Modeling Model Uncertainty

Abstract

Recently there has been a great deal of interest in studying monetary policy under model uncertainty. We develop new methods to analyze dierent sources of uncer- tainty in one coherent structure, which is useful for policy decisions. We show how to estimate the size of the uncertainty based on time series data, and how to incorpo- rate this uncertainty in choosing policy. In particular, we develop a new approach for modeling uncertainty called model error modeling. The approach imposes additional structure on the errors of an estimated model, and builds a statistical description of the uncertainty around the model. We develop both parametric and nonparametric specifications of this approach, and use them to estimate uncertainty in a small model of the US economy. We then use our estimates to compute Bayesian and minimax robust policy rules, which are designed to perform well in the face of uncertainty.