Arbeitspapier

A Bayesian Infinite Hidden Markov Vector Autoregressive Model

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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.

Sprache
Englisch

Erschienen in
Series: Tinbergen Institute Discussion Paper ; No. 16-107/III

Klassifikation
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
Thema
Time-Varying Parameter Vector Autoregressive Model
Semi-parametric Bayesian Inference
Dirichlet Process Mixture Model
Hidden Markov Chain
Monetary Policy Analysis
Real-time Forecasting

Ereignis
Geistige Schöpfung
(wer)
Nibbering, Didier
Paap, Richard
van der Wel, Michel
Ereignis
Veröffentlichung
(wer)
Tinbergen Institute
(wo)
Amsterdam and Rotterdam
(wann)
2016

Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Original beim Datenpartner anzeigen

Objekttyp

  • Arbeitspapier

Beteiligte

  • Nibbering, Didier
  • Paap, Richard
  • van der Wel, Michel
  • Tinbergen Institute

Entstanden

  • 2016

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