A Monte Carlo evaluation of the efficiency of the PCSE estimator
AbstractPanel data characterized by groupwise heteroscedasticity, cross-sectional correlation, and AR(1) serial correlation pose problems for econometric analyses. It is well known that the asymptotically efficient, Feasible Generalized Least Squares (FGLS) estimator (Parks) sometimes performs poorly in finite samples. In a widely cited paper, Beck and Katz (1995) claim that their estimator panel-corrected SE (PCSE) is able to produce more accurate coefficient SE without any loss in efficiency in 'practical research situations'. This study disputes that claim. We find that the PCSE estimator is usually less efficient than Parks - and substantially so - except when the number of time periods is close to the number of cross sections.
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Bibliographic InfoArticle provided by Taylor and Francis Journals in its journal Applied Economics Letters.
Volume (Year): 17 (2010)
Issue (Month): 1 ()
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Other versions of this item:
- Xiujian Chen & Shu Lin & W. Robert Reed, 2006. "A Monte Carlo Evaluation of the Efficiency of the PCSE Estimator," Working Papers in Economics 06/14, University of Canterbury, Department of Economics and Finance.
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
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