The PCSE Estimator is Good - Just Not as Good As You Think

Type of content
Discussion / Working Papers
Publisher's DOI/URI
Thesis discipline
Degree name
Publisher
College of Business and Economics
University of Canterbury. Department of Economics and Finance
Journal Title
Journal ISSN
Volume Title
Language
Date
2010
Authors
Reed, W.R.
Webb, R.
Abstract

This paper investigates the properties of the Panel-Corrected Standard Error (PCSE) estimator. The PCSE estimator is commonly used when working with time-series, crosssectional (TSCS) data. In an influential paper, Beck and Katz (1995) (henceforth BK) demonstrated that FGLS produces coefficient standard errors that are severely underestimated. They report Monte Carlo experiments in which the PCSE estimator produces accurate standard error estimates at no, or little, loss in efficiency compared to FGLS. Our study further investigates the properties of the PCSE estimator. We first reproduce the main experimental results of BK using their Monte Carlo framework. We then show that the PCSE estimator does not perform as well when tested in data environments that better resemble 'practical research situations.' When (i) the explanatory variable(s) are characterized by substantial persistence, (ii) there is serial correlation in the errors, and (iii) the time span of the data series is relatively short, coverage rates for the PCSE estimator frequently fall between 80 and 90 percent. Further, we find many 'practical research situations' where the PCSE estimator compares poorly with FGLS on efficiency grounds.

Description
RePEc Working Paper Series: No. 53/2010
Citation
Reed, W.R., Webb, R. (2010) The PCSE Estimator is Good - Just Not as Good As You Think. Department of Economics and Finance. 31pp..
Keywords
Panel data estimation, Monte Carlo analysis, FGLS, Parks, PCSE, finite sample
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
Field of Research::14 - Economics
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