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Autoregressions in small samples, priors about observables and initial conditions

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  • Jarociński, Marek
  • Marcet, Albert

Abstract

We propose a benchmark prior for the estimation of vector autoregressions: a prior about initial growth rates of the modeled series. We first show that the Bayesian vs frequentist small sample bias controversy is driven by different default initial conditions. These initial conditions are usually arbitrary and our prior serves to replace them in an intuitive way. To implement this prior we develop a technique for translating priors about observables into priors about parameters. We find that our prior makes a big difference for the estimated persistence of output responses to monetary policy shocks in the United States. JEL Classification: C11, C22, C32

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 1263.

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Date of creation: Nov 2010
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Handle: RePEc:ecb:ecbwps:20101263

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Keywords: Bayesian estimation; Bias Correction; Initial Condition; monetary policy shocks; Prior about Growth Rate; Small Sample Distribution; Vector autoregression;

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Cited by:
  1. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2012. "Prior Selection for Vector Autoregressions," NBER Working Papers 18467, National Bureau of Economic Research, Inc.
  2. Marek Jarocinski & Albert Marcet, 2013. "Priors about Observables in Vector Autoregressions," Working Papers 684, Barcelona Graduate School of Economics.

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