Bayesian Inference for the Mixed-Frequency VAR Model
AbstractIn this paper a mixed-frequency VAR à la Mariano & Murasawa (2004) with Markov regime switching in the parameters is estimated by Bayesian inference. Unlike earlier studies, that used the pseuo-EM algorithm of Dempster, Laird & Rubin (1977) to estimate the model, this paper describes how to make use of recent advances in Bayesian inference on mixture models. This way, one is able to surmount some well-known issues connected to inference on mixture models, e.g. the label switching problem. The paper features a numerical simulation study to gauge the model performance in terms of convergence to true parameter values and a small empirical example involving US business cycles.
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Bibliographic InfoPaper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 1172.
Length: 22 : Anh. p.
Date of creation: 2011
Date of revision:
Markov mixture models; Label switching; Bayesian VAR; Mixed frequencies;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Factor Analysis
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
- E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-11-14 (All new papers)
- NEP-ECM-2011-11-14 (Econometrics)
- NEP-ETS-2011-11-14 (Econometric Time Series)
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