In-Sample Inference and Forecasting in Misspecified Factor Models

Marine Carrasco and Barbara Rossi

Journal of Business & Economic Statistics, 2016, vol. 34, issue 3, 313-338

Abstract: This article 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 outperform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors’ forecasting ability.

Date: 2016
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Citations: View citations in EconPapers (37)

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Working Paper: In-sample Inference and Forecasting in Misspecified Factor Models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:34:y:2016:i:3:p:313-338

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DOI: 10.1080/07350015.2016.1186029

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