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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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 Bayesian Analysis (jel C11), Semiparametric and Nonparametric Methods (jel C14), Time-Series Models; Dynamic Quantile Regressions (jel C32), Model Construction and Estimation (jel C51)
Persistent URL hdl.handle.net/1765/97822
Series Tinbergen Institute Discussion Paper Series
Nibbering, D, Paap, R, & van der Wel, M. (2016). A Bayesian Infinite Hidden Markov Vector Autoregressive Model (No. 16-107/III). Tinbergen Institute Discussion Paper Series. Retrieved from http://hdl.handle.net/1765/97822