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Principal Component Analysis for a Mix of Stationary and Nonstationary Variables

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  • James D. Hamilton
  • Xinwei Ma
  • 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 & Xinwei Ma & Jin Xi, 2026. "Principal Component Analysis for a Mix of Stationary and Nonstationary Variables," Papers 2608.23732, arXiv.org.
    • Handle: RePEc:arx:papers:2608.23732
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    References listed on IDEAS

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    1. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    2. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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