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

Author

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  • Badi H. Baltagi
  • Peter Egger
  • Michael Pfaffermayr

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 autocorelation; (iv) MLE described in Baltagi, et al. [2006] which allows the spatial correlation parameter for the individual 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.

Suggested Citation

  • 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," Annals of Economics and Statistics, GENES, issue 87-88, pages 11-38.
    • Handle: RePEc:adr:anecst:y:2007:i:87-88:p:11-38
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    Cited by:

    1. Badi Baltagi & Alain Pirotte, 2011. "Seemingly unrelated regressions with spatial error components," Empirical Economics, Springer, vol. 40(1), pages 5-49, February.
    2. Arnab Bhattacharjee & Sean Holly, 2011. "Structural interactions in spatial panels," Empirical Economics, Springer, vol. 40(1), pages 69-94, February.
    3. Arnab Bhattacharjee & Sean Holly, 2011. "Structural interactions in spatial panels," Empirical Economics, Springer, vol. 40(1), pages 69-94, February.
    4. Arnab Bhattacharjee & Taps Maiti & Dennis Petrie, 2014. "Spatial structures of health outcomes and health behaviours in Scotland: Evidence from the Scottish Health Survey," SEEC Discussion Papers 1401, Spatial Economics and Econometrics Centre, Heriot Watt University.
    5. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Correction to: Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 65(3), pages 1509-1509, September.
    6. 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.
    7. 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.
    8. Michael Pfaffermayr, 2009. "Maximum Likelihood Estimation of a General Unbalanced Spatial Random Effects Model: a Monte Carlo Study," Spatial Economic Analysis, Taylor & Francis Journals, vol. 4(4), pages 467-483.
    9. 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.

    More about this item

    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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