Autoregressions in Small Samples, Priors about Observables and Initial Conditions

JEL codes: 
C11, C22, C32
Version Date: 
Sep 2010

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 di erent 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 di erence for the estimated persistence of output responses to monetary policy shocks in the United States.

PDF icon Download the paper (515.87 KB)