In-sample Inference and Forecasting in Misspecified Factor Models

Barbara Rossi and Marine Carrasco

No 11388, CEPR Discussion Papers from Centre for Economic Policy Research

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 ináation and output growth in the U.S. show that data-reduction methods out- perform conventional methods in several relevant settings, and might e§ectively guard against instabilities in predictorsíforecasting ability.

Keywords: Forecasting; Regularization methods; Factor models; Ridge; Partial least squares; Principal components; Sparsity; Large datasets; Variable selection; Gdp forecasts (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Date: 2016-07
New Economics Papers: this item is included in nep-ecm and nep-for
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Citations: View citations in EconPapers (41)

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Related works:
Journal Article: In-Sample Inference and Forecasting in Misspecified Factor Models (2016)
Working Paper: In-sample inference and forecasting in misspecified factor models (2016)
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