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Non-Markovian regime switching with endogenous states and time-varying state strengths

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Siddhartha Chib
Michael J. Dueker

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Abstract

This article presents a non-Markovian regime switching model in which the regime states depend on the sign of an autoregressive latent variable. The magnitude of the latent variable indexes the 'strength' of the state or how deeply the system is embedded in the current regime. In this model, regimes have dynamics, not only persistence, so that one regime can gradually give way to another. In this framework, it is natural to allow the autoregressive latent variable to be endogenous so that regimes are determined jointly with the observed data. We apply the model to GDP growth, as in Hamilton (1989), Albert and Chib (1993) and Filardo and Gordon (1998) to illustrate the relation of the regimes to NBER-dated recessions and the time-varying expected durations of regimes. The article makes use of the Metropolis-Hastings algorithm to make multi-move draws of the latent regime strength variable, where the extended Kalman filter provides a valid proposal density for the latent variable.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2004-030.

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Date of creation: 2004
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Handle: RePEc:fip:fedlwp:2004-030

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Keywords: Time-series analysis ; Business cycles;

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This paper has been announced in the following NEP Reports: References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Christopher A. Sims & Tao Zha, 2004. "Were there regime switches in U.S. monetary policy?," Working Paper 2004-14, Federal Reserve Bank of Atlanta. [Downloadable!]
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  2. Tiemen Woutersen & Robert M. de Jong, 2004. "Dynamic time series binary choice," Econometric Society 2004 North American Summer Meetings 365, Econometric Society. [Downloadable!]
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  3. 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.
  4. 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. [Downloadable!] (restricted)
  5. Pok-sang Lam, 2004. "A Markov-Switching Model Of Gnp Growth With Duration Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(1), pages 175-204, 02. [Downloadable!] (restricted)
  6. 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.
  7. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August. [Downloadable!]
  8. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May. [Downloadable!] (restricted)
  9. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
  10. Filardo, Andrew J. & Gordon, Stephen F., 1998. "Business cycle durations," Journal of Econometrics, Elsevier, vol. 85(1), pages 99-123, July. [Downloadable!] (restricted)
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  11. Chang-Jin Kim & Jeremy M. Piger & Richard Startz, 2004. "Estimation of Markov regime-switching regression models with endogenous switching," Working Papers 2003-015, Federal Reserve Bank of St. Louis. [Downloadable!]
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  1. Mark W. French, 2005. "A nonlinear look at trend MFP growth and the business cycle: result from a hybrid Kalman/Markov switching model," Finance and Economics Discussion Series 2005-12, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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