Motivated by the common finding that linear autoregressive models often forecast better than models that incorporate additional information, this paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining forecasts from nested models. In our analytics, the...

This paper evaluates potential explanations for the sometimes poor forecasting performance of the Phillips curve. One explanation is that out-of-sample metrics are noisy or, equivalently, have relatively low power. Another potential explanation is instability in the coefficients of the model. To...

We develop methods for testing whether, in a finite sample, forecasts from nested models are equally accurate. Most prior work has focused on a null of equal accuracy in population — basically, whether the additional coefficients of the larger model are zero. Our asymptotic approximation...

How should one conclude whether the data have come in stronger, weaker, or as expected?>

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper...