Revisiting targeted factors

Jack Fosten

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Abstract

This paper proposes new methods for ‘targeting’ factors estimated from a big dataset. We suggest that forecasts of economic variables can be improved by tuning factor estimates: (i) so that they are both more relevant for a specific target variable; and (ii) so that variables with considerable idiosyncratic noise are down-weighted prior to factor estimation. Existing targeted factor methodologies are limited to estimating the factors with only one of these two objectives in mind. We therefore combine these ideas by providing new weighted principal components analysis (PCA) procedures and a targeted generalized PCA (TGPCA) procedure. These methods offer a flexible combination of both types of targeting that is new to the literature. We illustrate this empirically by forecasting a range of US macroeconomic variables, finding that our combined approach yields important improvements over competing methods, consistently surviving elimination in the model confidence set procedure. Copyright © 2016 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)207–216
Number of pages10
JournalJournal of Forecasting
Volume36
Issue number2
Early online date14 Jun 2016
DOIs
Publication statusPublished - Mar 2017

Keywords

  • Forecasting
  • factor estimation
  • targeted predictors
  • LASSO
  • data reduction

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