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Principal Component and Static Factor Analysis

  • Jianfei Cao
  • Chris Gu
  • Yike WangEmail author
Chapter
  • 447 Downloads
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 52)

Abstract

Factor models are widely used in macroeconomic forecasting. With large datasets, factor models are particularly useful due to their intrinsic dimension reduction. In this chapter, we consider the forecasting problem using factor models, with special consideration to large datasets. In factor model estimation, we focus on principal component methods, and show how the estimated factors can be used to assist forecasting. Machine learning methods are discussed to encompass the high-dimensional features of large factor models. We consider policy evaluation as a nowcasting problem and show how factor analysis can be used to perform counter-factual outcome prediction in complicated models with observational data. The usage of all these techniques is illustrated by empirical examples.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Chicago Booth School of BusinessChicagoUSA
  2. 2.Scheller College of BusinessGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Department of EconomicsLondon School of EconomicsLondonUK

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