Paper #1530

Título:

In-sample inference and forecasting in misspecified factor models

Autores:

Marine Carrasco y Barbara Rossi

Data:

Abril 2016

Resumen:

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.

Palabras clave:

Forecasting, regularization methods, factor models, Ridge, partial least squares, principal components, sparsity, large datasets, variable selection, GDP forecasts, inflation forecasts

Códigos JEL:

C22, C52, C53.

Área de investigación:

Macroeconomía y Economía Internacional / Estadística, Econometría y Métodos Cuantitativos

Publicado en:

Journal of Business and Economic Statistics, 34(3): 313-338, 2016

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