IDEAS home Printed from https://ideas.repec.org/p/udt/wpecon/2016_04.html
   My bibliography  Save this paper

Maximum Likelihood Estimation in Possibly Misspeci ed Dynamic Models with Time-Inhomogeneous Markov Regimes

Author

Listed:
  • Demian Pouzo
  • Zacharias Psaradakis
  • Martin Sola

Abstract

This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency and local asymptotic normality of the ML estimator under general conditions which allow for autoregressive dynamics in the observable process, time-inhomogeneous Markov regime sequences, and possible model misspeci cation. A Monte Carlo study examines the nite-sample properties of the ML estimator. An empirical application is also discussed. Key words and phrases: Autoregressive model; consistency; hidden Markov model; Markov regimes; maximum likelihood; local asymptotic normality; misspeci ed models; time-inhomogenous Markov chain.

Suggested Citation

  • Demian Pouzo & Zacharias Psaradakis & Martin Sola, 2016. "Maximum Likelihood Estimation in Possibly Misspeci ed Dynamic Models with Time-Inhomogeneous Markov Regimes," Department of Economics Working Papers 2016_04, Universidad Torcuato Di Tella.
    • Handle: RePEc:udt:wpecon:2016_04
    as

    Download full text from publisher

    File URL: https://www.utdt.edu/download.php?fname=_150309052784567800.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alan J. Auerbach & Yuriy Gorodnichenko, 2012. "Measuring the Output Responses to Fiscal Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 4(2), pages 1-27, May.
    2. Chernozhukov, Victor & Fernández-Val, Iván & Hoderlein, Stefan & Holzmann, Hajo & Newey, Whitney, 2015. "Nonparametric identification in panels using quantiles," Journal of Econometrics, Elsevier, vol. 188(2), pages 378-392.
    3. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, Enero-Abr.
    4. Dueker, Michael J. & Psaradakis, Zacharias & Sola, Martin & Spagnolo, Fabio, 2011. "Multivariate contemporaneous-threshold autoregressive models," Journal of Econometrics, Elsevier, vol. 160(2), pages 311-325, February.
    5. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    6. Andrew J. Filardo, 1998. "Choosing information variables for transition probabilities in a time-varying transition probability Markov switching model," Research Working Paper 98-09, Federal Reserve Bank of Kansas City.
    7. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
    8. Bryan S. Graham & James L. Powell, 2012. "Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models," Econometrica, Econometric Society, vol. 80(5), pages 2105-2152, September.
    9. Alan J. Auerbach & Yuriy Gorodnichenko, 2013. "Corrigendum: Measuring the Output Responses to Fiscal Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 5(3), pages 320-322, August.
    10. White, Halbert, 1983. "Corrigendum [Maximum Likelihood Estimation of Misspecified Models]," Econometrica, Econometric Society, vol. 51(2), pages 513-513, March.
    11. Leena Kalliovirta & Mika Meitz & Pentti Saikkonen, 2015. "A Gaussian Mixture Autoregressive Model for Univariate Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 247-266, March.
    12. Dueker, Michael J. & Sola, Martin & Spagnolo, Fabio, 2007. "Contemporaneous threshold autoregressive models: Estimation, testing and forecasting," Journal of Econometrics, Elsevier, vol. 141(2), pages 517-547, December.
    13. Martín Solá & Zacharias Psaradakis & Fabio Spagnolo & Nicola Spagnolo, 2010. "Some Cautionary Results Concerning Markov-Switching Models with Time-Varying Transition Probabilities," Department of Economics Working Papers 2010-12, Universidad Torcuato Di Tella.
    14. Psaradakis, Zacharias & Sola, Martin, 1998. "Finite-sample properties of the maximum likelihood estimator in autoregressive models with Markov switching," Journal of Econometrics, Elsevier, vol. 86(2), pages 369-386, June.
    15. Chamberlain, Gary, 1992. "Efficiency Bounds for Semiparametric Regression," Econometrica, Econometric Society, vol. 60(3), pages 567-596, May.
    16. Joseph Tadjuidje Kamgaing & Hernando Ombao & Richard A. Davis, 2009. "Autoregressive processes with data‐driven regime switching," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(5), pages 505-533, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Psaradakis, Zacharias & Sola, Martin, 2024. "Markov-Switching Models with State-Dependent Time-Varying Transition Probabilities," Econometrics and Statistics, Elsevier, vol. 29(C), pages 49-63.
    2. repec:udt:wpecon:2017_1 is not listed on IDEAS
    3. Yonekura, Shouto & Beskos, Alexandros & Singh, Sumeetpal S., 2021. "Asymptotic analysis of model selection criteria for general hidden Markov models," Stochastic Processes and their Applications, Elsevier, vol. 132(C), pages 164-191.
    4. Feng Lingbing & Shi Yanlin, 2020. "Markov regime-switching autoregressive model with tempered stable distribution: simulation evidence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(1), pages 1-27, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Demian Pouzo & Zacharias Psaradakis & Martin Sola, 2022. "Maximum Likelihood Estimation in Markov Regime‐Switching Models With Covariate‐Dependent Transition Probabilities," Econometrica, Econometric Society, vol. 90(4), pages 1681-1710, July.
    2. Marie Bessec, 2019. "Revisiting the transitional dynamics of business cycle phases with mixed-frequency data," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 711-732, August.
    3. Yin, Ming, 2015. "Estimating Gaussian Mixture Autoregressive model with Sequential Monte Carlo algorithm: A parallel GPU implementation," MPRA Paper 88111, University Library of Munich, Germany, revised 2018.
    4. Meitz, Mika & Saikkonen, Pentti, 2021. "Testing for observation-dependent regime switching in mixture autoregressive models," Journal of Econometrics, Elsevier, vol. 222(1), pages 601-624.
    5. Kirstin Hubrich & Timo Teräsvirta, 2013. "Thresholds and Smooth Transitions in Vector Autoregressive Models," CREATES Research Papers 2013-18, Department of Economics and Business Economics, Aarhus University.
    6. Hamilton, J.D., 2016. "Macroeconomic Regimes and Regime Shifts," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 163-201, Elsevier.
    7. Marie Bessec, 2015. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Post-Print hal-01276824, HAL.
    8. repec:dau:papers:123456789/15246 is not listed on IDEAS
    9. Candelon, Bertrand & Lieb, Lenard, 2013. "Fiscal policy in good and bad times," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2679-2694.
    10. Ana Beatriz Galvão & Michael T. Owyang, 2018. "Financial Stress Regimes and the Macroeconomy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(7), pages 1479-1505, October.
    11. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, April.
    12. Peren Arin, K. & Koray, Faik & Spagnolo, Nicola, 2015. "Fiscal multipliers in good times and bad times," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 303-311.
    13. Irene Botosaru & Chris Muris, 2017. "Binarization for panel models with fixed effects," CeMMAP working papers 31/17, Institute for Fiscal Studies.
    14. Edmundo Inacio & Cassio Garcia Ribeiro & Carolina Troncoso Baltar & Rosangela Ballini & Yanchao Li, 2024. "The impact of Petrobras spending on economic cycles," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 23(3), pages 459-485, September.
    15. Graham, Bryan S. & Hahn, Jinyong & Poirier, Alexandre & Powell, James L., 2018. "A quantile correlated random coefficients panel data model," Journal of Econometrics, Elsevier, vol. 206(2), pages 305-335.
    16. Fatnassi, Ibrahim & Slim, Chaouachi & Ftiti, Zied & Ben Maatoug, Abderrazek, 2014. "Effects of monetary policy on the REIT returns: Evidence from the United Kingdom," Research in International Business and Finance, Elsevier, vol. 32(C), pages 15-26.
    17. Chen, Fei & Diebold, Francis X. & Schorfheide, Frank, 2013. "A Markov-switching multifractal inter-trade duration model, with application to US equities," Journal of Econometrics, Elsevier, vol. 177(2), pages 320-342.
    18. Gilles Dufrénot & Aurélia Jambois & Laurine Jambois & Guillaume Khayat, 2016. "Regime-Dependent Fiscal Multipliers in the United States," Open Economies Review, Springer, vol. 27(5), pages 923-944, November.
    19. Tuhin G M Al Mamun & Ehsanullah & Md Sharif Hassan & Mohd Faizal Yusof & Md Aminul Islam & Naharin Binte Rab, 2025. "Fiscal Policy and Household Savings in Central Europe (Poland, Croatia, and Slovak Republic) -- A Markov Switching VAR with Covid Shock," Papers 2502.14041, arXiv.org, revised Mar 2025.
    20. Oscar Jorda & Massimiliano Marcellino, "undated". "Stochastic Processes Subject To Time Scale Transformations: An Application To High-Frequency Fx Data," Department of Economics 00-02, California Davis - Department of Economics.
    21. Zacharias Psaradakis & Martin Sola & Nicola Spagnolo & Patricio Yunis, 2024. "Predictive Accuracy of Impulse Responses Estimated Using Local Projections and Vector Autoregressions," Department of Economics Working Papers 2024_02, Universidad Torcuato Di Tella.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:udt:wpecon:2016_04. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: María Cecilia Lafuente (email available below). General contact details of provider: https://edirc.repec.org/data/deutdar.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.