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Discussion Paper Details

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Title: In-sample Inference and Forecasting in Misspecified Factor Models

Author(s): Marine Carrasco and Barbara Rossi

Publication Date: July 2016

Keyword(s): factor models, Forecasting, GDP forecasts, large datasets, partial least squares, principal components, regularization methods, Ridge, sparsity and variable selection

Programme Area(s): Monetary Economics and Fluctuations

Abstract: 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.

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Bibliographic Reference

Carrasco, M and Rossi, B. 2016. 'In-sample Inference and Forecasting in Misspecified Factor Models'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=11388