Generalized shrinkage methods for forecasting using many predictors

James H. Stock, Mark W. Watson

Research output: Contribution to journalArticle

100 Scopus citations

Abstract

This article provides a simple shrinkage representation that describes the operational characteristics of various forecasting methods designed for a large number of orthogonal predictors (such as principal components). These methods include pretest methods, Bayesian model averaging, empirical Bayes, and bagging. We compare empirically forecasts from these methods with dynamic factor model (DFM) forecasts using a U.S. macroeconomic dataset with 143 quarterly variables spanning 1960-2008. For most series, including measures of real economic activity, the shrinkage forecasts are inferior to the DFM forecasts. This article has online supplementary material.

Original languageEnglish (US)
Pages (from-to)481-493
Number of pages13
JournalJournal of Business and Economic Statistics
Volume30
Issue number4
DOIs
StatePublished - Dec 10 2012

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Keywords

  • Dynamic factor models
  • Empirical bayes
  • High-dimensional model

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