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

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  • Brown Nicholas L.

    (Department of Economics, Florida State University, 113 Collegiate Loop, Tallahassee, FL 32304, USA)

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.

Suggested Citation

  • Brown Nicholas L., 2024. "Moment-Based Estimation of Linear Panel Data Models with Factor-Augmented Errors," Journal of Econometric Methods, De Gruyter, vol. 13(2), pages 299-317.
    • Handle: RePEc:bpj:jecome:v:13:y:2024:i:2:p:299-317:n:1005
    DOI: 10.1515/jem-2023-0050
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    More about this item

    Keywords

    factor models; common correlated effects; quasi-long-differencing; fixed-T; correlated random coefficients;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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