2016-12-08
A Bayesian Infinite Hidden Markov Vector Autoregressive Model
Publication
Publication
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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hdl.handle.net/1765/97822 | |
Tinbergen Institute Discussion Paper Series | |
Organisation | Erasmus School of Economics |
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 |