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Beta Autoregressive Transition Markov-Switching Models for Business Cycle Analysis

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  • Billio Monica

    ()
    (University of Venice)

  • Casarin Roberto

    ()
    (University of Venice)

Abstract

We propose a new class of Markov-switching models useful for business cycle analysis, with transition probabilities following independent beta autoregressive processes. We study the effects of the autoregressive dynamics on the regime duration. We propose a full Bayesian inference approach and particular attention is paid to the parameters of the latent beta autoregressive processes. We discuss the choice of the prior distributions and propose a Markov-chain Monte Carlo algorithm for estimating both the parameters and the latent variables. Finally, we provide an application to the Euro area business cycle.

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Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 15 (2011)
Issue (Month): 4 (September)
Pages: 1-32

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Handle: RePEc:bpj:sndecm:v:15:y:2011:i:4:n:2

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Cited by:
  1. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2012. "Combination schemes for turning point predictions," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 402-412.
  2. Adrian Pagan, 2013. "Patterns and Their Uses," NCER Working Paper Series 96, National Centre for Econometric Research.
  3. Roberto Casarin & Marco Tronzano & Domenico Sartore, 2013. "Bayesian Markov Switching Stochastic Correlation Models," Working Papers 2013:11, Department of Economics, University of Venice "Ca' Foscari".
  4. Adrian Pagan & Don Harding, 2011. "Econometric Analysis and Prediction of Recurrent Events," CREATES Research Papers 2011-33, School of Economics and Management, University of Aarhus.
  5. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Time-varying Combinations of Predictive Densities using Nonlinear Filtering," Tinbergen Institute Discussion Papers 12-118/III, Tinbergen Institute.

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