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In-sample inference and forecasting in misspecified factor models

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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.

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File URL: https://econ-papers.upf.edu/papers/1530.pdf
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Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 1530.

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Date of creation: Apr 2016
Handle: RePEc:upf:upfgen:1530
Contact details of provider: Web page: http://www.econ.upf.edu/

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