Spectral and Post-Spectral Estimators for Grouped Panel Data Models

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Abstract/Contents

Abstract: In this paper, we develop spectral and post-spectral estimators for grouped panel data models. Both estimators are consistent in the asymptotics where the number of observations N and the number of time periods T simultaneously grow large. In addition, the post-spectral estimator is root-NT consistent and asymptotically normal with mean zero under the assumption of well-separated groups even if T is growing much slower than N. The post-spectral estimator has, therefore, theoretical properties that are similar to those of the grouped fixed-effect estimator developed by Bonhomme and Manresa in [9]. In contrast to the grouped fixed-effect estimator, however, our post-spectral estimator is computationally straightforward.

Type of resource	text
Date created	July 14, 2021

DOI	https://doi.org/10.25740/wq575cf9578
Location	https://purl.stanford.edu/wq575cf9578

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License: This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).

Preferred citation: Chetverikov, D. and Manresa, E. (2022). Spectral and Post-Spectral Estimators for Grouped Panel Data Models. Stanford Digital Repository. Available at https://purl.stanford.edu/wq575cf9578

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