Forecasting without Persistence
30 Pages Posted: 11 Jun 2012
Date Written: June 11, 2012
The forecasting literature has identified three important and broad issues: the predictive content of explanatory variable is most of the times unstable over time, in-sample and out-of-sample results are often discordant and precise statistical inference with highly persistent predictors is challenging. In this paper, we simultaneously address these three issues, proposing to directly treat the persistence of forecasting variables before forecasting. We thus cut-out the low frequency components and show, in simulations and on real financial data, that this pre-treatment improves the predictive power of the predictor.
Keywords: forecasting, persistent regressors, detrending, expected returns
JEL Classification: C14, C53, G17
Suggested Citation: Suggested Citation