Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies

38 Pages Posted: 4 Jan 2023

See all articles by Runzhe Wan

Runzhe Wan

North Carolina State University

Yingying Li

Hong Kong University of Science & Technology (HKUST), Dept of ISOM and Dept of Finance; Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management

Wenbin Lu

North Carolina State University

Rui Song

North Carolina State University - Department of Statistics

Date Written: December 23, 2022

Abstract

Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate analysis such as principal component analysis. However, the former approach may suffer from the bias while the latter can not incorporate additional information. We propose to bridge these two approaches while allowing the number of factor proxies to diverge, and hence make the latent factor model estimation robust, flexible, and statistically more accurate. As a bonus, the number of factors is also allowed to grow. At the heart of our method is a penalized reduced rank regression to combine information. To further deal with heavy-tailed data, a computationally attractive penalized robust reduced rank regression method is proposed. We establish faster rates of convergence compared with the benchmark. Extensive simulations and real examples are used to illustrate the advantages.

Keywords: Low Rank, Heavy Tails, High Dimensionality, Reduced-rank Regression

JEL Classification: C13, C55, C58, C38

Suggested Citation

Wan, Runzhe and Li, Yingying and Li, Yingying and Lu, Wenbin and Song, Rui, Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies (December 23, 2022). Journal of Econometrics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=4310251

Runzhe Wan (Contact Author)

North Carolina State University ( email )

Hillsborough Street
Raleigh, NC 27695
United States

Yingying Li

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

Hong Kong University of Science & Technology (HKUST), Dept of ISOM and Dept of Finance ( email )

Clear Water Bay, Kowloon
Hong Kong

Wenbin Lu

North Carolina State University ( email )

Hillsborough Street
Raleigh, NC 27695
United States

Rui Song

North Carolina State University - Department of Statistics

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