A Bayesian Infinite Hidden Markov Vector Autoregressive Model

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 16-107/III

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Semiparametric and Nonparametric Methods: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Construction and Estimation ; Quantitative Policy Modeling
• Subject: Time-Varying Parameter Vector Autoregressive Model ; Semi-parametric Bayesian Inference ; Dirichlet Process Mixture Model ; Hidden Markov Chain ; Monetary Policy Analysis ; Real-time Forecasting

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Nibbering, Didier ; Paap, Richard ; van der Wel, Michel
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/149511
• Last update: 10.03.2025, 11:41 AM CET