol. 30(4), pages 1776-1797, July.

  11. Siem Jan Koopman & Marius Ooms, 2004. "Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models," Tinbergen Institute Discussion Papers 04-135/4, Tinbergen Institute.

    Cited by:

    1. Yorghos Tripodis & Jeremy Penzer, 2009. "Modelling time series with season-dependent autocorrelation structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 559-574.
    2. Cornillon, P.-A. & Imam, W. & Matzner-Lober, E., 2008. "Forecasting time series using principal component analysis with respect to instrumental variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1269-1280, January.
    3. Ollech, Daniel, 2018. "Seasonal adjustment of daily time series," Discussion Papers 41/2018, Deutsche Bundesbank.
    4. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2006. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Tinbergen Institute Discussion Papers 06-101/4, Tinbergen Institute.
    5. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    6. Yılmaz, Engin, 2015. "Forecasting tourist arrivals to Turkey," MPRA Paper 68616, University Library of Munich, Germany.
    7. Alonso, Andres M. & Sipols, Ana E., 2008. "A time series bootstrap procedure for interpolation intervals," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1792-1805, January.
    8. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
    9. Martín Rodríguez, Gloria & Cáceres Hernández, José Juan, 2010. "Splines and the proportion of the seasonal period as a season index," Economic Modelling, Elsevier, vol. 27(1), pages 83-88, January.
    10. Triantafyllopoulos, K. & Nason, G.P., 2007. "A Bayesian analysis of moving average processes with time-varying parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 1025-1046, October.
    11. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
    12. Bauer, Dietmar, 2019. "Periodic and seasonal (co-)integration in the state space framework," Economics Letters, Elsevier, vol. 174(C), pages 165-168.

  12. Jurgen A. Doornik & Marius Ooms, 2003. "Multimodality in the GARCH Regression Model," Economics Papers 2003-W20, Economics Group, Nuffield College, University of Oxford.

    Cited by:

    1. Bernd Hayo & Ali Kutan, 2004. "The Impact of News, Oil Prices, and Global Market Developments on Russian Financial Markets," Finance 0403002, University Library of Munich, Germany.
    2. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2007. "Effects of outliers on the identification and estimation of GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(4), pages 471-497, July.
    3. Rezitis Anthony N & Stavropoulos Konstantinos S, 2011. "Price Transmission and Volatility in the Greek Broiler Sector: A Threshold Cointegration Analysis," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 9(1), pages 1-37, July.
    4. Funke, Michael & Shu, Chang & Cheng, Xiaoqiang & Eraslan, Sercan, 2015. "Assessing the CNH–CNY pricing differential: Role of fundamentals, contagion and policy," Journal of International Money and Finance, Elsevier, vol. 59(C), pages 245-262.
    5. Boysen-Hogrefe, Jens, 2017. "Risk assessment on euro area government bond markets – The role of governance," Journal of International Money and Finance, Elsevier, vol. 73(PA), pages 104-117.
    6. Bernd Hayo & Ali M. Kutan & Matthias Neuenkirch, 2009. "Federal Reserve Communications and Emerging Equity Markets," MAGKS Papers on Economics 200923, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. David Büttner & Bernd Hayo & Matthias Neuenkirch, 2012. "The impact of foreign macroeconomic news on financial markets in the Czech Republic, Hungary, and Poland," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 39(1), pages 19-44, February.
    8. Doyle, John R. & Chen, Catherine Huirong, 2012. "A multidimensional classification of market anomalies: Evidence from 76 price indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1237-1257.
    9. Hayo, Bernd & Kutan, Ali M., 2002. "The impact of news, oil prices, and international spillovers on Russian financial markets," ZEI Working Papers B 20-2002, University of Bonn, ZEI - Center for European Integration Studies.
    10. Bernd Hayo & Ali Kutan & Matthias Neuenkirch, 2015. "Financial market reaction to Federal Reserve communications: Does the global financial crisis make a difference?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(1), pages 185-203, February.
    11. D Büttner & B. Hayo, 2012. "EMU-related news and financial markets in the Czech Republic, Hungary and Poland," Applied Economics, Taylor & Francis Journals, vol. 44(31), pages 4037-4053, November.
    12. Broto, Carmen, 2013. "The effectiveness of forex interventions in four Latin American countries," Emerging Markets Review, Elsevier, vol. 17(C), pages 224-240.
    13. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
    14. Mark, Joy, 2011. "Gold and the US dollar: Hedge or haven?," Finance Research Letters, Elsevier, vol. 8(3), pages 120-131, September.
    15. Baoying Lai & Nathan Lael Joseph, 2010. "Pricing-to-market and the volatility of UK export prices," Applied Financial Economics, Taylor & Francis Journals, vol. 20(18), pages 1441-1460.
    16. Kim, Suk-Joong, 2007. "Intraday evidence of efficacy of 1991-2004 Yen intervention by the Bank of Japan," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 17(4), pages 341-360, October.
    17. Schnabl, Gunther & Hillebrand, Eric, 2006. "A structural break in the effects of Japanese foreign exchange intervention on yen/dollar exchange rate volatility," Working Paper Series 650, European Central Bank.
    18. Jurgen A. Doornik & Marius Ooms, 2005. "Outlier Detection in GARCH Models," Economics Papers 2005-W24, Economics Group, Nuffield College, University of Oxford.
    19. Georgios Bampinas & Stilianos Fountas & Theodore Panagiotidis, 2015. "The day-of-the-week effect is weak: Evidence from the European Real Estate Sector," Discussion Paper Series 2015_02, Department of Economics, University of Macedonia, revised May 2015.
    20. Deren Caliskan & Mohammad Najand, 2016. "Stock market returns and the price of gold," Journal of Asset Management, Palgrave Macmillan, vol. 17(1), pages 10-21, January.
    21. Pellegrini, Santiago & Ruiz, Esther & Espasa, Antoni, 2011. "Prediction intervals in conditionally heteroscedastic time series with stochastic components," International Journal of Forecasting, Elsevier, vol. 27(2), pages 308-319, April.
    22. Siwen Zhou, 2021. "Exploring the driving forces of the Bitcoin currency exchange rate dynamics: an EGARCH approach," Empirical Economics, Springer, vol. 60(2), pages 557-606, February.
    23. Bernd Hayo & Matthias Neuenkirch, 2009. "Domestic or U.S. News: What Drives Canadian Financial Markets?," MAGKS Papers on Economics 200908, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    24. Manfred GILLI & Peter WINKER, 2008. "A review of heuristic optimization methods in econometrics," Swiss Finance Institute Research Paper Series 08-12, Swiss Finance Institute.
    25. Pascual, Lorenzo & Romo, Juan & Ruiz, Esther, 2006. "Bootstrap prediction for returns and volatilities in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2293-2312, May.
    26. Maurício Yoshinori Une & Marcelo Savino Portugal, 2005. "Can fear beat hope? A story of GARCH-in-Mean-Level effects for Emerging Market Country Risks," Econometrics 0509006, University Library of Munich, Germany.
    27. Zhou, Siwen, 2018. "Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach," MPRA Paper 89445, University Library of Munich, Germany.
    28. Coffinet, J. & Frappa, S., 2008. "Macroeconomic Surprises and the Inflation Compensation Curve in the Euro Area," Working papers 220, Banque de France.
    29. Casalin, Fabrizio, 2018. "Determinants of holiday effects in mainland Chinese and Hong-Kong markets," China Economic Review, Elsevier, vol. 49(C), pages 45-67.
    30. Sigauke, C. & Chikobvu, D., 2011. "Prediction of daily peak electricity demand in South Africa using volatility forecasting models," Energy Economics, Elsevier, vol. 33(5), pages 882-888, September.

  13. Charles S. Bos & Philip Hans Franses & Marius Ooms, 2001. "Inflation, Forecast Intervals and Long Memory Regression Models," Tinbergen Institute Discussion Papers 01-029/4, Tinbergen Institute.

    Cited by:

    1. Chevillon, Guillaume, 2009. "Multi-step forecasting in emerging economies: An investigation of the South African GDP," International Journal of Forecasting, Elsevier, vol. 25(3), pages 602-628, July.
    2. 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.
    3. Boubaker Heni & Canarella Giorgio & Gupta Rangan & Miller Stephen M., 2017. "Time-varying persistence of inflation: evidence from a wavelet-based approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    4. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 517-539, November.
    5. Philip Hans Franses, 2019. "Model‐based forecast adjustment: With an illustration to inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(2), pages 73-80, March.
    6. Carlos Barros & Luis Gil-Alana, 2012. "Inflation forecasting in Angola: a fractional approach," CEsA Working Papers 103, CEsA - Centre for African and Development Studies.
    7. 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.
    8. 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.
    9. 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.
    10. Chien-Chiang Lee & Chun-Ping Chang, 2007. "Mean reversion of inflation rates in 19 OECD countries: Evidence from panel Lm unit root tests with structural breaks," Economics Bulletin, AccessEcon, vol. 3(23), pages 1-15.
    11. 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.
    12. Heinen, Florian & Sibbertsen, Philipp & Kruse, Robinson, 2009. "Forecasting long memory time series under a break in persistence," Hannover Economic Papers (HEP) dp-433, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    13. Giorgio Canarella & Stephen M. Miller, 2016. "Inflation Persistence and Structural Breaks: The Experience of Inflation Targeting Countries and the US," Working papers 2016-11, University of Connecticut, Department of Economics.
    14. 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.
    15. J. Cunado & L.A. Gil-Alana & F. P Erez de Gracia, 2008. "Fractional Integration and Structural Breaks: Evidence from International Monthly Arrivals in the USA," Tourism Economics, , vol. 14(1), pages 13-23, March.
    16. 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.
    17. 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.
    18. 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.
    19. Laura Mayoral, 2005. "The Persistence of Inflation in OECD Countries:a Fractionally Integrated Approach," Working Papers 259, Barcelona School of Economics.
    20. Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute.
    21. Lovcha, Yuliya & Pérez Laborda, Àlex, 2013. "A fractionally integrated approach to monetary policy and inflation dynamics," Working Papers 2072/211795, Universitat Rovira i Virgili, Department of Economics.
    22. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
    23. Georgios KOURETAS & Mark E. WOHAR, 2010. "The Dynamics of Inflation: A Study of a Large Number of Countries," EcoMod2010 259600096, EcoMod.
    24. 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.
    25. 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.
    26. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
    27. Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2018. "Long-Memory Modeling and Forecasting: Evidence from the U.S. Historical Series of Inflation," Working Papers 201869, University of Pretoria, Department of Economics.
    28. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.
    29. Carlos Barros & Guglielmo Maria Caporale & Luis Gil-Alana, 2014. "Long Memory in Angolan Macroeconomic Series: Mean Reversion versus Explosive Behaviour," African Development Review, African Development Bank, vol. 26(1), pages 59-73.
    30. 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.
    31. Juncal Cunado & Luis A. Gil-Alana & Fernando Pérez de Gracia, 2003. "Additional Empirical Evidence on Real Convergence: A Fractionally Integrated Approach," Faculty Working Papers 01/03, School of Economics and Business Administration, University of Navarra.
    32. 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.
    33. Chu Shiou-Yen & Shane Christopher, 2017. "Using the hybrid Phillips curve with memory to forecast US inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-16, September.
    34. 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.
    35. Gil-Alana, Luis A. & Mudida, Robert, 2017. "CPI and inflation in Kenya. Structural breaks, non-linearities and dependence," International Economics, Elsevier, vol. 150(C), pages 72-79.
    36. Markku Lanne, 2006. "A Mixture Multiplicative Error Model for Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 594-616.
    37. Dmytro Krukovets & Olesia Verchenko, 2019. "Short-Run Forecasting of Core Inflation in Ukraine: a Combined ARMA Approach," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 248, pages 11-20.
    38. 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.
    39. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    40. Eugenie Hol & Siem Jan Koopman, 2002. "Stock Index Volatility Forecasting with High Frequency Data," Tinbergen Institute Discussion Papers 02-068/4, Tinbergen Institute.
    41. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2018. "Monetary policy shocks, inflation persistence, and long memory," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 117-127.
    42. Becker Ralf & Clements Adam E & Hurn Stan, 2011. "Semi-Parametric Forecasting of Realized Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-23, May.
    43. Luis A. Gil-Alana & Yadollah Dadgar & Rouhollah Nazari, 2019. "Iranian inflation: peristence and structural breaks," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(2), pages 398-408, April.

  14. Jurgen A. Doornik & Marius Ooms, 2001. "Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models," Economics Papers 2001-W27, Economics Group, Nuffield College, University of Oxford.

    Cited by:

    1. Marius Ooms & M. Angeles Carnero & Siem Jan Koopman, 2004. "Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices," Econometric Society 2004 Australasian Meetings 158, Econometric Society.
    2. Chevillon, Guillaume, 2009. "Multi-step forecasting in emerging economies: An investigation of the South African GDP," International Journal of Forecasting, Elsevier, vol. 25(3), pages 602-628, July.
    3. McHale, I.G. & Peel, D.A., 2010. "Habit and long memory in UK lottery sales," Economics Letters, Elsevier, vol. 109(1), pages 7-10, October.
    4. Henryk GURGUL & Tomasz WÓJTOWICZ, 2006. "Long Memory on the German Stock Exchange," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 56(09-10), pages 447-468, September.
    5. Shelton Peiris & Manabu Asai & Michael McAleer, 2016. "Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models," Tinbergen Institute Discussion Papers 16-044/III, Tinbergen Institute.
    6. Zevallos, Mauricio & Palma, Wilfredo, 2013. "Minimum distance estimation of ARFIMA processes," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 242-256.
    7. Martin, Gael M. & Nadarajah, K. & Poskitt, D.S., 2020. "Issues in the estimation of mis-specified models of fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 215(2), pages 559-573.
    8. Kubokawa, Tatsuya & Nagashima, Bui, 2012. "Parametric bootstrap methods for bias correction in linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 1-16.
    9. 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.
    10. 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.
    11. Geert Mesters & Siem Jan Koopman & Marius Ooms, 2011. "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models," Tinbergen Institute Discussion Papers 11-090/4, Tinbergen Institute.
    12. Manabu Asai & Shelton Peiris & Michael McAleer & David E. Allen, 2018. "Cointegrated Dynamics for A Generalized Long Memory Process: An Application to Interest Rates," Documentos de Trabajo del ICAE 2018-22, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    13. Poskitt, D.S. & Grose, Simone D. & Martin, Gael M., 2015. "Higher-order improvements of the sieve bootstrap for fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 188(1), pages 94-110.
    14. Charles S. Bos, 2003. "Time Series Modelling using TSMod 3.24," Tinbergen Institute Discussion Papers 03-091/4, Tinbergen Institute.
    15. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana, 2024. "Persistence and long memory in monetary policy spreads," Applied Economics, Taylor & Francis Journals, vol. 56(20), pages 2422-2433, April.
    16. Blazej Mazur, 2015. "Density forecasts based on disaggregate data: nowcasting Polish inflation," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 71-87.
    17. Palma, Wilfredo & Bondon, Pascal & Tapia, José, 2008. "Assessing influence in Gaussian long-memory models," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4487-4501, May.
    18. S. Lardic & V. Mignon, 2003. "The exact minimum likelihood estimation of ARFIMA processes and model selection criteria: A Monte Carlo study," THEMA Working Papers 2003-06, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    19. N. H. Chan & A. E. Brockwell, 2006. "Long-memory dynamic Tobit models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(5), pages 351-367.
    20. Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles, 2007. "Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 16-27, March.
    21. Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute.
    22. Stelios Arvanitis & Antonis Demos, 2014. "A Class of Indirect Inference Estimators: Higher Order Asymptotics and Approximate Bias Correction (Revised)," DEOS Working Papers 1411, Athens University of Economics and Business, revised 23 Sep 2014.
    23. Gutierrez-Barroso Josue & Báez-García Alberto Javier & Flores-Muñoz Francisco & Ruiz Medina Luis Javier & Trujillo González Juan Vianney & Padrón-Armas Ana Goretty, 2024. "Google Trends of political parties in Europe: a fractal exploration," Central European Journal of Public Policy, Sciendo, vol. 18(1), pages 24-36.
    24. Rebecca J. Sela & Clifford M. Hurvich, 2009. "Computationally efficient methods for two multivariate fractionally integrated models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 631-651, November.
    25. Ko, Kyungduk & Lee, Jaechoul & Lund, Robert, 2008. "Confidence intervals for long memory regressions," Statistics & Probability Letters, Elsevier, vol. 78(13), pages 1894-1902, September.
    26. 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.
    27. Proietti, Tommaso & Maddanu, Federico, 2024. "Modelling cycles in climate series: The fractional sinusoidal waveform process," Journal of Econometrics, Elsevier, vol. 239(1).
    28. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707, November.
    29. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Modified information criteria and selection of long memory time series models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 116-131.
    30. Alexander Ayertey Odonkor & Emmanuel Nkrumah Ababio & Emmanuel Amoah- Darkwah & Richard Andoh, 2022. "Stock Returns and Long-range Dependence," Global Business Review, International Management Institute, vol. 23(1), pages 37-47, February.
    31. Andreas Noack Jensen & Morten Ø. Nielsen, 2013. "A Fast Fractional Difference Algorithm," Working Paper 1307, Economics Department, Queen's University.
    32. Emmanuel Dubois & Sandrine Lardic & Valérie Mignon, 2004. "The Exact Maximum Likelihood-Based Test for Fractional Cointegration: Critical Values, Power and Size," Computational Economics, Springer;Society for Computational Economics, vol. 24(3), pages 239-255, July.
    33. Henryk Gurgul & Tomasz Wójtowicz, 2006. "Long-run properties of trading volume and volatility of equities listed in DJIA index," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 16(3-4), pages 29-56.
    34. 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.
    35. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    36. Pai, Jeffrey & Ravishanker, Nalini, 2015. "Fast approximate likelihood evaluation for stable VARFIMA processes," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 160-168.
    37. 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.
    38. Flores-Muñoz, Francisco & Báez-García, Alberto Javier & Gutiérrez-Barroso, Josué, 2019. "Fractional differencing in stock market price and online presence of global tourist corporations," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 24(48), pages 194-204.
    39. Vasyl Golosnoy & Yarema Okhrin, 2015. "Using information quality for volatility model combinations," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 1055-1073, June.
    40. Shapour Mohammadi & Ahmad Pouyanfar, 2011. "Behaviour of stock markets' memories," Applied Financial Economics, Taylor & Francis Journals, vol. 21(3), pages 183-194.
    41. Barndorff-Nielsen, Ole E. & Shephard, Neil, 2006. "Impact of jumps on returns and realised variances: econometric analysis of time-deformed Levy processes," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 217-252.
    42. Jos'e Igor Morlanes, 2017. "Mixed Models as an Alternative to Farima," Papers 1712.03044, arXiv.org.
    43. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Nicola Rubino & Inmaculada Vilchez, 2024. "Modelling Loans to Non-Financial Corporations in the Eurozone: A Long-Memory Approach," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 30(3), pages 231-254, August.
    44. Silva, E.M. & Franco, G.C. & Reisen, V.A. & Cruz, F.R.B., 2006. "Local bootstrap approaches for fractional differential parameter estimation in ARFIMA models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1002-1011, November.
    45. Dima, Bogdan & Dima, Ştefana Maria, 2017. "Mutual information and persistence in the stochastic volatility of market returns: An emergent market example," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 36-59.
    46. 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.
    47. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2020. "Modelling Loans to Non-Financial Corporations within the Eurozone: A Long-Memory Approach," CESifo Working Paper Series 8674, CESifo.
    48. Doornik Jurgen A & Ooms Marius, 2004. "Inference and Forecasting for ARFIMA Models With an Application to US and UK Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-25, May.
    49. Fraire, Francisco & Leatham, David J., 2006. "Decision Making Tool to Hedge Exchange Rate Risk," 2006 Agricultural and Rural Finance Markets in Transition, October 2-3, 2006, Washington, DC 133082, Regional Research Committee NC-1014: Agricultural and Rural Finance Markets in Transition.
    50. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2014. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 10/14, Monash University, Department of Econometrics and Business Statistics.

  15. Jurgen A. Doornik & Marius Ooms, 2000. "Multimodality and the GARCH Likelihood," Econometric Society World Congress 2000 Contributed Papers 0798, Econometric Society.

    Cited by:

    1. B. D. McCullough & H. D. Vinod, 2003. "Verifying the Solution from a Nonlinear Solver: A Case Study," American Economic Review, American Economic Association, vol. 93(3), pages 873-892, June.
    2. Eric Hillebrand & Gunther Schnabl, 2004. "The Effects of Japanese Foreign Exchange Intervention: GARCH Estimation and Change Point Detection," International Finance 0410008, University Library of Munich, Germany.
    3. Laurini, Márcio Poletti & Portugal, Marcelo Savino, 2004. "Long memory in the R$ / US$ exchange rate: A robust analysis," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 24(1), May.
    4. Kwami Adanu, 2006. "Optimizing the Garch Model–An Application of Two Global and Two Local Search Methods," Computational Economics, Springer;Society for Computational Economics, vol. 28(3), pages 277-290, October.
    5. Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
    6. Hwang. S. & Pedro L. Valls Pereira, 2003. "Small Sample Properties of GARCH Estimates and Persistence," Finance Lab Working Papers flwp_48, Finance Lab, Insper Instituto de Ensino e Pesquisa.
    7. Henrik Amilon, 2002. "A Score Test for Discreteness in GARCH Models," Research Paper Series 76, Quantitative Finance Research Centre, University of Technology, Sydney.
    8. Amilon, Henrik, 2003. "GARCH estimation and discrete stock prices: an application to low-priced Australian stocks," Economics Letters, Elsevier, vol. 81(2), pages 215-222, November.
    9. Yi-Chi Chen, 2013. "The Dynamics of Interbank Rate Behavior Under Alternative Monetary Regimes: The Case of Hong Kong," China Economic Policy Review (CEPR), World Scientific Publishing Co. Pte. Ltd., vol. 2(02), pages 1-21.

  16. Marius Ooms & Björn de Groot & Siem Jan Koopman, 1999. "Time-Series Modelling of Daily Tax Revenues," Computing in Economics and Finance 1999 312, Society for Computational Economics.

    Cited by:

    1. Clive G. Bowsher & Roland Meeks, 2006. "High Dimensional Yield Curves: Models and Forecasting," OFRC Working Papers Series 2006fe11, Oxford Financial Research Centre.
    2. Cabrero, Alberto & Camba-Méndez, Gonzalo & Hirsch, Astrid & Nieto, Fernando, 2002. "Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank," Working Paper Series 142, European Central Bank.
    3. Barend Abeln & Jan P.A.M. Jacobs, 2021. "COVID-19 and seasonal adjustment," CAMA Working Papers 2021-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Barend Abeln & Jan P.A.M. Jacobs & Machiel Mulder, 2022. "Seasonal adjustment of daily data with CAMPLET," CIRANO Working Papers 2022s-06, CIRANO.
    5. Ollech, Daniel, 2018. "Seasonal adjustment of daily time series," Discussion Papers 41/2018, Deutsche Bundesbank.
    6. Robert Ambrisko, 2022. "Nowcasting Macroeconomic Variables Using High-Frequency Fiscal Data," Working Papers 2022/5, Czech National Bank.
    7. 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.
    8. Siem Jan Koopman & Marius Ooms, 2004. "Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models," Tinbergen Institute Discussion Papers 04-135/4, Tinbergen Institute.
    9. Clive G. Bowsher & Roland Meeks, 2008. "The dynamics of economics functions: modelling and forecasting the yield curve," Working Papers 0804, Federal Reserve Bank of Dallas.
    10. Eliana González & Luis F. Melo & Luis E. Rojas & Brayan Rojas, 2010. "Estimations of the natural rate of interest in Colombia," Borradores de Economia 626, Banco de la Republica de Colombia.
    11. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.

  17. Ooms, M. & Doornik, J.A., 1999. "Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK inflation," Econometric Institute Research Papers EI 9947/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Jussi Tolvi, 2003. "Long memory and outliers in stock market returns," Applied Financial Economics, Taylor & Francis Journals, vol. 13(7), pages 495-502.
    2. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 517-539, November.
    3. Bhansali, R. J. & Kokoszka, P. S., 2002. "Computation of the forecast coefficients for multistep prediction of long-range dependent time series," International Journal of Forecasting, Elsevier, vol. 18(2), pages 181-206.
    4. Morana, Claudio, 2000. "Measuring core inflation in the euro area," Working Paper Series 36, European Central Bank.
    5. 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.
    6. Arielle Beyaert, 2004. "Fractional Output Convergence, with an Application to Nine Developed Countries," Econometric Society 2004 Australasian Meetings 280, Econometric Society.
    7. Jurgen A. Doornik & Marius Ooms, 2001. "Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average Models," Economics Papers 2001-W27, Economics Group, Nuffield College, University of Oxford.
    8. Müller-Kademann Christian, 2015. "Internal Validation of Temporal Disaggregation: A Cloud Chamber Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(3), pages 298-319, June.
    9. Emma Iglesias & Garry Phillips, 2005. "Analysing one-month Euro-market interest rates by fractionally integrated models," Applied Financial Economics, Taylor & Francis Journals, vol. 15(2), pages 95-106.
    10. Isao Ishida & Toshiaki Watanabe, 2009. "Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model," CARF F-Series CARF-F-145, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    11. Laura Mayoral, 2005. "The Persistence of Inflation in OECD Countries:a Fractionally Integrated Approach," Working Papers 259, Barcelona School of Economics.
    12. 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.
    13. Emmanuel Dubois & Sandrine Lardic & Valérie Mignon, 2004. "The Exact Maximum Likelihood-Based Test for Fractional Cointegration: Critical Values, Power and Size," Computational Economics, Springer;Society for Computational Economics, vol. 24(3), pages 239-255, July.
    14. Vasco J. Gabriel & Luis F. Martins, 2000. "The Forecast Performance of Long Memory and Markov Switching Models," NIPE Working Papers 2/2000, NIPE - Universidade do Minho.
    15. Morana Claudio, 2002. "Common Persistent Factors in Inflation and Excess Nominal Money Growth and a New Measure of Core Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-40, November.
    16. Abidin Ozdemir, Zeynel & Fisunoglu, Mahir, 2008. "On the inflation-uncertainty hypothesis in Jordan, Philippines and Turkey: A long memory approach," International Review of Economics & Finance, Elsevier, vol. 17(1), pages 1-12.

  18. Franses, Ph.H.B.F. & Ooms, M. & Bos, C.S., 1998. "Long memory and level shifts: re-analysing inflation rates," Econometric Institute Research Papers EI 9811, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    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. Nasr, Adnen Ben & Lux, Thomas & Ajm, Ahdi Noomen & Gupta, Rangan, 2014. "Forecasting the volatility of the dow jones islamic stock market index: Long memory vs. regime switching," Economics Working Papers 2014-07, Christian-Albrechts-University of Kiel, Department of Economics.
    3. 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.
    4. Nobay, A. Robert & Paya, Ivan & Peel, David A., 2007. "Inflation dynamics in the US - a nonlinear perspective," LSE Research Online Documents on Economics 24499, London School of Economics and Political Science, LSE Library.
    5. 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.
    6. Boubaker Heni & Canarella Giorgio & Gupta Rangan & Miller Stephen M., 2017. "Time-varying persistence of inflation: evidence from a wavelet-based approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    7. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 517-539, November.
    8. Claudio Morana, 2007. "A structural common factor approach to core inflation estimation and forecasting," Applied Economics Letters, Taylor & Francis Journals, vol. 14(3), pages 163-169.
    9. Carlos Barros & Luis Gil-Alana, 2012. "Inflation forecasting in Angola: a fractional approach," CEsA Working Papers 103, CEsA - Centre for African and Development Studies.
    10. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    11. Gilles Dufrénot & Dominique Guegan & Anne Peguin-Feissolle, 2005. "Long-memory dynamics in a SETAR model - Applications to stock markets," Post-Print halshs-00179339, HAL.
    12. 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.
    13. 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.
    14. Giorgio Canarella & Stephen M. Miller, 2016. "Inflation Persistence and Structural Breaks: The Experience of Inflation Targeting Countries and the US," Working papers 2016-11, University of Connecticut, Department of Economics.
    15. 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.
    16. Belkhouja, Mustapha & Mootamri, Imene, 2016. "Long memory and structural change in the G7 inflation dynamics," Economic Modelling, Elsevier, vol. 54(C), pages 450-462.
    17. Morana, Claudio, 2000. "Measuring core inflation in the euro area," Working Paper Series 36, European Central Bank.
    18. 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.
    19. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415, November.
    20. Massimiliano Caporin & Rangan Gupta, 2014. "Time-Varying Persistence in US Inflation," Working Papers 201457, University of Pretoria, Department of Economics.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. McMillan, David G. & Wohar, Mark E., 2010. "Persistence and time-varying coefficients," Economics Letters, Elsevier, vol. 108(1), pages 85-88, July.
    26. Charles S. Bos, 2003. "Time Series Modelling using TSMod 3.24," Tinbergen Institute Discussion Papers 03-091/4, Tinbergen Institute.
    27. Ahdi Ajmi & Adnen Ben Nasr & Mohamed Boutahar, 2008. "Seasonal Nonlinear Long Memory Model for the US Inflation Rates," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 243-254, April.
    28. 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).
    29. Sibbertsen, Philipp & Wenger, Kai & Wingert, Simon, 2020. "Testing for Multiple Structural Breaks in Multivariate Long Memory Time Series," Hannover Economic Papers (HEP) dp-676, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    30. Bos, Charles S. & Franses, Philip Hans & Ooms, Marius, 2002. "Inflation, forecast intervals and long memory regression models," International Journal of Forecasting, Elsevier, vol. 18(2), pages 243-264.
    31. 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.
    32. 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.
    33. Guglielmo M. Caporale & Luis A. Gil‐Alana, 2004. "Testing for Seasonal Fractional Roots in German Real Output," German Economic Review, Verein für Socialpolitik, vol. 5(3), pages 319-333, August.
    34. Laura Mayoral, 2005. "The Persistence of Inflation in OECD Countries:a Fractionally Integrated Approach," Working Papers 259, Barcelona School of Economics.
    35. 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.
    36. 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.
    37. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2020. "Modeling US historical time-series prices and inflation using alternative long-memory approaches," Empirical Economics, Springer, vol. 58(4), pages 1491-1511, April.
    38. Georgios KOURETAS & Mark E. WOHAR, 2010. "The Dynamics of Inflation: A Study of a Large Number of Countries," EcoMod2010 259600096, EcoMod.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. Chih-Chiang Hsu, 2000. "Long Memory or Structural Change: Testing Method and Empirical Examination," Econometric Society World Congress 2000 Contributed Papers 0867, Econometric Society.
    45. Luis A. Gil-Alana & Andrea Mervar & James E. Payne, 2017. "The stationarity of inflation in Croatia: anti-inflation stabilization program and the change in persistence," Economic Change and Restructuring, Springer, vol. 50(1), pages 45-58, February.
    46. Hwang, Eunju & Shin, Dong Wan, 2015. "A CUSUMSQ test for structural breaks in error variance for a long memory heterogeneous autoregressive model," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 167-176.
    47. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2007. "Long Run and Cyclical Dynamics in the US Stock Market," CESifo Working Paper Series 2046, CESifo.
    48. 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.
    49. Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
    50. Franses, Philip Hans & Janssens, Eva, 2018. "Inflation in Africa, 1960–2015," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 57(C), pages 261-292.
    51. 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.
    52. Narayan, Paresh Kumar & Popp, Stephan, 2011. "An application of a new seasonal unit root test to inflation," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 707-716, October.
    53. Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2018. "Long-Memory Modeling and Forecasting: Evidence from the U.S. Historical Series of Inflation," Working Papers 201869, University of Pretoria, Department of Economics.
    54. Jesús Clemente & María Dolores Gadea & Antonio Montañés & Marcelo Reyes, 2017. "Structural Breaks, Inflation and Interest Rates: Evidence from the G7 Countries," Econometrics, MDPI, vol. 5(1), pages 1-17, February.
    55. Carlos Barros & Guglielmo Maria Caporale & Luis Gil-Alana, 2014. "Long Memory in Angolan Macroeconomic Series: Mean Reversion versus Explosive Behaviour," African Development Review, African Development Bank, vol. 26(1), pages 59-73.
    56. 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.
    57. Baillie, Richard T. & Morana, Claudio, 2012. "Adaptive ARFIMA models with applications to inflation," Economic Modelling, Elsevier, vol. 29(6), pages 2451-2459.
    58. Heni Boubaker, 2016. "A Comparative Study of the Performance of Estimating Long-Memory Parameter Using Wavelet-Based Entropies," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 693-731, December.
    59. Rinke, Saskia & Busch, Marie & Leschinski, Christian, 2017. "Long Memory, Breaks, and Trends: On the Sources of Persistence in Inflation Rates," Hannover Economic Papers (HEP) dp-584, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    60. 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.
    61. Reisen, Valdério Anselmo & Monte, Edson Zambon & da Conceição Franco, Glaura & Sgrancio, Adriano Marcio & Molinares, Fábio Alexander Fajardo & Bondon, Pascal & Ziegelmann, Flávio Augusto & Abraham, Bo, 2018. "Robust estimation of fractional seasonal processes: Modeling and forecasting daily average SO2 concentrations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 146(C), pages 27-43.
    62. 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.
    63. Cleomar Gomes da Silva & Maria Carolina da Silva Leme, 2008. "Inflation and Interest Rate: Which one is more persistent in Brazil?," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807181224190, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    64. Vasco J. Gabriel & Luis F. Martins, 2000. "The Forecast Performance of Long Memory and Markov Switching Models," NIPE Working Papers 2/2000, NIPE - Universidade do Minho.
    65. Reisen Valderio A & Cribari-Neto Francisco & Jensen Mark J, 2003. "Long Memory Inflationary Dynamics: The Case of Brazil," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(3), pages 1-18, October.
    66. G. K. Randolph TAN, 2004. "Long Memory in Import and Export Price Inflation and Persistence of Shocks to the Terms of Trade," Econometric Society 2004 Far Eastern Meetings 732, Econometric Society.
    67. Adnen Ben Nasr & Mohamed Boutahar & Abdelwahed Trabelsi, 2010. "Fractionally integrated time varying GARCH model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(3), pages 399-430, August.
    68. Sibbertsen, Philipp & Venetis, Ioannis, 2003. "Distinguishing between long-range dependence and deterministic trends," Technical Reports 2003,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    69. 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.
    70. Morana Claudio, 2002. "Common Persistent Factors in Inflation and Excess Nominal Money Growth and a New Measure of Core Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-40, November.
    71. Baillie, Richard T. & Cho, Dooyeon & Rho, Seunghwa, 2024. "Combining Long and Short Memory in Time Series Models: the Role of Asymptotic Correlations of the MLEs," Econometrics and Statistics, Elsevier, vol. 29(C), pages 88-112.
    72. 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.
    73. 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.
    74. Conrad Christian & Karanasos Menelaos, 2005. "Dual Long Memory in Inflation Dynamics across Countries of the Euro Area and the Link between Inflation Uncertainty and Macroeconomic Performance," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-38, December.
    75. Luis A. Gil-Alana & Yadollah Dadgar & Rouhollah Nazari, 2019. "Iranian inflation: peristence and structural breaks," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(2), pages 398-408, April.

  19. Ooms, M. & Franses, Ph.H.B.F., 1998. "A seasonal periodic long memory model for monthly river flows," Econometric Institute Research Papers EI 9842, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Siem Jan Koopman & Marius Ooms, 2001. "Time Series Modelling of Daily Tax Revenues," Tinbergen Institute Discussion Papers 01-032/4, Tinbergen Institute.

  20. Ooms, M., 1995. "Flexible Seasonal Long Memory and Economic Time Series," Econometric Institute Research Papers EI 9515-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. McCoy, E. J. & Stephens, D. A., 2004. "Bayesian time series analysis of periodic behaviour and spectral structure," International Journal of Forecasting, Elsevier, vol. 20(4), pages 713-730.
    2. Guglielmo Caporale & Luis Gil-Alana, 2008. "Testing for unit and fractional orders of integration in the trend and seasonal components of US monetary aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 35(3), pages 241-253, July.
    3. Guglielmo Maria Caporale & Luis A. Gil‐Alana, 2007. "Nonlinearities and Fractional Integration in the US Unemployment Rate," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(4), pages 521-544, August.
    4. Sun, Jingwei & Shi, Wendong, 2014. "Aggregation of the generalized fractional processes," Economics Letters, Elsevier, vol. 124(2), pages 258-262.
    5. Johan Lyhagen, 2001. "The effect of precautionary saving on consumption in Sweden," Applied Economics, Taylor & Francis Journals, vol. 33(5), pages 673-681.
    6. Ooms, M. & Franses, Ph.H.B.F., 1998. "A seasonal periodic long memory model for monthly river flows," Econometric Institute Research Papers EI 9842, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Reisen, Valderio Anselmo & Rodrigues, Alexandre L. & Palma, Wilfredo, 2006. "Estimation of seasonal fractionally integrated processes," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 568-582, January.
    8. Wilfredo Palma & Ngai Hang Chan, 2005. "Efficient Estimation of Seasonal Long‐Range‐Dependent Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(6), pages 863-892, November.
    9. Javier De Peña & Luis A. Gil-Alana, 2003. "Testing of Nonstationary Cycles in Financial Time Series Data," Faculty Working Papers 15/03, School of Economics and Business Administration, University of Navarra.
    10. Voges, Michelle & Sibbertsen, Philipp, 2021. "Cyclical fractional cointegration," Econometrics and Statistics, Elsevier, vol. 19(C), pages 114-129.
    11. Brandon Whitcher, 2000. "Wavelet-Based Estimation Procedures For Seasonal Long-Memory Models," Computing in Economics and Finance 2000 148, Society for Computational Economics.
    12. Ben Nasr, Adnen & Trabelsi, Abdelwahed, 2005. "Seasonal and Periodic Long Memory Models in the In�ation Rates," MPRA Paper 22690, University Library of Munich, Germany, revised 03 Feb 2006.
    13. 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.
    14. C. Bisognin & S. R. C. Lopes, 2007. "Estimating and forecasting the long-memory parameter in the presence of periodicity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(6), pages 405-427.

Articles

  1. G. Mesters & S. J. Koopman & M. Ooms, 2016. "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 659-687, April.
    See citations under working paper version above.
  2. 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.

    Cited by:

    1. Belkhouja, Mustapha & Mootamri, Imene, 2016. "Long memory and structural change in the G7 inflation dynamics," Economic Modelling, Elsevier, vol. 54(C), pages 450-462.
    2. 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.
    3. Goliński, Adam & Zaffaroni, Paolo, 2016. "Long memory affine term structure models," Journal of Econometrics, Elsevier, vol. 191(1), pages 33-56.
    4. M. Shelton Peiris & Manabu Asai, 2016. "Generalized Fractional Processes with Long Memory and Time Dependent Volatility Revisited," Econometrics, MDPI, vol. 4(3), pages 1-21, September.
    5. Granville, Brigitte & Zeng, Ning, 2019. "Time variation in inflation persistence: New evidence from modelling US inflation," Economic Modelling, Elsevier, vol. 81(C), pages 30-39.
    6. Chu Shiou-Yen & Shane Christopher, 2017. "Using the hybrid Phillips curve with memory to forecast US inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-16, September.
    7. Todea, Alexandru, 2016. "Cross-correlations between volatility, volatility persistence and stock market integration: the case of emergent stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 87(C), pages 208-215.
    8. Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2020. "On generalized bivariate student-t Gegenbauer long memory stochastic volatility models with leverage: Bayesian forecasting of cryptocurrencies with a focus on Bitcoin," Econometrics and Statistics, Elsevier, vol. 16(C), pages 69-90.
    9. Rinke, Saskia & Busch, Marie & Leschinski, Christian, 2017. "Long Memory, Breaks, and Trends: On the Sources of Persistence in Inflation Rates," Hannover Economic Papers (HEP) dp-584, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    10. 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.
    11. 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.
    12. Nima Nonejad, 2019. "Has the 2008 financial crisis and its aftermath changed the impact of inflation on inflation uncertainty in member states of the european monetary union?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(2), pages 246-276, May.
    13. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.

  3. Irma Hindrayanto & John A.D. Aston & Siem Jan Koopman & Marius Ooms, 2013. "Modelling trigonometric seasonal components for monthly economic time series," Applied Economics, Taylor & Francis Journals, vol. 45(21), pages 3024-3034, July.
    See citations under working paper version above.
  4. Dordonnat, Virginie & Koopman, Siem Jan & Ooms, Marius, 2012. "Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3134-3152.

    Cited by:

    1. Marie Bessec & Julien Fouquau, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," Post-Print hal-01644930, HAL.
    2. Caston Sigauke & Murendeni Maurel Nemukula & Daniel Maposa, 2018. "Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models," Energies, MDPI, vol. 11(9), pages 1-21, August.
    3. Rodríguez Caballero, Carlos Vladimir, 2017. "Estimation of a Dynamic Multilevel Factor Model with possible long-range dependence," DES - Working Papers. Statistics and Econometrics. WS 24614, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Komi Nagbe & Jairo Cugliari & Julien Jacques, 2018. "Short-Term Electricity Demand Forecasting Using a Functional State Space Model," Energies, MDPI, vol. 11(5), pages 1-24, May.
    5. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
    6. Kamal Chapagain & Somsak Kittipiyakul & Pisut Kulthanavit, 2020. "Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand," Energies, MDPI, vol. 13(10), pages 1-29, May.
    7. Shahriyar Mukhtarov & Jeyhun I. Mikayilov & Sugra Humbatova & Vugar Muradov, 2020. "Do High Oil Prices Obstruct the Transition to Renewable Energy Consumption?," Sustainability, MDPI, vol. 12(11), pages 1-16, June.
    8. Yunus Emre Ergemen, 2022. "Parametric Estimation of Long Memory in Factor Models," CREATES Research Papers 2022-10, Department of Economics and Business Economics, Aarhus University.
    9. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    10. Antoniadis, Anestis & Brossat, Xavier & Cugliari, Jairo & Poggi, Jean-Michel, 2016. "A prediction interval for a function-valued forecast model: Application to load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 939-947.

  5. Commandeur, Jacques J. F. & Koopman, Siem Jan & Ooms, Marius, 2011. "Statistical Software for State Space Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i01).

    Cited by:

    1. Alexander Dokumentov & Rob J. Hyndman, 2015. "STR: A Seasonal-Trend Decomposition Procedure Based on Regression," Monash Econometrics and Business Statistics Working Papers 13/15, Monash University, Department of Econometrics and Business Statistics.
    2. Zietz, Joachim & Traian, Anca, 2014. "When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 271-281.
    3. Christoph F. Kurz & Martin Rehm & Rolf Holle & Christina Teuner & Michael Laxy & Larissa Schwarzkopf, 2019. "The effect of bariatric surgery on health care costs: A synthetic control approach using Bayesian structural time series," Health Economics, John Wiley & Sons, Ltd., vol. 28(11), pages 1293-1307, November.
    4. Gómez, Victor, 2015. "SSMMATLAB: A Set of MATLAB Programs for the Statistical Analysis of State Space Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i09).
    5. Weigand Roland & Wanger Susanne & Zapf Ines, 2018. "Factor Structural Time Series Models for Official Statistics with an Application to Hours Worked in Germany," Journal of Official Statistics, Sciendo, vol. 34(1), pages 265-301, March.
    6. Gabriele Fiorentini & Enrique Sentana, 2016. "Neglected serial correlation tests in UCARIMA models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 121-178, March.
    7. Alexander Dokumentov & Rob J. Hyndman, 2022. "STR: Seasonal-Trend Decomposition Using Regression," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 50-62, April.
    8. Qian, Hang, 2015. "Inequality Constrained State Space Models," MPRA Paper 66447, University Library of Munich, Germany.
    9. Jacques Peeperkorn & Yudhvir Seetharam, 2016. "A learning-augmented approach to pricing risk in South Africa," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 6(1), pages 117-139, April.
    10. 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.
    11. Riccardo “Jack” Lucchetti & Francesco Valentini, 2024. "Linear models with time-varying parameters: a comparison of different approaches," Computational Statistics, Springer, vol. 39(7), pages 3523-3545, December.
    12. Jong-Min Kim & Bainwen Sun & Sunghae Jun, 2019. "Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    13. Jokivuolle, Esa & Tölö, Eero & Virén, Matti, 2015. "Do banks’ overnight borrowing rates lead their CDS Price? Evidence from the Eurosystem," Working Paper Series 1809, European Central Bank.

  6. Hindrayanto, Irma & Koopman, Siem Jan & Ooms, Marius, 2010. "Exact maximum likelihood estimation for non-stationary periodic time series models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2641-2654, November.

    Cited by:

    1. Alj, Abdelkamel & Jónasson, Kristján & Mélard, Guy, 2016. "The exact Gaussian likelihood estimation of time-dependent VARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 633-644.
    2. 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.
    3. Boshnakov, Georgi N. & Lambert-Lacroix, Sophie, 2012. "A periodic Levinson-Durbin algorithm for entropy maximization," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 15-24, January.
    4. Thornton, Michael A., 2013. "Removing seasonality under a changing regime: Filtering new car sales," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 4-14.
    5. Abdelkamel Alj & Christophe Ley & Guy Melard, 2015. "Asymptotic Properties of QML Estimators for VARMA Models with Time-Dependent Coefficients: Part I," Working Papers ECARES ECARES 2015-21, ULB -- Universite Libre de Bruxelles.
    6. Milenković, Miloš S. & Bojović, Nebojša J. & Švadlenka, Libor & Melichar, Vlastimil, 2015. "A stochastic model predictive control to heterogeneous rail freight car fleet sizing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 162-198.
    7. Dordonnat, Virginie & Koopman, Siem Jan & Ooms, Marius, 2012. "Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3134-3152.

  7. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2009. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 683-713, October.
    See citations under working paper version above.
  8. Doornik, Jurgen A. & Ooms, Marius, 2008. "Multimodality in GARCH regression models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 432-448.
    See citations under working paper version above.
  9. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
    See citations under working paper version above.
  10. Siem Jan Koopman & Marius Ooms & André Lucas & Kees van Montfort & Victor Van Der Geest, 2008. "Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 104-130, February.
    See citations under working paper version above.
  11. Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles, 2007. "Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 16-27, March.
    See citations under working paper version above.
  12. Marius Ooms & Jurgen A. Doornik, 2006. "Econometric software development: past, present and future," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(2), pages 206-224, May.

    Cited by:

    1. Franses, Ph.H.B.F. & van Dijk, D.J.C., 2009. "Cointegration in a historical perspective," Econometric Institute Research Papers EI 2009-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Ooms, M., 2008. "Trends in Applied Econometrics Software Development 1985-2008, an analysis of Journal of Applied Econometrics research articles, software reviews, data and code," Serie Research Memoranda 0021, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    3. Roger Koenker & Achim Zeileis, 2009. "On reproducible econometric research," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 833-847.

  13. Koopman, Siem Jan & Ooms, Marius, 2006. "Forecasting daily time series using periodic unobserved components time series models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 885-903, November.
    See citations under working paper version above.
  14. Doornik Jurgen A & Ooms Marius, 2004. "Inference and Forecasting for ARFIMA Models With an Application to US and UK Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-25, May.

    Cited by:

    1. da Silva, Cleomar Gomes & Leme, Maria Carolina da Silva, 2011. "An Analysis of the Degrees of Persistence of Inflation, Inflation Expectations and Real Interest Rate in Brazil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 65(3), September.
    2. 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.
    3. Till Weigt & Bernd Wilfling, 2016. "A new combination approach to reducing forecast errors with an application to volatility forecasting," CQE Working Papers 4616, Center for Quantitative Economics (CQE), University of Muenster.
    4. Anders Eriksson & Daniel P. A. Preve & Jun Yu, 2019. "Forecasting Realized Volatility Using a Nonnegative Semiparametric Model," JRFM, MDPI, vol. 12(3), pages 1-23, August.
    5. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 517-539, November.
    6. Carlos Barros & Luis Gil-Alana, 2012. "Inflation forecasting in Angola: a fractional approach," CEsA Working Papers 103, CEsA - Centre for African and Development Studies.
    7. 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.
    8. Bello, Omar & Cantú, Fernando & Heresi, Rodrigo, 2011. "Latin America: variability and persistence in commodity prices," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), April.
    9. Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2016. "Time series analysis of volatility in the petroleum pricing markets: the persistence, asymmetry and jumps in the returns series," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 40(3), pages 235-262, September.
    10. 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).
    11. Verena Monschang & Bernd Wilfling, 2022. "A procedure for upgrading linear-convex combination forecasts with an application to volatility prediction," CQE Working Papers 9722, Center for Quantitative Economics (CQE), University of Muenster.
    12. Heejoon Han & Myung D. Park & Shen Zhang, 2015. "A Multiplicative Error Model with Heterogeneous Components for Forecasting Realized Volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 209-219, April.
    13. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana, 2024. "Persistence and long memory in monetary policy spreads," Applied Economics, Taylor & Francis Journals, vol. 56(20), pages 2422-2433, April.
    14. Laura Mayoral, 2005. "The Persistence of Inflation in OECD Countries:a Fractionally Integrated Approach," Working Papers 259, Barcelona School of Economics.
    15. Jurgen A. Doornik & Marius Ooms, 2003. "Multimodality in the GARCH Regression Model," Economics Papers 2003-W20, Economics Group, Nuffield College, University of Oxford.
    16. 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.
    17. Roxana Chiriac & Valeri Voev, 2011. "Modelling and forecasting multivariate realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 922-947, September.
    18. Ulrich K. Müller & Mark W. Watson, 2016. "Measuring Uncertainty about Long-Run Predictions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1711-1740.
    19. Stefanos Kechagias & Vladas Pipiras, 2020. "Modeling bivariate long‐range dependence with general phase," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 268-292, March.
    20. Evans, Mark, 2011. "Steel consumption and economic activity in the UK: The integration and cointegration debate," Resources Policy, Elsevier, vol. 36(2), pages 97-106, June.
    21. Morten Ø. Nielsen & Per Houmann Frederiksen, 2008. "Fully Modified Narrow-band Least Squares Estimation Of Stationary Fractional Cointegration," Working Paper 1171, Economics Department, Queen's University.
    22. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707, November.
    23. Aviral Kumar Tiwari & Claudiu T Albulescu & Phouphet Kyophilavong, 2014. "A comparison of different forecasting models of the international trade in India," Economics Bulletin, AccessEcon, vol. 34(1), pages 420-429.
    24. 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.
    25. Cheung, Yin-Wong & Chung, Sang-Kuck, 2009. "A Long Memory Model with Mixed Normal GARCH for US Inflation Data," Santa Cruz Department of Economics, Working Paper Series qt94r403d2, Department of Economics, UC Santa Cruz.
    26. 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.
    27. Härdle, Wolfgang Karl & Mungo, Julius, 2008. "Value-at-risk and expected shortfall when there is long range dependence," SFB 649 Discussion Papers 2008-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    28. 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.
    29. 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.
    30. Lee Jihyun & Kim Tong S & Lee Hoe Kyung, 2010. "Return-Volatility Relationship in High Frequency Data: Multiscale Horizon Dependency," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(1), pages 1-43, December.
    31. Jorge V Pérez-Rodríguez & María Santana-Gallego, 2020. "Modelling tourism receipts and associated risks, using long-range dependence models," Tourism Economics, , vol. 26(1), pages 70-96, February.
    32. Cleomar Gomes da Silva & Maria Carolina da Silva Leme, 2008. "Inflation and Interest Rate: Which one is more persistent in Brazil?," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807181224190, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    33. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Nicola Rubino & Inmaculada Vilchez, 2024. "Modelling Loans to Non-Financial Corporations in the Eurozone: A Long-Memory Approach," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 30(3), pages 231-254, August.
    34. 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.
    35. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2020. "Modelling Loans to Non-Financial Corporations within the Eurozone: A Long-Memory Approach," CESifo Working Paper Series 8674, CESifo.
    36. 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.
    37. Guglielmo Maria Caporale & Marinko Skare, 2014. "Long Memory in UK Real GDP, 1851-2013: An ARFIMA-FIGARCH Analysis," Discussion Papers of DIW Berlin 1395, DIW Berlin, German Institute for Economic Research.
    38. Rodrigo Mariscal & Andrew Powell, 2012. "Forecasting Inflation Risks in Latin America: A Technical Note," Research Department Publications 4785, Inter-American Development Bank, Research Department.

  15. Bart Hobijn & Philip Hans Franses & Marius Ooms, 2004. "Generalizations of the KPSS‐test for stationarity," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(4), pages 483-502, November.

    Cited by:

    1. Jorge Andrés Tamayo Castaño, 2012. "Asimetrías en la demanda por trabajo en Colombia: el papel del ciclo económico," Borradores de Economia 9286, Banco de la Republica.
    2. Christopher F Baum & John Barkoulas, 2002. "Dynamics of Intra-EMS Interest Rate Linkages," Computing in Economics and Finance 2002 13, Society for Computational Economics.
    3. Nunzio Cappuccio & Diego Lubian, 2010. "The fragility of the KPSS stationarity test," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(2), pages 237-253, June.
    4. Ai, Xiaohui & Li, Wenbo V. & Liu, Guoqing, 2012. "Karhunen–Loeve expansions for the detrended Brownian motion," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1235-1241.
    5. Giorgio Canarella & Stephen M. Miller, 2016. "Inflation Persistence and Structural Breaks: The Experience of Inflation Targeting Countries and the US," Working papers 2016-11, University of Connecticut, Department of Economics.
    6. Joseph Ross, 2021. "Stationarity Statistics on Rolling Windows," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 655-691, February.
    7. Maria Christisou & Theodore Panagiotidis & Abhijit Sharma, 2013. "On the stationarity of per capita carbon dioxide emissions over a century," Discussion Paper Series 2013_02, Department of Economics, University of Macedonia, revised Dec 2013.
    8. Maria Christidou & Theodore Panagiotidis, 2010. "Purchasing Power Parity and the European Single Currency: Some New Evidence," Koç University-TUSIAD Economic Research Forum Working Papers 1018, Koc University-TUSIAD Economic Research Forum.
    9. Sephton, Peter S., 2019. "El Niño, La Niña, and a cup of Joe," Energy Economics, Elsevier, vol. 84(C).
    10. Vasco J. Gabriel, 2001. "Tests for the Null Hypothesis of Cointegration: a Monte Carlo Comparison," NIPE Working Papers 7/2001, NIPE - Universidade do Minho.
    11. Ismail H. GENC & Anil RUPASINGHA, 2009. "Time-series Tests of Stochastic Earnings Convergence across US Nonmetropolitan Counties, 1969-2004," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 9(2).
    12. P. S. Sephton, 2012. "Breaking deterministics, test size and the efficient Wald test for fractional unit roots," Applied Economics Letters, Taylor & Francis Journals, vol. 19(1), pages 83-85, January.
    13. Paul Alagidede & Theodore Panagiotidis & Xu Zhang, 2010. "Causal Relationship between Stock Prices and Exchange Rates," Discussion Paper Series 2010_01, Department of Economics, University of Macedonia, revised Jan 2010.
    14. Caner, M. & Kilian, L., 2001. "Size distortions of tests of the null hypothesis of stationarity: evidence and implications for the PPP debate," Journal of International Money and Finance, Elsevier, vol. 20(5), pages 639-657, October.
    15. Corrado, L. & Martin, R. & Weeks, M., 2004. "Identifying and Interpreting Convergence Clusters Across Europe," Cambridge Working Papers in Economics 0414, Faculty of Economics, University of Cambridge.
    16. Andersen, Torben G. & Varneskov, Rasmus T., 2021. "Consistent inference for predictive regressions in persistent economic systems," Journal of Econometrics, Elsevier, vol. 224(1), pages 215-244.
    17. Emma Iglesias & Garry Phillips, 2005. "Analysing one-month Euro-market interest rates by fractionally integrated models," Applied Financial Economics, Taylor & Francis Journals, vol. 15(2), pages 95-106.
    18. Vincent Bouvatier, 2006. "Hot money inflows in China: How the people's bank of China took up the challenge," Cahiers de la Maison des Sciences Economiques bla06011, Université Panthéon-Sorbonne (Paris 1).
    19. Dawar Hama Khalid Mohammed & Fatih Külahcı & Ahmad Muhammed, 2021. "Determination of possible responses of Radon-222, magnetic effects, and total electron content to earthquakes on the North Anatolian Fault Zone, Turkiye: an ARIMA and Monte Carlo Simulation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 2493-2512, September.
    20. Yongtao Guan, 2008. "A KPSS Test for Stationarity for Spatial Point Processes," Biometrics, The International Biometric Society, vol. 64(3), pages 800-806, September.
    21. Schröder, Michael & Hüfner, Felix P., 2002. "Exchange rate pass-through to consumer prices: a European perspective," ZEW Discussion Papers 02-20, ZEW - Leibniz Centre for European Economic Research.
    22. Kapetanios, George, 2009. "Testing for strict stationarity in financial variables," Journal of Banking & Finance, Elsevier, vol. 33(12), pages 2346-2362, December.
    23. Ghassan, Hassan B. & Alhajhoj, Hassan R. & Balli, Faruk, 2020. "Bi-Demographic and Current Account Dynamics using SVAR Model: Evidence from Saudi Arabia," MPRA Paper 109772, University Library of Munich, Germany, revised Jun 2021.
    24. Jamie Emerson, 2023. "Education, employment, and labor force participation in the United States," Economics Bulletin, AccessEcon, vol. 43(3), pages 1377-1388.
    25. Rashid Sbia & Muhammad Shahbaz & Ilhan Ozturk, 2017. "Economic growth, financial development, urbanisation and electricity consumption nexus in UAE," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 30(1), pages 527-549, January.
    26. Zhijie Xiao & Luiz Renato Lima, 2007. "Testing Covariance Stationarity," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 643-667.
    27. Cho, Cheol-Keun & Amsler, Christine & Schmidt, Peter, 2015. "A test of the null of integer integration against the alternative of fractional integration," Journal of Econometrics, Elsevier, vol. 187(1), pages 217-237.
    28. Lee Kian Lim & Michael McAleer, 2003. "Convergence and Catching Up in ASEAN: A Comparative Analysis," CIRJE F-Series CIRJE-F-218, CIRJE, Faculty of Economics, University of Tokyo.
    29. Jushan Bai & Serena Ng, 2001. "A New Look at Panel Testing of Stationarity and the PPP Hypothesis," Economics Working Paper Archive 467, The Johns Hopkins University,Department of Economics.
    30. Lüders, Erik & Lüders-Amann, Inge & Schröder, Michael, 2004. "The Power Law and Dividend Yields," ZEW Discussion Papers 04-51, ZEW - Leibniz Centre for European Economic Research.
    31. Rehim Kılıç & Patrick McCarthy, 2012. "Long-run equilibrium and short-run dynamics between risk exposure and highway safety," Empirical Economics, Springer, vol. 42(3), pages 899-913, June.
    32. Ghassan, Hassan & Alhajhoj, Hassan R. & Balli, Faruk, 2018. "Bi-Demographic Changes and Current Account using SVAR Modeling: Evidence from Saudi Arabia," MPRA Paper 93013, University Library of Munich, Germany, revised 01 Feb 2019.
    33. Sephton, Peter, 2023. "Threshold cointegration and asymmetries between dividends and earnings news," Finance Research Letters, Elsevier, vol. 58(PB).
    34. Sephton, Peter & Mann, Janelle, 2018. "Gold and crude oil prices after the great moderation," Energy Economics, Elsevier, vol. 71(C), pages 273-281.
    35. Landajo, Manuel & Presno, María José, 2010. "Nonparametric pseudo-Lagrange multiplier stationarity testing," MPRA Paper 25659, University Library of Munich, Germany.
    36. Hassan Belkacem Ghassan & Hassan Rafdan Al-Hajhoj & Faruk Balli, 2019. "Bi-Demographic Changes and Current Account using SVAR Modeling: Evidence from Saudi Economy," Working Papers hal-01742574, HAL.
    37. 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.
    38. Manuel Landajo & María José Presno, 2010. "Stationarity testing under nonlinear models. Some asymptotic results," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 392-405, September.
    39. Nazlioglu, Saban & Karul, Cagin, 2017. "A panel stationarity test with gradual structural shifts: Re-investigate the international commodity price shocks," Economic Modelling, Elsevier, vol. 61(C), pages 181-192.
    40. Jönsson, Kristian, 2006. "Testing Stationarity in Small and Medium-Sized Samples when Disturbances are Serially Correlated," Working Papers 2006:20, Lund University, Department of Economics, revised 09 Nov 2009.
    41. Ahmad Zubaidi Baharumshah & Aliyu Alhaji Jibrilla & Abdalla Sirag & Hamisu Sadi Ali & Ibrahim Muye Muhammad, 2016. "Public Revenue-Expenditure Nexus in South Africa: Are there Asymmetries?," South African Journal of Economics, Economic Society of South Africa, vol. 84(4), pages 520-537, December.
    42. Eduard Baumöhl & Štefan Lyócsa & Tomáš Výrost, 2011. "Volatility Regimes in Macroeconomic Time Series: The Case of the Visegrad Group," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(6), pages 530-544, December.
    43. Lee, Jin & Lee, Young Im, 2012. "Size improvement of the KPSS test using sieve bootstraps," Economics Letters, Elsevier, vol. 116(3), pages 483-486.
    44. Peter Sephton & Janelle Mann, 2015. "Nonlinear attractors and asymmetries between non-life insurance premiums and financial markets," Empirical Economics, Springer, vol. 49(3), pages 783-799, November.
    45. Baumöhl, Eduard & Lyócsa, Štefan, 2012. "Constructing weekly returns based on daily stock market data: A puzzle for empirical research?," MPRA Paper 43431, University Library of Munich, Germany.
    46. Giorgio Canarella & Stephen M Miller, 2017. "Inflation Persistence Before and After Inflation Targeting: A Fractional Integration Approach," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(1), pages 78-103, January.
    47. George Kapetanios, 2007. "Testing for Strict Stationarity," Working Papers 602, Queen Mary University of London, School of Economics and Finance.
    48. Markus Eberhardt & Francis Teal, 2009. "A Common Factor Approach to Spatial Heterogeneity in Agricultural Productivity Analysis," CSAE Working Paper Series 2009-05, Centre for the Study of African Economies, University of Oxford.
    49. Bordo, Michael D. & Duca, John V., 2023. "How the new fed municipal bond facility capped municipal-treasury yield spreads in the Covid-19 recession," Journal of the Japanese and International Economies, Elsevier, vol. 67(C).
    50. Taylor, Nicholas, 2008. "Can idiosyncratic volatility help forecast stock market volatility?," International Journal of Forecasting, Elsevier, vol. 24(3), pages 462-479.
    51. Sephton, Peter S., 2022. "Revisiting the inflation-hedging properties of precious metals in Africa," Resources Policy, Elsevier, vol. 77(C).
    52. Bart Hobijn & Philip Hans Franses, 2000. "Asymptotically perfect and relative convergence of productivity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 59-81.
    53. Yilmazkuday, Hakan, 2009. "Inflation Targeting and Inflation Convergence within Turkey," MPRA Paper 16770, University Library of Munich, Germany.
    54. Jönsson, Kristian, 2006. "Finite-Sample Stability of the KPSS Test," Working Papers 2006:23, Lund University, Department of Economics.
    55. Tamayo Castaño, Jorge Andrés, 2012. "Asimetrías en la demanda por trabajo en Colombia : el papel del ciclo económico," Chapters, in: Arango-Thomas, Luis Eduardo & Hamann-Salcedo, Franz Alonso (ed.), El mercado de trabajo en Colombia : hechos, tendencias e instituciones, chapter 12, pages 487-542, Banco de la Republica de Colombia.
    56. Ferrer-Pérez, H. & Ayuda, M.I. & Aznar, A., 2017. "A comparison of two modified stationarity tests. A Monte Carlo study," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 134(C), pages 28-36.
    57. L.A. Gil-Alana, 2003. "Testing the Power of a Generalization of the KPSS-Tests against Fractionally Integrated Hypotheses," Computational Economics, Springer;Society for Computational Economics, vol. 22(1), pages 23-38, August.
    58. Bordo, Michael D. & Duca, John V., 2022. "How new Fed corporate bond programs cushioned the Covid-19 recession," Journal of Banking & Finance, Elsevier, vol. 136(C).
    59. Marco G. Ercolani & Zheng Wei, 2010. "An Empirical Analysis of the Lewis-Ranis-FEi Theory of Dualistic Economic Development for China," Discussion Papers 10-06, Department of Economics, University of Birmingham.
    60. Lyócsa, Štefan & Baumöhl, Eduard, 2012. "Testing the covariance stationarity of CEE stocks," MPRA Paper 43432, University Library of Munich, Germany.
    61. Mann, Janelle & Sephton, Peter, 2019. "A (negative) replication of ‘The relationship between energy consumption, energy prices, and economic growth: Time series evidence from Asian developing countries’ (Energy Economics, 2000)," Energy Economics, Elsevier, vol. 82(C), pages 78-84.
    62. Hassan B. Ghassan & Hassan R. Al-Hajhoj & Faruk Balli, 2018. "Bi-Demographic Changes and Current Account using SVAR Modeling," Papers 1803.11161, arXiv.org, revised Mar 2019.
    63. Daiki Maki, 2008. "The Performance of Variance Ratio Unit Root Tests Under Nonlinear Stationary TAR and STAR Processes: Evidence from Monte Carlo Simulations and Applications," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 77-94, February.
    64. Peter Sephton, 2017. "Finite Sample Critical Values of the Generalized KPSS Stationarity Test," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 161-172, June.
    65. Alper Kara & Dilem Yildirim & G. Ipek Tunc, 2023. "Market efficiency in non-renewable resource markets: evidence from stationarity tests with structural changes," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 279-290, June.
    66. Marios Poulos, 2016. "Determining the Stationarity Distance via a Reversible Stochastic Process," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-23, October.
    67. Alper Kara & Dilem Yıldırım & Gül İpek Tunç, 2021. "Market Efficiency In Non-Renewable Resource Markets: Evidence From Stationarity Tests With Structural Changes," ERC Working Papers 2103, ERC - Economic Research Center, Middle East Technical University, revised Apr 2021.
    68. Koch, Cathérine Tahmee, 2014. "Risky adjustments or adjustments to risks: Decomposing bank leverage," Journal of Banking & Finance, Elsevier, vol. 45(C), pages 242-254.
    69. P. S. Sephton, 2010. "Unit roots and purchasing power parity: another kick at the can," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3439-3453.
    70. Ulrich K. Müller, 2002. "Size and Power of Tests for Stationarity in Highly Autocorrelated Time Series," University of St. Gallen Department of Economics working paper series 2002 2002-26, Department of Economics, University of St. Gallen.
    71. Lee Kian Lim, 2000. "Convergence and Catching Up in South-East Asia: A Comparative Analysis," Econometric Society World Congress 2000 Contributed Papers 1844, Econometric Society.
    72. Baumöhl, Eduard, 2013. "Stock market integration between the CEE-4 and the G7 markets: Asymmetric DCC and smooth transition approach," MPRA Paper 43834, University Library of Munich, Germany.
    73. Ismail Genc & Jon Miller & Anil Rupasingha, 2011. "Stochastic convergence tests for US regional per capita personal income; some further evidence: a research note," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(2), pages 369-377, April.

  16. Doornik, Jurgen A. & Ooms, Marius, 2003. "Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 333-348, March.
    See citations under working paper version above.
  17. Siem Jan Koopman & Marius Ooms, 2003. "Time Series Modelling of Daily Tax Revenues," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(4), pages 439-469, November.
    See citations under working paper version above.
  18. Bos, Charles S. & Franses, Philip Hans & Ooms, Marius, 2002. "Inflation, forecast intervals and long memory regression models," International Journal of Forecasting, Elsevier, vol. 18(2), pages 243-264.
    See citations under working paper version above.
  19. Eisinga, Rob & Franses, Philip Hans & Ooms, Marius, 1999. "Forecasting long memory left-right political orientations," International Journal of Forecasting, Elsevier, vol. 15(2), pages 185-199, April.

    Cited by:

    1. Elliot Tonkes & Dharma Lesmono, 2010. "Consistency in the US Congressional Popular Opinion Polls and Prediction Markets," Journal of Prediction Markets, University of Buckingham Press, vol. 4(2), pages 45-64, September.
    2. Goodell, John W. & McGroarty, Frank & Urquhart, Andrew, 2015. "Political uncertainty and the 2012 US presidential election: A cointegration study of prediction markets, polls and a stand-out expert," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 162-171.
    3. Laura Mayoral & Juan J. Dolado & Jesús Gonzalo, 2003. "Long-range dependence in Spanish political opinion poll series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(2), pages 137-155.
    4. Lesmono, Dharma & Tonkes, Elliot & Burrage, Kevin, 2009. "Opportunistic timing and manipulation in Australian Federal Elections," European Journal of Operational Research, Elsevier, vol. 192(2), pages 677-691, January.
    5. Pérez, Ana, 2001. "Modelos de memoria larga para series económicas y financieras," DES - Documentos de Trabajo. Estadística y Econometría. DS ds010101, Universidad Carlos III de Madrid. Departamento de Estadística.

  20. Philip Hans Franses & Marius Ooms & Charles S. Bos, 1999. "Long memory and level shifts: Re-analyzing inflation rates," Empirical Economics, Springer, vol. 24(3), pages 427-449.
    See citations under working paper version above.
  21. Marius Ooms, 1999. "Review of SsfPack 2.2: statistical algorithms for models in state space," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 161-166.

    Cited by:

    1. Francisco Cribari-Neto & Spyros Zarkos, 2003. "Econometric and Statistical Computing Using Ox," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 277-295, June.

  22. Ooms, Marius & Franses, Philip Hans, 1997. "On Periodic Correlations between Estimated Seasonal and Nonseasonal Components in German and U.S. Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 470-481, October.

    Cited by:

    1. Marius Ooms & M. Angeles Carnero & Siem Jan Koopman, 2004. "Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices," Econometric Society 2004 Australasian Meetings 158, Econometric Society.
    2. Franses, Ph.H.B.F. & de Bruin, P., 1999. "Seasonal adjustment and the business cycle in unemployment," Econometric Institute Research Papers EI 9923-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Pami Dua & Lokendra Kumawat, 2005. "Modelling and Forecasting Seasonality in Indian Macroeconomic Time Series," Working papers 136, Centre for Development Economics, Delhi School of Economics.
    4. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2006. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Tinbergen Institute Discussion Papers 06-101/4, Tinbergen Institute.
    5. Ooms, M. & Franses, Ph.H.B.F., 1998. "A seasonal periodic long memory model for monthly river flows," Econometric Institute Research Papers EI 9842, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Raymund Abara, 2006. "Estimation and evaluation of asset pricing models with habit formation using Philippine data," Applied Economics Letters, Taylor & Francis Journals, vol. 13(8), pages 493-497.
    7. Siem Jan Koopman & Philip Hans Franses, 2002. "Constructing Seasonally Adjusted Data with Time‐varying Confidence Intervals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(5), pages 509-526, December.

  23. Franses, Philip Hans & Ooms, Marius, 1997. "A periodic long-memory model for quarterly UK inflation," International Journal of Forecasting, Elsevier, vol. 13(1), pages 117-126, March.

    Cited by:

    1. Laurent Ferrara & Dominique Guegan & Zhiping Lu, 2010. "Testing Fractional Order of Long Memory Processes: A Monte Carlo Study," Post-Print hal-00486655, HAL.
    2. Marius Ooms & M. Angeles Carnero & Siem Jan Koopman, 2004. "Periodic Heteroskedastic RegARFIMA models for daily electricity spot prices," Econometric Society 2004 Australasian Meetings 158, Econometric Society.
    3. Yin-Wong Cheung & Sang-Kuck Chung, 2011. "A Long Memory Model with Normal Mixture GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 38(4), pages 517-539, November.
    4. Carlos Barros & Luis Gil-Alana, 2012. "Inflation forecasting in Angola: a fractional approach," CEsA Working Papers 103, CEsA - Centre for African and Development Studies.
    5. Guy P. Nason & Ben Powell & Duncan Elliott & Paul A. Smith, 2017. "Should we sample a time series more frequently?: decision support via multirate spectrum estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 353-407, February.
    6. 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.
    7. Laurent Ferrara & Dominique Guegan, 2008. "Business surveys modelling with Seasonal-Cyclical Long Memory models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00283710, HAL.
    8. Ahdi Ajmi & Adnen Ben Nasr & Mohamed Boutahar, 2008. "Seasonal Nonlinear Long Memory Model for the US Inflation Rates," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 243-254, April.
    9. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    10. Laura Mayoral, 2005. "The Persistence of Inflation in OECD Countries:a Fractionally Integrated Approach," Working Papers 259, Barcelona School of Economics.
    11. John Barkoulas & Christopher F. Baum, 2003. "Long-Memory Forecasting of U.S. Monetary Indices," Boston College Working Papers in Economics 558, Boston College Department of Economics.
    12. 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.
    13. Ooms, M. & Franses, Ph.H.B.F., 1998. "A seasonal periodic long memory model for monthly river flows," Econometric Institute Research Papers EI 9842, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Ye, Xunyu & Gao, Ping & Li, Handong, 2015. "Improving estimation of the fractionally differencing parameter in the SARFIMA model using tapered periodogram," Economic Modelling, Elsevier, vol. 46(C), pages 167-179.
    15. Carlos Barros & Guglielmo Maria Caporale & Luis Gil-Alana, 2014. "Long Memory in Angolan Macroeconomic Series: Mean Reversion versus Explosive Behaviour," African Development Review, African Development Bank, vol. 26(1), pages 59-73.
    16. Bensalma, Ahmed, 2018. "Two Distinct Seasonally Fractionally Differenced Periodic Processes," MPRA Paper 84969, University Library of Munich, Germany.
    17. Fernando Zarzosa Valdivia, 2020. "Inflation Dynamics in the ABC (Argentina, Brazil and Chile) countries," Ensayos de Política Económica, Departamento de Investigación Francisco Valsecchi, Facultad de Ciencias Económicas, Pontificia Universidad Católica Argentina., vol. 3(2), pages 77-99, Octubre.
    18. Ben Nasr, Adnen & Trabelsi, Abdelwahed, 2005. "Seasonal and Periodic Long Memory Models in the In�ation Rates," MPRA Paper 22690, University Library of Munich, Germany, revised 03 Feb 2006.
    19. Pérez, Ana, 2001. "Modelos de memoria larga para series económicas y financieras," DES - Documentos de Trabajo. Estadística y Econometría. DS ds010101, Universidad Carlos III de Madrid. Departamento de Estadística.
    20. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    21. Łukasz Lenart, 2017. "Examination of Seasonal Volatility in HICP for Baltic Region Countries: Non-Parametric Test versus Forecasting Experiment," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 9(1), pages 29-67, March.
    22. G. K. Randolph TAN, 2004. "Long Memory in Import and Export Price Inflation and Persistence of Shocks to the Terms of Trade," Econometric Society 2004 Far Eastern Meetings 732, Econometric Society.
    23. Dominique Guegan, 2003. "A prospective study of the k-factor Gegenbauer processes with heteroscedastic errors and an application to inflation rates," Post-Print halshs-00201314, HAL.
    24. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    25. Guglielmo Maria Caporale & Marinko Skare, 2014. "Long Memory in UK Real GDP, 1851-2013: An ARFIMA-FIGARCH Analysis," Discussion Papers of DIW Berlin 1395, DIW Berlin, German Institute for Economic Research.
    26. 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.
    27. Luis A. Gil-Alana & Yadollah Dadgar & Rouhollah Nazari, 2019. "Iranian inflation: peristence and structural breaks," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(2), pages 398-408, April.

  24. Ooms, Marius & Hassler, Uwe, 1997. "On the effect of seasonal adjustment on the log-periodogram regression," Economics Letters, Elsevier, vol. 56(2), pages 135-141, October.

    Cited by:

    1. Franses, Ph.H.B.F. & Ooms, M. & Bos, C.S., 1998. "Long memory and level shifts: re-analysing inflation rates," Econometric Institute Research Papers EI 9811, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Gil-Alaña, Luis A., 2000. "Deterministic seasonality versus seasonal fractional integration," SFB 373 Discussion Papers 2000,106, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    3. Gil-Alana, Luis & Lovcha, Yuliya & Pérez Laborda, Àlex, 2016. "On the invertibility of seasonally adjusted series," Working Papers 2072/261539, Universitat Rovira i Virgili, Department of Economics.
    4. Bos, Charles S. & Franses, Philip Hans & Ooms, Marius, 2002. "Inflation, forecast intervals and long memory regression models," International Journal of Forecasting, Elsevier, vol. 18(2), pages 243-264.
    5. Uwe Hassler & Francesc Marmol & C. Velasco, 2000. "Fractional Cointegrating Regression In The Presence Of Linear Time Trends," Computing in Economics and Finance 2000 138, Society for Computational Economics.
    6. Leschinski, Christian & Sibbertsen, Philipp, 2019. "Model order selection in periodic long memory models," Econometrics and Statistics, Elsevier, vol. 9(C), pages 78-94.
    7. Voges, Michelle & Leschinski, Christian & Sibbertsen, Philipp, 2017. "Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks," Hannover Economic Papers (HEP) dp-599, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    8. G. K. Randolph TAN, 2004. "Long Memory in Import and Export Price Inflation and Persistence of Shocks to the Terms of Trade," Econometric Society 2004 Far Eastern Meetings 732, Econometric Society.
    9. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

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