Estimating standard errors for the Parks model: Can jackknifing help?
AbstractNon-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. --
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Bibliographic InfoArticle provided by Kiel Institute for the World Economy in its journal Economics: The Open-Access, Open-Assessment E-Journal.
Volume (Year): 5 (2011)
Issue (Month): 1 ()
Panel data estimation; Parks model; cross-sectional correlation; jackknife; Monte Carlo;
Other versions of this item:
- Reed, W. Robert & Webb, Rachel S., 2010. "Estimating standard errors for the Parks model: Can jackknifing help?," Economics Discussion Papers 2010-23, Kiel Institute for the World Economy.
- W. Robert Reed & Rachel S. Webb, 2009. "Estimating Standard Errors For The Parks Model: Can Jackknifing Help?," Working Papers in Economics 09/18, University of Canterbury, Department of Economics and Finance.
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
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