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Forecasting with Bayesian Vector Autoregressions Estimated Using Professional Forecasts
Author(s) -
Frey Christoph,
Mokinski Frieder
Publication year - 2015
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2483
Subject(s) - bayesian vector autoregression , vector autoregression , econometrics , benchmark (surveying) , survey of professional forecasters , bayesian probability , nowcasting , shrinkage estimator , variable (mathematics) , bayesian inference , range (aeronautics) , shrinkage , computer science , mean squared error , economics , statistics , mathematics , monetary policy , geography , engineering , mathematical analysis , geodesy , minimum variance unbiased estimator , aerospace engineering , meteorology , bias of an estimator , monetary economics
Summary We propose a Bayesian shrinkage approach for vector autoregressions (VARs) that uses short‐term survey forecasts as an additional source of information about model parameters. In particular, we augment the vector of dependent variables by their survey nowcasts, and claim that each variable modelled in the VAR and its nowcast are likely to depend in a similar way on the lagged dependent variables. In an application to macroeconomic data, we find that the forecasts obtained from a VAR fitted by our new shrinkage approach typically yield smaller mean squared forecast errors than the forecasts obtained from a range of benchmark methods. Copyright © 2015 John Wiley & Sons, Ltd.