Business Intelligence

Autoregressions In Small Samples, Priors About Observables And Initial Conditions

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Executive Summary

The authors propose a benchmark prior for the estimation of vector autoregressions: a prior about initial growth rates of the modeled series. They 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 the prior serves to replace them in an intuitive way. To implement this prior they develop a technique for translating priors about observables into priors about parameters. They find that the prior makes a big difference for the estimated persistence of output responses to monetary policy shocks in the United States.

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