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Generalized Shrinkage Methods for Forecasting Using Many Predictors

Listed author(s):
  • James H. Stock
  • Mark W. Watson

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.

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File URL: http://hdl.handle.net/10.1080/07350015.2012.715956
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Article provided by Taylor & Francis Journals in its journal Journal of Business & Economic Statistics.

Volume (Year): 30 (2012)
Issue (Month): 4 (June)
Pages: 481-493

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Handle: RePEc:taf:jnlbes:v:30:y:2012:i:4:p:481-493
DOI: 10.1080/07350015.2012.715956
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