A Monte Carlo Study for Pure and Pretest Estimators of a Panel Data Model with Spatially Autocorrelated Disturbances
AbstractThis paper examines the consequences of model misspecification using a panel data model with spatially autocorrelated disturbances. The performance of several maximum likelihood estimators assuming different specifications for this model are compared using Monte Carlo experiments. These include (i) MLE of a random effects model that ignore the spatial correlation; (ii) MLE described in Anselin (1988) which assumes that the individual effects are not spatially autocorrelated; (iii) MLE described in Kapoor et al. (2006) which assumes that both the individual effects and the remainder error are governed by the same spatial autocorrelation; (iv) MLE descrdibed in Baltagi et al. (2006) which allows the spatial correlation parameter for the iondividual effects to be different from that of the remainder error term. The latter model encompasses the other models and allows the researcher to test these specifications as restrictions on the general model using LM and LR tests. In fact, based on these tests, we suggest a pretest estimator which is shown to perform well in Monte Carlo experiments, ranking a close second to the true MLE in mean squared error performance.
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Bibliographic InfoPaper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 98.
Length: 27 pages
Date of creation: Dec 2007
Date of revision:
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Panel data; Spatially autocorrelated residuals; Pretest estimator; Maximum-likelihood estimation;
Other versions of this item:
- Badi H. BALTAGI & Peter EGGER & Michael PFAFFERMAYR, 2007. "A Monte Carlo Study for Pure and Pretest Estimators of a Panel Data Model with Spatially Autocorrelated Disturbances," Annales d'Economie et de Statistique, ENSAE, issue 87-88, pages 11-38.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-01-05 (All new papers)
- NEP-ECM-2008-01-05 (Econometrics)
- NEP-ORE-2008-01-05 (Operations Research)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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Center for Policy Research Working Papers
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- Badi Baltagi & Alain Pirotte, 2011.
"Seemingly unrelated regressions with spatial error components,"
Springer, vol. 40(1), pages 5-49, February.
- Badi H. Baltagi & Alain Pirotte, 2010. "Seemingly Unrelated Regressions with Spatial Error Components," Center for Policy Research Working Papers 125, Center for Policy Research, Maxwell School, Syracuse University.
- Baltagi B-H. & Pirotte, 2010. "Seemingly Unrelated Regressions With Spatial Error Components," Working Papers ERMES 0902, ERMES, University Paris 2.
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