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, FGLS estimator (Parks) sometimes performs poorly in finite samples. In a widely cited paper, Beck and Katz (1995) claim that their estimator (PCSE) is able to produce more accurate coefficient standard errors 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 InfoPaper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 06/14.
Length: 11 pages
Date of creation: 03 Nov 2006
Date of revision:
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Panel data estimation; Monte Carlo analysis; FGLS; Parks; PCSE; finite sample;
Other versions of this item:
- Xiujian Chen & Shu Lin & W. Robert Reed, 2010. "A Monte Carlo evaluation of the efficiency of the PCSE estimator," Applied Economics Letters, Taylor and Francis Journals, vol. 17(1), pages 7-10.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-01-14 (All new papers)
- NEP-ECM-2007-01-14 (Econometrics)
- NEP-ETS-2007-01-14 (Econometric Time Series)
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