期刊名称：Economics - The Open-Access, Open-Assessment E-Journal
印刷版ISSN：1864-6042
出版年度：2011
卷号：5
页码：1-14
出版社：Kiel Institute for the World Economy
摘要：Non-spherical errors, namely heteroscedasticity, serial correlation and cross-sectional correlation are commonly present within panel data sets. These can cause significant problems for econometric analyses. The FGLS(Parks) estimator has been demonstrated to produce considerable efficiency gains in these settings. However, it suffers from underestimation of coefficient standard errors, oftentimes severe. Potentially, jackknifing the FGLS(Parks) estimator could allow one to maintain the efficiency advantages of FGLS(Parks) while producing more reliable estimates of coefficient standard errors. Accordingly, this study investigates the performance of the jackknife estimator of FGLS(Parks) using Monte Carlo experimentation. We find that jackknifing can - in narrowly defined situations - substantially improve the estimation of coefficient standard errors. However, its overall performance is not sufficient to make it a viable alternative to other panel data estimators.
关键词：Panel data estimation; Parks model; cross-sectional correlation; jackknife; Monte Carlo
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