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My bibliography Save this paperLimit Theorems for Factor Models
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Abstract
The paper establishes the central limit theorems and proposes how to perform valid inference in factor models. We consider a setting where many counties/regions/assets are observed for many time periods, and when estimation of a global parameter includes aggregation of a cross-section of heterogeneous micro-parameters estimated separately for each entity. The central limit theorem applies for quantities involving both cross-sectional and time series aggregation, as well as for quadratic forms in time-aggregated errors. The paper studies the conditions when one can consistently estimate the asymptotic variance, and proposes a bootstrap scheme for cases when one cannot. A small simulation study illustrates performance of the asymptotic and bootstrap procedures. The results are useful for making inferences in two-step estimation procedures related to factor models, as well as in other related contexts. Our treatment avoids structural modeling of cross-sectional dependence but imposes time-series independence.
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- Anatolyev, Stanislav & Mikusheva, Anna, 2021. "Limit Theorems For Factor Models," Econometric Theory, Cambridge University Press, vol. 37(5), pages 1034-1074, October.
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Cited by:
- Anatolyev, Stanislav & Mikusheva, Anna, 2022.
"Factor models with many assets: Strong factors, weak factors, and the two-pass procedure,"
Journal of Econometrics, Elsevier, vol. 229(1), pages 103-126.
- Stanislav Anatolyev & Anna Mikusheva, 2018. "Factor models with many assets: strong factors, weak factors, and the two-pass procedure," Papers 1807.04094, arXiv.org, revised Apr 2019.
- Anatolyev, Stanislav & Sølvsten, Mikkel, 2023.
"Testing many restrictions under heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 236(1).
- Stanislav Anatolyev & Mikkel S{o}lvsten, 2020. "Testing Many Restrictions Under Heteroskedasticity," Papers 2003.07320, arXiv.org, revised Jan 2023.
- Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
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