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A large Bayesian VAR with a block‐specific shrinkage: A forecasting application for Italian industrial production
Author(s) -
Aprigliano Valentina
Publication year - 2020
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2687
Subject(s) - shrinkage , prior probability , bayesian vector autoregression , kalman filter , bayesian probability , shrinkage estimator , industrial production , econometrics , lag , industrial production index , vector autoregression , production (economics) , autoregressive model , computer science , statistics , mathematics , economics , mean squared error , computer network , minimum variance unbiased estimator , bias of an estimator , keynesian economics , macroeconomics
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.