Estimating Standard Errors For The Parks Model: Can Jackknifing Help?
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.
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| Length: | 12 pages |
| Date of creation: | 15 Nov 2009 |
| Handle: | RePEc:cbt:econwp:09/18 |
| Contact details of provider: | Postal: Private Bag 4800, Christchurch, New Zealand Phone: 64 3 369 3123 (Administrator) Fax: 64 3 364 2635 Web page: http://www.econ.canterbury.ac.nz More information through EDIRC
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References listed on IDEAS
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The Review of Economics and Statistics,
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- Doug Miller & A. Colin Cameron & Jonah B. Gelbach, 2006. "Bootstrap-Based Improvements for Inference with Clustered Errors," Working Papers 621, University of California, Davis, Department of Economics.
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- Sunil Sapra, 2002. "A jackknife maximum likelihood estimator for the probit model," Applied Economics Letters, Taylor & Francis Journals, vol. 9(2), pages 73-74. Full references (including those not matched with items on IDEAS)
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