# 16-107/III (2016-12-06; 2017-10-13)

Didier Nibbering, Erasmus University Rotterdam, The Netherlands; Richard Paap, Erasmus University Rotterdam, The Netherlands; Michel van der Wel, Erasmus University Rotterdam, The Netherlands
Time-Varying Parameter Vector Autoregressive Model, Semi-parametric Bayesian Inference, Dirichlet Process Mixture Model, Hidden Markov Chain, Monetary Policy Analysis, Real-time Forecasting
JEL codes:
C11, C14, C32, C51, C54

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.