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A Monte Carlo Study for Pure and Pretest Estimators of a Panel Data Model with Spatially Autocorrelated Disturbances

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Abstract

This 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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  • 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," Center for Policy Research Working Papers 98, Center for Policy Research, Maxwell School, Syracuse University.
  • Handle: RePEc:max:cprwps:98
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    1. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    2. Badi H. Baltagi & Peter Egger & Michael Pfaffermayr, 2013. "A Generalized Spatial Panel Data Model with Random Effects," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 650-685, August.
    3. Giles, Judith A & Giles, David E A, 1993. " Pre-test Estimation and Testing in Econometrics: Recent Developments," Journal of Economic Surveys, Wiley Blackwell, vol. 7(2), pages 145-197, June.
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    Cited by:

    1. Arnab Bhattacharjee & Sean Holly, 2011. "Structural interactions in spatial panels," Empirical Economics, Springer, vol. 40(1), pages 69-94, February.
    2. Badi Baltagi & Alain Pirotte, 2011. "Seemingly unrelated regressions with spatial error components," Empirical Economics, Springer, vol. 40(1), pages 5-49, February.
    3. Fingleton, Bernard, 2010. "Predicting the geography of house prices," LSE Research Online Documents on Economics 33507, London School of Economics and Political Science, LSE Library.
    4. Daniel Arribas-Bel & Julia Koschinsky & Pedro Amaral, 2012. "Improving the multi-dimensional comparison of simulation results: a spatial visualization approach," Letters in Spatial and Resource Sciences, Springer, vol. 5(2), pages 55-63, July.
    5. Bhattacharjee, Arnab & Maiti, Taps & Petrie, Dennis, 2014. "General equilibrium effects of spatial structure: Health outcomes and health behaviours in Scotland," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 286-297.

    More about this item

    Keywords

    Panel data; Spatially autocorrelated residuals; Pretest estimator; Maximum-likelihood estimation;

    JEL classification:

    • 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; Spatio-temporal Models

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