A Monte Carlo Evaluation of the Efficiency of the PCSE Estimator
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.
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| Length: | 11 pages |
| Date of creation: | 03 Nov 2006 |
| Date of revision: | |
| Handle: | RePEc:cbt:econwp:06/14 |
| Contact details of provider: | Postal: Phone: 64 3 369 3123 (Administrator) Fax: 64 3 364 2635 Web page: http://www.econ.canterbury.ac.nz More information through EDIRC
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Joseph DeJuan & Maria J. Luengo-Prado, 2005.
"Consumption and Aggregate Constraints: International Evidence,"
Macroeconomics
0501018, EconWPA.
- Joseph P. Dejuan & Maria Jose Luengo-Prado, 2006. "Consumption and Aggregate Constraints: International Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(1), pages 81-99, 02.
- Michael A. Lapré & Nikos Tsikriktsis, 2006. "Organizational Learning Curves for Customer Dissatisfaction: Heterogeneity Across Airlines," Management Science, INFORMS, vol. 52(3), pages 352-366, March.
- Yermack, David, 2006. "Flights of fancy: Corporate jets, CEO perquisites, and inferior shareholder returns," Journal of Financial Economics, Elsevier, vol. 80(1), pages 211-242, April.
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