IDEAS home Printed from https://ideas.repec.org/a/taf/jnlbes/v35y2017i2p162-182.html
   My bibliography  Save this article

Autoregressive Moving Average Infinite Hidden Markov-Switching Models

Author

Listed:
  • Luc Bauwens
  • Jean-François Carpantier
  • Arnaud Dufays

Abstract

Markov-switching models are usually specified under the assumption that all the parameters change when a regime switch occurs. Relaxing this hypothesis and being able to detect which parameters evolve over time is relevant for interpreting the changes in the dynamics of the series, for specifying models parsimoniously, and may be helpful in forecasting. We propose the class of sticky infinite hidden Markov-switching autoregressive moving average models, in which we disentangle the break dynamics of the mean and the variance parameters. In this class, the number of regimes is possibly infinite and is determined when estimating the model, thus avoiding the need to set this number by a model choice criterion. We develop a new Markov chain Monte Carlo estimation method that solves the path dependence issue due to the moving average component. Empirical results on macroeconomic series illustrate that the proposed class of models dominates the model with fixed parameters in terms of point and density forecasts.

Suggested Citation

  • Luc Bauwens & Jean-François Carpantier & Arnaud Dufays, 2017. "Autoregressive Moving Average Infinite Hidden Markov-Switching Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(2), pages 162-182, April.
  • Handle: RePEc:taf:jnlbes:v:35:y:2017:i:2:p:162-182
    DOI: 10.1080/07350015.2015.1123636
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07350015.2015.1123636
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bauwens, Luc & Dufays, Arnaud & Rombouts, Jeroen V.K., 2014. "Marginal likelihood for Markov-switching and change-point GARCH models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 508-522.
    2. Markus Jochmann, 2015. "Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 537-558, May.
    3. Jin, Xin & Maheu, John M., 2016. "Bayesian semiparametric modeling of realized covariance matrices," Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
    4. Arnaud Dufays, 2014. "On the conjugacy of off-line and on-line Sequential Monte Carlo Samplers," Working Paper Research 263, National Bank of Belgium.
    5. Markus Haas, 2004. "A New Approach to Markov-Switching GARCH Models," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(4), pages 493-530.
    6. Ardia, David & Baştürk, Nalan & Hoogerheide, Lennart & van Dijk, Herman K., 2012. "A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3398-3414.
    7. 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.
    8. Goutte, Stéphane, 2014. "Conditional Markov regime switching model applied to economic modelling," Economic Modelling, Elsevier, vol. 38(C), pages 258-269.
    9. DUFAYS, Arnaud, 2012. "Infinite-state Markov-switching for dynamic volatility and correlation models," CORE Discussion Papers 2012043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Yong Song, 2014. "Modelling Regime Switching And Structural Breaks With An Infinite Hidden Markov Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 825-842, August.
    11. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    12. Sylvia Fruhwirth-Schnatter, 2004. "Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 143-167, June.
    13. Ajay Jasra & David A. Stephens & Arnaud Doucet & Theodoros Tsagaris, 2011. "Inference for Lévy‐Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(1), pages 1-22, March.
    14. Doornik, Jurgen A., 2013. "A Markov-switching model with component structure for US GNP," Economics Letters, Elsevier, vol. 118(2), pages 265-268.
    15. Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November.
    16. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
    17. Eo, Yunjong, 2012. "Bayesian Inference about the Types of Structural Breaks When There are Many Breaks," Working Papers 2012-05, University of Sydney, School of Economics.
    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. Jean Hindriks & Yukihiro Nishimura, 2017. "Equilibrium leadership in tax competition models with capital ownership: a rejoinder," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 24(2), pages 338-349, April.
    2. John M. Maheu & Yong Song, 2018. "An efficient Bayesian approach to multiple structural change in multivariate time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 251-270, March.
    3. Chiara Lattanzi & Manuele Leonelli, 2019. "A changepoint approach for the identification of financial extreme regimes," Papers 1902.09205, arXiv.org.
    4. DESCHAMPS, Philippe J., 2016. "Bayesian Semiparametric Forecasts of Real Interest Rate Data," CORE Discussion Papers 2016050, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Vrins, F. & Jeanblanc, M., 2015. "The [phi]-Martingale," CORE Discussion Papers 2015022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Dufays, A. & Rombouts, V., 2015. "Sparse Change-Point Time Series Models," CORE Discussion Papers 2015032, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Didier Nibbering & Richard Paap & Michel van der Wel, 2016. "A Bayesian Infinite Hidden Markov Vector Autoregressive Model," Tinbergen Institute Discussion Papers 16-107/III, Tinbergen Institute, revised 13 Oct 2017.
    8. Balandraud, Eric & Queyranne, Maurice & Tardella, Fabio, 2015. "Largest minimally inversion-complete and pair-complete sets of permutations," CORE Discussion Papers 2015009, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    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:taf:jnlbes:v:35:y:2017:i:2:p:162-182. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: http://www.tandfonline.com/UBES20 .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.