Autoregressions in small samples, priors about observables and initial conditions

Marek Jarociński and Albert Marcet

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: vector autoregression; initial condition; bayesian estimation; prior about growth rate; monetary policy shocks; small sample distribution; bias correction (search for similar items in EconPapers)
JEL-codes: C11 C22 C32 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2011-07-13
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Downloads: (external link)
http://eprints.lse.ac.uk/121711/ Open access version. (application/pdf)

Related works:
Working Paper: Autoregressions in Small Samples, Priors about Observables and Initial Conditions (2011)
Working Paper: Autoregressions in small samples, priors about observables and initial conditions (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:121711

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