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A Bayesian Infinite Hidden Markov Vector Autoregressive Model

Author

Listed:
  • Didier Nibbering

    () (Erasmus University Rotterdam, The Netherlands)

  • Richard Paap

    () (Erasmus University Rotterdam, The Netherlands)

  • Michel van der Wel

    () (Erasmus University Rotterdam, The Netherlands)

Abstract

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.

Suggested Citation

  • Didier Nibbering & Richard Paap & Michel van der Wel, 2016. "A Bayesian Infinite Hidden Markov Vector Autoregressive Model," Tinbergen Institute Discussion Papers 16-107/III, Tinbergen Institute, revised 13 Oct 2017.
  • Handle: RePEc:tin:wpaper:20160107
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    File URL: http://papers.tinbergen.nl/16107.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Time-Varying Parameter Vector Autoregressive Model; Semi-parametric Bayesian Inference; Dirichlet Process Mixture Model; Hidden Markov Chain; Monetary Policy Analysis; Real-time Forecasting;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling

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