Skip to navigation
Skip to main content
Abstract for "Model Comparisons in Unstable Environments" by Barbara Rossi
We propose new methods for analyzing the relative performance of two competing, misspecified models in the presence of possible data instability. The main idea is to develop a measure of the relative "local performance" for the two models, and to investigate its stability over time by means of statistical tests based on the local Kullback-Leibler information criterion. We propose two tests: a "fluctuation test" for analyzing the evolution of the model's relative performance over historical samples, and a "one-time reversal test", designed specifically to test one time reversals in the relative performance. Compared to previous approaches to model selection, which are based on measures of "global performance", our focus of the entire time path of the models' relative performance may contain useful information that is lost when looking for a globally best model. An empirical application provides insights into the time variation in the performance of a representative DSGE model of the European economy relative to that of VARs.