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• W2368500460 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." @default.
• W2368500460 created "2016-06-24" @default.
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• W2368500460 date "2016-07-02" @default.
• W2368500460 modified "2023-10-09" @default.
• W2368500460 title "In-Sample Inference and Forecasting in Misspecified Factor Models" @default.
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• W2368500460 doi "https://doi.org/10.1080/07350015.2016.1186029" @default.
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