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Autor:innen: 
Giannone, Domenico
Primiceri, Giorgio E.
Lenza, Michele
Datum: 
2017
Schriftenreihe/Nr.: 
Staff Report No. 832
Verlag: 
Federal Reserve Bank of New York, New York, NY
Zusammenfassung: 
We propose a class of prior distributions that discipline the long-run predictions of vector autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run are conjugate, and can thus be easily implemented using dummy observations and combined with other popular priors. In VARs with standard macroeconomic variables, a prior based on the long-run predictions of a wide class of theoretical models yields substantial improvements in the forecasting performance.
Schlagwörter: 
Bayesian vector autoregression
forecasting
overfitting
initial conditions
hierarchical model
JEL: 
C11
C32
C33
E37
Dokumentart: 
Working Paper

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