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 oftraditional principal component analysis with some added advantages.

Publication:

JOURNAL OF ECONOMETRICS

http://dx.doi.org/10.1016/j.jeconom.2026.106317

Author:

James D. Hamilton

University of California at San Diego, United States

Corresponding author

E-mail addresses: jhamilton@ucsd.edu

Xinwei Ma

University of California at San Diego, United States

E-mail addresses: x1ma@ucsd.edu

Jin Xi

Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China

E-mail addresses: xijin@amss.ac.cn