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Federal Reserve Bank of New York
Staff Reports
Priors for the long run
Domenico Giannone
Michele Lenza
Giorgio E. Primiceri
Abstract

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.


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Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, Priors for the long run, Federal Reserve Bank of New York, Staff Reports 832, 01 Nov 2017.
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Keywords: Bayesian vector autoregression; forecasting; overfitting; initial conditions; hierarchical model
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