Canonical Correlation-based Model Selection for the Multilevel Factors

In Choi, Rui Lin, Yongcheol Shin

Research output: Contribution to journalArticlepeer-review


We develop a novel approach based on the canonical correlation analysis to identify the number of the global factors in the multilevel factor model. We propose the two consistent selection criteria, the canonical correlations difference (CCD) and the modified canonical correlations (MCC). Via Monte Carlo simulations, we show that CCD and MCC select the number of global factors correctly even in small samples, and they are robust to the presence of serially correlated and weakly cross-sectionally correlated idiosyncratic errors as well as the correlated local factors. Finally, we demonstrate the utility of our approach with an application to the multilevel asset pricing model for the stock return data in 12 industries in the U.S.
Original languageEnglish
Pages (from-to)22-44
Number of pages23
JournalJournal of Econometrics
Issue number1
Publication statusPublished - 1 Mar 2023

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© 2021 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.


  • Multilevel Factor Models
  • Principal Components
  • Canonical Correlation Difference
  • Modified Canonical Correlations
  • Multilevel Asset Pricing Models

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