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Autoren: 
Nibbering, Didier
Paap, Richard
van der Wel, Michel
Datum: 
2016
Reihe/Nr.: 
Tinbergen Institute Discussion Paper 16-107/III
Zusammenfassung: 
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.
Schlagwörter: 
Time-Varying Parameter Vector Autoregressive Model
Semi-parametric Bayesian Inference
Dirichlet Process Mixture Model
Hidden Markov Chain
Monetary Policy Analysis
Real-time Forecasting
JEL: 
C11
C14
C32
C51
C54
Dokumentart: 
Working Paper
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