Granger causality and regime inference in Bayesian Markov-Switching VARs

Arbeitspapier

We derive restrictions for Granger noncausality in Markov-switching vector autoregressive models and also show under which conditions a variable does not affect the forecast of the hidden Markov process. Based on Bayesian approach to evaluating the hypotheses, the computational tools for posterior inference include a novel block Metropolis-Hastings sampling algorithm for the estimation of the restricted models. We analyze a system of monthly US data on money and income. The test results in MS-VARs contradict those in linear VARs: the money aggregate M1 is useful for forecasting income and for predicting the next period’s state.

Object Details

• ISBN: 978-92-899-1607-3
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: ECB Working Paper ; No. 1794

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Hypothesis Testing: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Forecasting Models; Simulation Methods ; Business Fluctuations; Cycles
• Subject: Bayesian hypothesis testing ; block Metropolis-Hastings sampling ; Markov-switching models ; mixture models ; posterior odds ratio ; Kausalanalyse ; Markov-Kette ; Stichprobenerhebung ; VAR-Modell ; Schätzung ; Industrieproduktion ; Geldmenge ; USA

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Droumaguet, Matthieu ; Warne, Anders ; Woźniak, Tomasz
• Event: Veröffentlichung
• (who): European Central Bank (ECB)
• (where): Frankfurt a. M.
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/154227
• Last update: 10.03.2025, 11:41 AM CET