In-sample inference and forecasting in misspecified factor models

Marine Carrasco and Barbara Rossi

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

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 components, 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.

Keywords: Forecasting; regularization methods; factor models; Ridge; partial least squares; principal components; sparsity; large datasets; variable selection; GDP forecasts; inflation forecasts (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Date: 2016-04
New Economics Papers: this item is included in nep-cse, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (39)

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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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Persistent link: https://EconPapers.repec.org/RePEc:upf:upfgen:1530

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