A large Bayesian VAR with a block-specific shrinkage: A forecasting application for Italian industrial production |
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Authors: | Valentina Aprigliano |
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Institution: | Economic Outlook and Monetary Policy Directorate, Banca d'Italia, Rome, Italy |
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Abstract: | This paper proposes a Bayesian vector autoregression (BVAR) model with the Kalman filter to forecast the Italian industrial production index in a pseudo real-time experiment. Minnesota priors are adopted as a general framework, but a different shrinkage pattern is imposed for both the VAR coefficients and the Kalman gain, depending on the informative contribution of each variable investigated at frequency level. Both a time-varying and a constant selection for the shrinkage are proposed. Overall, the new BVAR models significantly improve the forecasting performance in comparison with the more traditional versions based on standard Minnesota priors with a single shrinkage, equal for all the variables, and selected on the basis of some optimal criteria. Very promising results come out in terms of density forecasting as well. |
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Keywords: | bayesian VAR gibbs sampling industrial production kalman filter wavelet filter |
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