Estimating Standard Errors For The Parks Model: Can Jackknifing Help?

W. Reed () and Rachel S. Webb

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

Abstract: 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.

Keywords: Panel Data estimation; Parks model; cross-sectional correlation; jackknife; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C15 C23 (search for similar items in EconPapers)
Pages: 12 pages
Date: 2009-11-15
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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https://repec.canterbury.ac.nz/cbt/econwp/0918.pdf (application/pdf)

Related works:
Journal Article: Estimating standard errors for the Parks model: Can jackknifing help? (2011)
Working Paper: Estimating standard errors for the Parks model: Can jackknifing help? (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:cbt:econwp:09/18

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