Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)

In Bayesian analysis of dynamic stochastic general equilibrium (DSGE) models, prior distributions for some of the taste-and-technology parameters can be obtained from microeconometric or presample evidence, but it is difficult to elicit priors for the parameters that govern the law of motion of unobservable exogenous processes. Moreover, since it is challenging to formulate beliefs about the correlation of parameters, most researchers assume that all model parameters are independent of each other. We provide a simple method of constructing prior distributions for a subset of DSGE model parameters from beliefs about the moments of the endogenous variables. We use our approach to investigate the importance of nominal rigidities and show how the specification of prior distributions affects our assessment of the relative importance of different frictions.