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Classical Estimation of Multivariate Markov-Switching Models using MSVARlib

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  • BENOIT BELLONE

    (OECD)

Abstract

This paper introduces an upgraded version of MSVARlib, a Gauss and Ox- Gauss compliant library, focusing on Multivariate Markov Switching Regressions in their most general specification. This new set of procedures allows to estimate, through classical optimization methods, models belonging to the MSI(M)(AH)-VARX ``intercept regime dependent'' family. This research enhances the first package MSVARlib 1.1, which has been deeply inspired by the works of Hamilton and Krolzig. Not to mention the extension to a generalized multivariate regression framework, it notably augments the range of models with a possibly unlimited finite number of Markov states, offers automatic or manual intialization procedures and adds new statistical tests. The first part of this article provides the basic theoretical grounds of the related Markov-switching models. Following sections give some illustrations of the programs through univariate and multivariate examples. One is based on a non-linear reading of the american unemployment rate. A second study is focused on coincident stochastic models of US recessions and slowdowns. The paper concludes on possible extensions and new applications. Detailed guidelines in appendices and tutorial programs are provided to help the reader handling the Gauss package and the joined replication files.

Suggested Citation

  • Benoit Bellone, 2005. "Classical Estimation of Multivariate Markov-Switching Models using MSVARlib," Econometrics 0508017, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:0508017
    Note: Type of Document - pdf; pages: 27. Gauss programs, compatible with 3.2 Versions or upper. A complete Gauss library to estimate MSVAR models or Markov switching regressions. Codes, data and programs available at http://bellone.ensae.net
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0508/0508017.pdf
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    13. Benoit Bellone & David Saint-Martin, 2004. "Detecting Turning Points with Many Predictors through Hidden Markov Models," Econometrics 0407001, University Library of Munich, Germany.
    14. Eva Andersson & David Bock & Marianne Frisén, 2004. "Detection of Turning Points in Business Cycles," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(1), pages 93-108.
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    Cited by:

    1. Roy H. Kwon & Jonathan Y. Li, 2016. "A stochastic semidefinite programming approach for bounds on option pricing under regime switching," Annals of Operations Research, Springer, vol. 237(1), pages 41-75, February.
    2. Klaus Abberger & Wolfgang Nierhaus, 2010. "Markov-Switching and the Ifo Business Climate: the Ifo Business Cycle Traffic Lights," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-13.
    3. Fabrizio Almeida Marodin & Marcelo Savino Portugal, 2019. "Exchange Rate Pass-Through in Brazil: À Markov Switching DSGE Estimation for the Inflation Targeting Period," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 36-66, March.
    4. BenSaïda, Ahmed & Litimi, Houda & Abdallah, Oussama, 2018. "Volatility spillover shifts in global financial markets," Economic Modelling, Elsevier, vol. 73(C), pages 343-353.
    5. Benoît Bellone & Erwan Gautier & Sébastien Le Coent, 2006. "Les marchés financiers anticipent-ils les retournements conjoncturels ?," Economie & Prévision, La Documentation Française, vol. 172(1), pages 83-99.
    6. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    7. Ginters BUSS, 2010. "Forecasts With Single - Equation Markov - Switching Model: An Application To The Gross Domestic Product Of Latvia," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 5(2(12)/Sum), pages 48-58.
    8. Pardo, S. & Rautureau, N. & Vallée, T., 2011. "Optimal versus realized policy rules in a regime-switching framework," Economic Modelling, Elsevier, vol. 28(6), pages 2761-2775.
    9. Adél Bosch & Franz Ruch, 2013. "An Alternative Business Cycle Dating Procedure for South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 81(4), pages 491-516, December.
    10. Xiaowei Cai & Kyle Stiegert & Stephen Koontz, 2011. "Regime switching and oligopsony power: the case of U.S. beef processing," Agricultural Economics, International Association of Agricultural Economists, vol. 42(1), pages 99-109, January.
    11. Chambers, Robert G. & Tzouvelekas, Vangelis, 2013. "Estimating population dynamics without population data," Journal of Environmental Economics and Management, Elsevier, vol. 66(3), pages 510-522.
    12. Robert G. Chambers & Margarita Genius & Vangelis Tzouvelekas, 2021. "Invariant Risk Preferences and Supply Response under Price Risk," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(5), pages 1802-1819, October.
    13. Marie Adanero-Donderis & Olivier Darné & Laurent Ferrara, 2009. "Un indicateur probabiliste du cycle d'accélération pour l'économie française," Economie & Prévision, La Documentation Française, vol. 0(3), pages 95-114.
    14. Robert G. Chambers & Margarita Genius & Vangelis Tzouvelekas, 2012. "A Supply-Response Model Under Invariant Risk Preferences," Working Papers 1209, University of Crete, Department of Economics.
    15. Fuentes, Cesar A. & Rios, Ronald, 2014. "Non-explicit FOREX intervention: The role of the Central Reserve Bank in a dollarized economy and its effects on expectations from the “peso problem” perspective: The case of Peru," Journal of Business Research, Elsevier, vol. 67(4), pages 558-566.
    16. Roy Kwon & Jonathan Li, 2016. "A stochastic semidefinite programming approach for bounds on option pricing under regime switching," Annals of Operations Research, Springer, vol. 237(1), pages 41-75, February.
    17. Klaus Abberger & Wolfgang Nierhaus, 2008. "Markov Switching and the Ifo Business Climate," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 61(10), pages 25-30, May.
    18. Sulaiman Al-Abduljader & Imad Moosa, 2007. "A Test of the News Model of Stock Price Determination in an Emerging Market: The Case of Kuwait," Working Papers 710, Economic Research Forum, revised 01 Jan 2007.
    19. Benoît Bellone, 2006. "Une lecture probabiliste du cycle d’affaires américain," Économie et Prévision, Programme National Persée, vol. 172(1), pages 63-81.
    20. Adanero-Donderis , M. & Darné, O. & Ferrara, L., 2007. "Deux indicateurs probabilistes de retournement cyclique pour l’économie française," Working papers 187, Banque de France.

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    More about this item

    Keywords

    Multivariate Markov-Switching Regressions; Hidden markov Models; Non linear regressions; Open source Gauss library; Business cycle; EM algorithm; Kittagawa-Hamilton Filtering; Recession Detection Models; MSVAR; MS-VAR; Hamilton's Model; Krolzig MSVAR library; Filtered probabilities; Smoothed probabilities.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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