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Shrinkage Estimation of Factor Models With Global and Group-Specific Factors

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  • Xu Han

Abstract

This article develops an adaptive group lasso estimator for factor models with both global and group-specific factors. The global factors can affect all variables, whereas the group-specific factors are only allowed to affect the variables within a certain group. We propose a new method to separately identify the spaces spanned by global and group-specific factors, and we develop a new shrinkage estimator that can consistently estimate the factor loadings and determine the number of factors simultaneously. The asymptotic result shows that the proposed estimator can select the true model specification with a probability approaching one. An information criterion is developed to select the optimal tuning parameters in the shrinkage estimation. Monte Carlo simulations confirm our asymptotic theory, and the proposed estimator performs well in finite samples. In an empirical application, we implement the proposed method to a dataset consisting of Eurozone, United States, and United Kingdom macroeconomic variables, and we detect one global factor, one U.S.-specific factor, and one Eurozone-specific factor.

Suggested Citation

  • Xu Han, 2021. "Shrinkage Estimation of Factor Models With Global and Group-Specific Factors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 1-17, January.
    • Handle: RePEc:taf:jnlbes:v:39:y:2021:i:1:p:1-17
    DOI: 10.1080/07350015.2019.1617157
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    Cited by:

    1. Jiti Gao & Bin Peng & Yayi Yan, 2022. "Nonparametric Estimation and Testing for Time-Varying VAR Models," Monash Econometrics and Business Statistics Working Papers 3/22, Monash University, Department of Econometrics and Business Statistics.
    2. Sung Hoon Choi & Donggyu Kim, 2023. "Large Global Volatility Matrix Analysis Based on Observation Structural Information," Papers 2305.01464, arXiv.org, revised Feb 2024.
    3. Venetis, Ioannis & Ladas, Avgoustinos, 2022. "Co-movement and global factors in sovereign bond yields," MPRA Paper 115801, University Library of Munich, Germany.
    4. Sung Hoon Choi & Donggyu Kim, 2022. "Large Volatility Matrix Analysis Using Global and National Factor Models," Papers 2208.12323, arXiv.org, revised Dec 2022.
    5. Choi, In & Lin, Rui & Shin, Yongcheol, 2023. "Canonical correlation-based model selection for the multilevel factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 22-44.
    6. Choi, Sung Hoon & Kim, Donggyu, 2023. "Large volatility matrix analysis using global and national factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1917-1933.
    7. Guohua Feng & Jiti Gao & Bin Peng, 2022. "Multi-Level Panel Data Models: Estimation and Empirical Analysis," Monash Econometrics and Business Statistics Working Papers 4/22, Monash University, Department of Econometrics and Business Statistics.
    8. Antoine A. Djogbenou, 2024. "Identifying oil price shocks with global, developed, and emerging latent real economy activity factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 128-149, January.

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