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Subspace Methods

  • Tom BootEmail author
  • Didier Nibbering
Chapter
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 52)

Abstract

With increasingly many variables available to macroeconomic forecasters, dimension reduction methods are essential to obtain accurate forecasts. Subspace methods are a new class of dimension reduction methods that have been found to yield precise forecasts when applied to macroeconomic and financial data. In this chapter, we review three subspace methods: subset regression, random projection regression, and compressed regression. We provide currently available theoretical results, and indicate a number of open avenues. The methods are illustrated in various settings relevant to macroeconomic forecasters.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Economics, Econometrics and FinanceUniversity of GroningenGroningenThe Netherlands
  2. 2.Department of Econometrics and Business StatisticsMonash UniversityClaytonAustralia

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