Autoregressions in small samples, priors about observables and initial conditions

Albert Marcet and Marek Jarociński

No 1263, Working Paper Series from European Central Bank

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

Keywords: bayesian estimation; Bias Correction; Initial Condition; monetary policy shocks; Prior about Growth Rate; Small Sample Distribution; vector autoregression (search for similar items in EconPapers)
Date: 2010-11
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets and nep-mac
Note: 400529
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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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 (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20101263

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