LIMIT THEOREMS FOR FACTOR MODELS
Name
final version of paper.pdf
Description
Accepted version
Size
357.01 KB
Format
Adobe PDF
Checksum (MD5)
3498cce9725bab26bf4bcf437300acf0
Author(s)
Anatolyev, Stanislav
Mikusheva, Anna
Date Issued
2020
Journal
Econometric Theory
Publisher
Cambridge University Press (CUP)
Version
Author's final manuscript
Abstract
© The Author(s), 2020. Published by Cambridge University Press. This paper establishes central limit theorems (CLTs) 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 microparameters estimated separately for each entity. The CLT applies for quantities involving both cross-sectional and time series aggregation, as well as for quadratic forms in time-aggregated errors. This 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.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1017/S0266466620000468
