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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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  • Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
  • Handle: RePEc:upf:upfgen:1530
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    Cited by:

    1. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank, Research Department.
    2. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
    3. Djogbenou, Antoine A., 2017. "Model Selection in Factor-Augmented Regressions with Estimated Factors," Queen's Economics Department Working Papers 274717, Queen's University - Department of Economics.
    4. repec:aea:jecper:v:31:y:2017:i:2:p:59-86 is not listed on IDEAS
    5. Antoine A. Djogbenou, 2017. "Model Selection in Factor-Augmented Regressions with Estimated Factors," Working Papers 1391, Queen's University, Department of Economics.

    More about this item

    Keywords

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

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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