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Common correlated effects estimation of dynamic panels with cross-sectional dependence

Gerdie Everaert (UGent) and Tom De Groote (UGent)
(2016) ECONOMETRIC REVIEWS. 35(3). p.428-463
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Abstract
We derive inconsistency expressions for dynamic panel data estimators under error cross-sectional dependence generated by an unobserved common factor in both the fixed effect and the incidental trends case. We show that for a temporally dependent factor, the standard within groups (WG) estimator is inconsistent even as both N and T tend to infinity. Next we investigate the properties of the common correlated effects pooled (CCEP) estimator of Pesaran [Econometrica, 2006] which eliminates the error cross-sectional dependence using cross-sectional averages of the data. In contrast to the static case, the CCEP estimator is only consistent when next to N also T tends to infinity. It is shown that for the most relevant parameter settings, the inconsistency of the CCEP estimator is larger than that of the infeasible WG estimator, which includes the common factors as regressors. Restricting the CCEP estimator results in a somewhat smaller inconsistency. The small sample properties of the various estimators are analysed using Monte Carlo experiments. The simulation results suggest that the CCEP estimator can be used to estimate dynamic panel data models provided T is not too small. The size of N is of less importance.
Keywords
MULTIFACTOR ERROR STRUCTURE, DATA MODELS, BIAS, INFERENCE, TESTS, Cross-sectional dependence, Common correlated effects, Dynamic panel

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Citation

Please use this url to cite or link to this publication:

Chicago
Everaert, Gerdie, and Tom De Groote. 2016. “Common Correlated Effects Estimation of Dynamic Panels with Cross-sectional Dependence.” Econometric Reviews 35 (3): 428–463.
APA
Everaert, Gerdie, & De Groote, T. (2016). Common correlated effects estimation of dynamic panels with cross-sectional dependence. ECONOMETRIC REVIEWS, 35(3), 428–463.
Vancouver
1.
Everaert G, De Groote T. Common correlated effects estimation of dynamic panels with cross-sectional dependence. ECONOMETRIC REVIEWS. 2016;35(3):428–63.
MLA
Everaert, Gerdie, and Tom De Groote. “Common Correlated Effects Estimation of Dynamic Panels with Cross-sectional Dependence.” ECONOMETRIC REVIEWS 35.3 (2016): 428–463. Print.
@article{4378230,
  abstract     = {We derive inconsistency expressions for dynamic panel data estimators under error cross-sectional dependence generated by an unobserved common factor in both the fixed effect and the incidental trends case. We show that for a temporally dependent factor, the standard within groups (WG) estimator is inconsistent even as both N and T tend to infinity. Next we investigate the properties of the common correlated effects pooled (CCEP) estimator of Pesaran [Econometrica, 2006] which eliminates the error cross-sectional dependence using cross-sectional averages of the data. In contrast to the static case, the CCEP estimator is only consistent when next to N also T tends to infinity. It is shown that for the most relevant parameter settings, the inconsistency of the CCEP estimator is larger than that of the infeasible WG estimator, which includes the common factors as regressors. Restricting the CCEP estimator results in a somewhat smaller inconsistency. The small sample properties of the various estimators are analysed using Monte Carlo experiments. The simulation results suggest that the CCEP estimator can be used to estimate dynamic panel data models provided T is not too small. The size of N is of less importance.},
  author       = {Everaert, Gerdie and De Groote, Tom},
  issn         = {0747-4938},
  journal      = {ECONOMETRIC REVIEWS},
  keyword      = {MULTIFACTOR ERROR STRUCTURE,DATA MODELS,BIAS,INFERENCE,TESTS,Cross-sectional dependence,Common correlated effects,Dynamic panel},
  language     = {eng},
  number       = {3},
  pages        = {428--463},
  title        = {Common correlated effects estimation of dynamic panels with cross-sectional dependence},
  url          = {http://dx.doi.org/10.1080/07474938.2014.966635},
  volume       = {35},
  year         = {2016},
}

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