DP11388 In-sample Inference and Forecasting in Misspecified Factor Models

Author(s):	Marine Carrasco, Barbara Rossi
Publication Date:	July 2016
Keyword(s):	factor models, Forecasting, GDP forecasts, large datasets, partial least squares, principal components, regularization methods, Ridge, sparsity, variable selection
JEL(s):	C22, C52, C53
Programme Areas:	Monetary Economics and Fluctuations
Link to this Page:	cepr.org/active/publications/discussion_papers/dp.php?dpno=11388

This paper considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension reduction devices: principal compo- nents, Ridge, Landweber Fridman, and Partial Least Squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross- validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting inflation and output growth in the U.S. show that data-reduction methods out- perform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors' forecasting ability.