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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.

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File URL: http://128.118.178.162/eps/em/papers/0508/0508017.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0508017.

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Length: 27 pages
Date of creation: 19 Aug 2005
Date of revision:
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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Web page: http://128.118.178.162

Related research

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.;

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References

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  1. Hamilton, James D., 1996. "Specification testing in Markov-switching time-series models," Journal of Econometrics, Elsevier, vol. 70(1), pages 127-157, January.
  2. Benoit Bellone, 2004. "MSVARlib: a new Gauss library to estimate multivariate Hidden Markov Models," Econometrics 0406004, EconWPA.
  3. Philip Rothman, 1998. "Forecasting Asymmetric Unemployment Rates," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 164-168, February.
  4. LAURENT, Sébastien & URBAIN, Jean-Pierre, 2004. "Bridging the gap between Ox and Gauss using OxGauss," CORE Discussion Papers 2004012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
  6. James D. Hamilton, 2005. "What's Real About the Business Cycle?," NBER Working Papers 11161, National Bureau of Economic Research, Inc.
  7. Marine Carrasco & Liang Hu, 2004. "Optimal test for Markov switching," Econometric Society 2004 North American Summer Meetings 396, Econometric Society.
  8. Eva Andersson & David Bock & Marianne Frisén, 2004. "Detection of Turning Points in Business Cycles," Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2004(1), pages 93-108.
  9. 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 357-84, March.
  10. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
  11. Hansen, Bruce E., 1992. "Testing for parameter instability in linear models," Journal of Policy Modeling, Elsevier, vol. 14(4), pages 517-533, August.
  12. Sichel, Daniel E, 1994. "Inventories and the Three Phases of the Business Cycle," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 269-77, July.
  13. Benoit Bellone & David Saint-Martin, 2004. "Detecting Turning Points with Many Predictors through Hidden Markov Models," Econometrics 0407001, EconWPA.
  14. Ferrara, Laurent, 2003. "A three-regime real-time indicator for the US economy," Economics Letters, Elsevier, vol. 81(3), pages 373-378, December.
  15. Anas, Jacques & Ferrara, Laurent, 2002. "Un indicateur d'entrée et sortie de récession: application aux Etats-Unis
    [A start-end recession index: Application for United-States]
    ," MPRA Paper 4043, University Library of Munich, Germany.
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Citations

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Cited by:
  1. Robert Chambers & Margarita Genius & Vangelis Tzouvelekas, 2012. "A Supply-Response Model Under Invariant Risk Preferences," Working Papers 1209, University of Crete, Department of Economics.
  2. Adanero-Donderis , M. & Darné, O. & Ferrara, L., 2007. "Deux indicateurs probabilistes de retournement cyclique pour l’économie française," Working papers 187, Banque de France.
  3. Klaus Abberger & Wolfgang Nierhaus, 2008. "Markov-Switching und ifo Geschäftsklima," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 61(10), pages 25-30, 05.
  4. Benoît Bellone & Erwan Gautier & Sébastien Le Coent, 2006. "Les marchés financiers anticipent-ils les retournements conjoncturels ?," Économie et Prévision, Programme National Persée, vol. 172(1), pages 83-99.
  5. 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,CIRET, vol. 2010(2), pages 1-13.
  6. Laurent Ferrara & Olivier Darné & Marie Adanero-Donderis, 2009. "Un indicateur probabiliste du cycle d’accélération pour l’économie française," Économie et Prévision, Programme National Persée, vol. 189(3), pages 95-114.
  7. Benoît Bellone, 2006. "Une lecture probabiliste du cycle d’affaires américain," Économie et Prévision, Programme National Persée, vol. 172(1), pages 63-81.
  8. 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.
  9. 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.
  10. 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.

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