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Prior Selection for Vector Autoregressions

Listed author(s):
  • Domenico Giannone
  • Michele Lenza
  • Giorgio E. Primiceri

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.

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File URL: http://www.nber.org/papers/w18467.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 18467.

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Date of creation: Oct 2012
Publication status: 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.
Handle: RePEc:nbr:nberwo:18467
Note: EFG ME TWP
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