Abstract We formulate a prior about observables in a vector autoregression (VAR) and then solve the deconvolution problem for the implied prior about VAR parameters. Formulating a prior about observables is more intuitive than formulating a prior about VAR parameters directly, because VAR parameters are hard to interpret. Our numerical algorithm for approximating the implied prior about parameters works well even in high-dimensional problems and can be applied also for models other than VARs. In the empirical application we formulate a prior about growth rates of the observables in a VAR model of the United States economy. We find that this prior makes a big difference for the estimated persistence of output responses to monetary policy shocks, compared with the results of standard priors for VARs
