Prior Selection for Vector Autoregressions

Domenico Giannone, Michele Lenza and Giorgio Primiceri

No 18467, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of-sample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors, in order to shrink the richly parameterized unrestricted model towards a parsimonious naïve benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach is theoretically grounded, easy to implement, and greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well both in terms of out-of-sample forecasting--as well as factor models--and accuracy in the estimation of impulse response functions.

JEL-codes: C11 C32 C53 E37 E47 (search for similar items in EconPapers)
Date: 2012-10
New Economics Papers: this item is included in nep-ets and nep-for
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Citations: View citations in EconPapers (93)

Published as Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 2(97), pages 436-451, May.

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Related works:
Journal Article: Prior Selection for Vector Autoregressions (2015)
Working Paper: Prior Selection for Vector Autoregressions (2012)
Working Paper: Prior Selection for Vector Autoregressions (2012)
Working Paper: Prior selection for vector autoregressions (2012)
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