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

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Published/Copyright: September 20, 2010
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Journal of Time Series Econometrics
From the journal Journal of Time Series Econometrics Volume 2 Issue 1

This paper investigates the properties of the Panel-Corrected Standard Error (PCSE) estimator. The PCSE estimator is commonly used when working with time-series, cross-sectional (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.

Keywords: panel data; Monte Carlo analysis; FGLS; Parks; PCSE; finite sample
Published Online: 2010-9-20

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

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  9. The PCSE Estimator is Good -- Just Not As Good As You Think
https://doi.org/10.2202/1941-1928.1032
Keywords for this article
panel data; Monte Carlo analysis; FGLS; Parks; PCSE; finite sample

Articles in the same Issue

  1. Article
  2. Nonlinearity and Spatial Lag Dependence: Tests Based on Double-Length Regressions
  3. Testing Unit Root Based on Partially Adaptive Estimation
  4. On Convergence of the QMLE for Misspecified GARCH Models
  5. Signal Extraction Revision Variances as a Goodness-of-Fit Measure
  6. A Nonlinear IV Likelihood-Based Rank Test for Multivariate Time Series and Long Panels
  7. Has the Volatility of U.S. Inflation Changed and How?
  8. Extended Fractional Gaussian Noise and Simple ARFIMA Approximations
  9. The PCSE Estimator is Good -- Just Not As Good As You Think
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