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Autoregressions in Small Samples, Priors about Observables and Initial Conditions

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  • Marek Jarocinski
  • Albert Marcet

Abstract

We propose a benchmark prior for the estimation of vector autoregressions: a prior about initial growth rates of the modelled 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.

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Bibliographic Info

Paper provided by Centre for Economic Performance, LSE in its series CEP Discussion Papers with number dp1061.

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Date of creation: Jul 2011
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Handle: RePEc:cep:cepdps:dp1061

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Web page: http://cep.lse.ac.uk/_new/publications/series.asp?prog=CEP

Related research

Keywords: Vector autoregression; initial condition; bayesian estimation; prior about growthrate; monetary policy shocks; small sample distribution; bias correction;

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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," UFAE and IAE Working Papers 929.13, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).

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