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Moment-Based Estimation of Linear Panel Data Models with Factor-Augmented Errors

Nicholas L. Brown

Journal of Econometric Methods2024https://doi.org/10.1515/jem-2023-0050article
AJG 1ABDC B
Weight
0.51

Abstract

I compare two popular methods of estimation for linear panel data models with unobserved factors: the first eliminates the factors with a parameterized quasi-long-differencing (QLD) transformation. The other, referred to as common correlated effects (CCE), uses cross-sectional averages of the data to proxy for the factor space. I show that the CCE assumptions imply unused moment conditions that can be exploited by the QLD transformation. I also derive new linear estimators that weaken identifying assumptions and have desirable theoretical properties. Unlike CCE, these estimators do not require the number of covariates to be less than the number of time periods. I provide the first proof of a fixed- T consistent mean group estimator for heterogeneous linear models with interactive fixed effects. I investigate the effects of per-student expenditure on standardized test performance using data from the state of Michigan.

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https://doi.org/https://doi.org/10.1515/jem-2023-0050

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@article{nicholas2024,
  title        = {{Moment-Based Estimation of Linear Panel Data Models with Factor-Augmented Errors}},
  author       = {Nicholas L. Brown},
  journal      = {Journal of Econometric Methods},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1515/jem-2023-0050},
}

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Evidence weight

0.51

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.51 × 0.4 = 0.20
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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