Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2016-12-06
Revision-Date: 2017-10-13
Number: 16-107/III
Author-Name: Didier Nibbering
Author-Email: nibbering@ese.eur.nl
Author-Workplace-Name: Erasmus University Rotterdam, The Netherlands
Author-Name: Richard Paap
Author-Email: paap@ese.eur.nl
Author-Workplace-Name: Erasmus University Rotterdam, The Netherlands
Author-Name: Michel van der Wel
Author-Email: vanderwel@ese.eur.nl
Author-Workplace-Name: Erasmus University Rotterdam, The Netherlands
Title: A Bayesian Infinite Hidden Markov Vector Autoregressive Model
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.
Classification-JEL: C11, C14, C32, C51, C54
Keywords: Time-Varying Parameter Vector Autoregressive Model, Semi-parametric Bayesian Inference, Dirichlet Process Mixture Model, Hidden Markov Chain, Monetary Policy Analysis, Real-time Forecasting
File-Url: https://papers.tinbergen.nl/16107.pdf
File-Format: application/pdf
File-Size: 940450 bytes
Handle: RePEc:tin:wpaper:20160107