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A Bayesian Infinite Hidden Markov Vector Autoregressive Model

Listed author(s):
  • Didier Nibbering

    ()

    (Erasmus University Rotterdam, The Netherlands)

  • Richard Paap

    ()

    (Erasmus University Rotterdam, The Netherlands)

  • Michel van der Wel

    ()

    (Erasmus University Rotterdam, The Netherlands)

We propose a Bayesian infinite hidden Markov model to estimate time-varying parameters in a vector autoregressive model. The Markov structure allows for heterogeneity over time while accounting for state-persistence. By modelling the transition distribution as a Dirichlet process mixture model, parameters can vary over potentially an infinite number of regimes. The Dirichlet process however favours a parsimonious model without imposing restrictions on the parameter space. An empirical application demonstrates the ability of the model to capture both smooth and abrupt parameter changes over time, and a real-time forecasting exercise shows excellent predictive performance even in large dimensional VARs.

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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 16-107/III.

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Date of creation: 06 Dec 2016
Date of revision: 13 Oct 2017
Handle: RePEc:tin:wpaper:20160107
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  15. Bauwens, Luc & Carpantier, Jean-François & Dufays, Arnaud, 2015. "Autoregressive moving average infinite hidden markov-switching models," CORE Discussion Papers 2015007, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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