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Bias-corrected Common Correlated Effects Pooled estimation in homogeneous dynamic panels

Ignace De Vos (UGent) and Gerdie Everaert (UGent)
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HPC-UGent: the central High Performance Computing infrastructure of Ghent University
Abstract
This paper extends the Common Correlated Effects Pooled (CCEP) estimator to homogeneous dynamic panels. In this setting CCEP suffers from a large bias when the time series dimension (T) is fixed. We develop a bias-corrected estimator that is valid for a multi-factor error structure provided that a sufficient number of cross-sectional averages, and lags thereof, are added to the model. We show that the resulting CCEPbc estimator is consistent as the number of cross-sections (N) tends to infinity, both for T fixed or growing large. Monte Carlo experiments show that our correction offers strong improvements in terms of bias and variance.
Keywords
bias correction, CCEP, Dynamic panel data, unobserved common factors

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Citation

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

MLA
De Vos, Ignace, and Gerdie Everaert. “Bias-corrected Common Correlated Effects Pooled Estimation in Homogeneous Dynamic Panels.” Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 2016 : n. pag. Print.
APA
De Vos, I., & Everaert, G. (2016). Bias-corrected Common Correlated Effects Pooled estimation in homogeneous dynamic panels. Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium. Ghent, Belgium: Ghent University, Faculty of Economics and Business Administration.
Chicago author-date
De Vos, Ignace, and Gerdie Everaert. 2016. “Bias-corrected Common Correlated Effects Pooled Estimation in Homogeneous Dynamic Panels.” Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium. Ghent, Belgium: Ghent University, Faculty of Economics and Business Administration.
Chicago author-date (all authors)
De Vos, Ignace, and Gerdie Everaert. 2016. “Bias-corrected Common Correlated Effects Pooled Estimation in Homogeneous Dynamic Panels.” Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium. Ghent, Belgium: Ghent University, Faculty of Economics and Business Administration.
Vancouver
1.
De Vos I, Everaert G. Bias-corrected Common Correlated Effects Pooled estimation in homogeneous dynamic panels. Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium. Ghent, Belgium: Ghent University, Faculty of Economics and Business Administration; 2016.
IEEE
[1]
I. De Vos and G. Everaert, “Bias-corrected Common Correlated Effects Pooled estimation in homogeneous dynamic panels,” Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium. Ghent University, Faculty of Economics and Business Administration, Ghent, Belgium, 2016.
@misc{7206033,
  abstract     = {This paper extends the Common Correlated Effects Pooled (CCEP) estimator to homogeneous dynamic panels. In this setting CCEP suffers from a large bias when the time series dimension (T) is fixed. We develop a bias-corrected estimator that is valid for a multi-factor error structure provided that a sufficient number of cross-sectional averages, and lags thereof, are added to the model. We show that the resulting CCEPbc estimator is consistent as the number of cross-sections (N) tends to infinity, both for T fixed or growing large. Monte Carlo experiments show that our correction offers strong improvements in terms of bias and variance.},
  articleno    = {2016/920},
  author       = {De Vos, Ignace and Everaert, Gerdie},
  keywords     = {bias correction,CCEP,Dynamic panel data,unobserved common factors},
  language     = {eng},
  pages        = {59},
  publisher    = {Ghent University, Faculty of Economics and Business Administration},
  series       = {Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium},
  title        = {Bias-corrected Common Correlated Effects Pooled estimation in homogeneous dynamic panels},
  year         = {2016},
}