Forecasting with Approximate Dynamic Factor Models: The Role of Non-Pervasive Shocks

Abstract

In this paper we investigate whether accounting for non-pervasive shocks improves the forecast of a factor model. We compare four models on a large panel of US quarterly data: factor models, factor models estimated on selected variables, Bayesian shrinkage, and factor models together with Bayesian shrinkage for the idiosyncratic component. The results of the forecasting exercise show that the four approaches considered perform equally well and produce highly correlated forecasts, meaning that non-pervasive shocks are of no helps in forecasting. We conclude that comovements captured by factor models are informative enough to make accurate forecasts.

Keywords: Dynamic Factor Models, Penalized Regressions, Local Factors, Bayesian Shrinkage, Forecasting

JEL Classification: C13, C32, C33, C52, C53