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Peer-reviewedCC BYJournal of Econometrics

Principal component analysis for a mix of stationary and nonstationary variables

  • James D. Hamilton1
  • Xinwei Ma1
  • Jin Xi2
  1. 1University of California at San Diego, United States
  2. 2Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China
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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 FREDMD macroeconomic dataset demonstrates that the approach offers similar benefits to those of traditional principal component analysis with some added advantages.

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Cite

BibTeXRIS
BibTeX
@article{hamilton2026principal,
  title = {{Principal component analysis for a mix of stationary and nonstationary variables}},
  author = {James D. Hamilton and Xinwei Ma and Jin Xi},
  journal = {Journal of Econometrics},
  year = {2026},
  month = sep,
  doi = {10.1016/j.jeconom.2026.106317},
  url = {https://doi.org/10.1016/j.jeconom.2026.106317},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Principal component analysis for a mix of stationary and nonstationary variables
AU  - James D. Hamilton
AU  - Xinwei Ma
AU  - Jin Xi
PY  - 2026
DA  - 2026/09/04
JO  - Journal of Econometrics
DO  - 10.1016/j.jeconom.2026.106317
UR  - https://doi.org/10.1016/j.jeconom.2026.106317
AB  - 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 FREDMD macroeconomic dataset demonstrates that the approach offers similar benefits to those of traditional principal component analysis with some added advantages.
SN  - 0304-4076
SN  - 1872-6895
ER  -