A Monte Carlo Evaluation of the Efficiency of the PCSE Estimator

Xiujian Chen, Shu Lin and W. Reed ()

Working Papers in Economics from University of Canterbury, Department of Economics and Finance

Abstract: Panel 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.

Keywords: Panel data estimation; Monte Carlo analysis; FGLS; Parks; PCSE; finite sample (search for similar items in EconPapers)
JEL-codes: C15 C23 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2006-11-03
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: A Monte Carlo evaluation of the efficiency of the PCSE estimator (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:cbt:econwp:06/14

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