A Bayesian Infinite Hidden Markov Vector Autoregressive Model

Didier Nibbering, Richard Paap and Michel van der Wel ()

No 16-107/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: Time-Varying Parameter Vector Autoregressive Model; Semi-parametric Bayesian Inference; Dirichlet Process Mixture Model; Hidden Markov Chain; Monetary Policy Analysis; Real-time Forecasting (search for similar items in EconPapers)
JEL-codes: C11 C14 C32 C51 C54 (search for similar items in EconPapers)
Date: 2016-12-06, Revised 2017-10-13
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20160107

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