We evaluate various models' relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the models' relative performance can be varying over time. We show that the models' relative performance has, in fact,...

We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the out-of-sample performance of the model, judged by some loss...

The goal of this paper is to develop formal tests to evaluate the relative in-sample performance of two competing, misspecified non-nested models in the presence of possible data instability. Compared to previous approaches to model selection, which are based on measures of global performance,...