Artikel
Panel data estimation for correlated random coefficients models
This paper considers methods of estimating a static correlated random coefficient model with panel data. We mainly focus on comparing two approaches of estimating unconditional mean of the coefficients for the correlated random coefficients models, the group mean estimator and the generalized least squares estimator. For the group mean estimator, we show that it achieves Chamberlain (1992) semi-parametric efficiency bound asymptotically. For the generalized least squares estimator, we show that when T is large, a generalized least squares estimator that ignores the correlation between the individual coefficients and regressors is asymptotically equivalent to the group mean estimator. In addition, we give conditions where the standard within estimator of the mean of the coefficients is consistent. Moreover, with additional assumptions on the known correlation pattern, we derive the asymptotic properties of panel least squares estimators. Simulations are used to examine the finite sample performances of different estimators.
- Language
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Englisch
- Bibliographic citation
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 7 ; Year: 2019 ; Issue: 1 ; Pages: 1-18 ; Basel: MDPI
- Classification
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Wirtschaft
Estimation: General
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
- Subject
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panel data
correlated random coefficients
efficiency bound
- Event
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Geistige Schöpfung
- (who)
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Hsiao, Cheng
Li, Qi
Liang, Zhongwen
Xie, Wei
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2019
- DOI
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doi:10.3390/econometrics7010007
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
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Object type
- Artikel
Associated
- Hsiao, Cheng
- Li, Qi
- Liang, Zhongwen
- Xie, Wei
- MDPI
Time of origin
- 2019
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