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Principal Component Analysis for Nonstationary Series

Author

Listed:
  • James D. Hamilton
  • Jin Xi

Abstract

This paper develops a procedure for uncovering the common cyclical factors that drive a mix of stationary and nonstationary variables. The method does not require knowing which variables are nonstationary or the nature of the nonstationarity. An application to the FRED-MD macroeconomic dataset demonstrates that the approach offers similar benefits to those of traditional principal component analysis with some added advantages.

Suggested Citation

  • James D. Hamilton & Jin Xi, 2024. "Principal Component Analysis for Nonstationary Series," NBER Working Papers 32068, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:32068
    Note: EFG
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    Cited by:

    1. Rajveer Jat & Daanish Padha, 2024. "Kernel Three Pass Regression Filter," Papers 2405.07292, arXiv.org, revised Jun 2024.

    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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