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Maximum Likelihood Estimation of a General Unbalanced Spatial Random Effects Model: a Monte Carlo Study

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  • Michael Pfaffermayr

Abstract

Abstract This paper discusses the maximum likelihood estimator of a general unbalanced spatial random effects model with normal disturbances, assuming that some observations are missing at random. Monte Carlo simulations show that the maximum likelihood estimator for unbalanced panels performs well and that missing observations affect mainly the root mean square error. As expected, these estimates are less efficient than those based on the unobserved balanced model, especially if the share of missing observations is large or spatial autocorrelation in the error terms is pronounced. Estimation de vraisemblance maximale d'un modèle général d'effets aléatoires spatiaux déséquilibré: une étude Monte Carlo RÉSUMÉ La présente communication se penche sur l'estimateur du maximum de vraisemblance d'un modèle général d'effets aléatoires spatiaux déséquilibré avec des perturbations normales, en supposant l'absence aléatoire de certaines observations. Des simulations de Monte Carlo montrent que des groupes déséquilibrés se comporte bien, et que les observations manquantes affectent principalement l'erreur de la moyenne quadratique. Comme prévu, ces évaluations sont moins efficaces que celles qui sont basées sur le modèle équilibré non observé, notamment si la part des observations manquantes est importantes, ou l'on déclare une autocorrélation spatiale dans les termes d'erreur. Estimación de la probabilidad máxima de un modelo espacial general desequilibrado de efectos al azar: un estudio de Monte Carlo RÉSUMÉN Este trabajo discute el estimador de probabilidad máxima de un modelo espacial general desequilibrado de efectos al azar con alteraciones normales, suponiendo que faltan algunas observaciones al azar. Las simulaciones de Monte Carlo muestran que el estimador de probabilidad máxima para los paneles desequilibrados funciona satisfactoriamente, y que las observaciones omisas afectan principalmente al error de la media cuadrática. Como se suponía, estas estimaciones son menos eficientes que las basadas en el modelo equilibrado inadvertido, especialmente si la cantidad de omisiones es grande/o la autocorrelación en los términos de error es pronunciada.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:specan:v:4:y:2009:i:4:p:467-483
    DOI: 10.1080/17421770903317645
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. repec:adr:anecst:y:2007:i:87-88:p:02 is not listed on IDEAS
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    1. Bergantino, Angela S. & Capozza, Claudia & Intini, Mario, 2020. "Empirical investigation of retail fuel pricing: The impact of spatial interaction, competition and territorial factors," Energy Economics, Elsevier, vol. 90(C).
    2. Yun, Seong Do & Gramig, Benjamin M & Delgado, Michael S. & Florax, Raymond J.G.M., 2015. "Does Spatial Correlation Matter in Econometric Models of Crop Yield Response and Weather?," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205465, Agricultural and Applied Economics Association.
    3. Marcos Sanso-Navarro & María Vera-Cabello & Miguel Puente-Ajovín, 2020. "Regional convergence and spatial dependence: a worldwide perspective," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 65(1), pages 147-177, August.
    4. Peter Egger & Michael Pfaffermayr, 2016. "A generalized spatial error components model for gravity equations," Empirical Economics, Springer, vol. 50(1), pages 177-195, February.
    5. Hou, Zhezhi & Jin, Man & Kumbhakar, Subal C., 2020. "Productivity spillovers and human capital: A semiparametric varying coefficient approach," European Journal of Operational Research, Elsevier, vol. 287(1), pages 317-330.
    6. Antonia Schickinger & Alexandra Bertschi-Michel & Max P. Leitterstorf & Nadine Kammerlander, 2022. "Same same, but different: capital structures in single family offices compared with private equity firms," Small Business Economics, Springer, vol. 58(3), pages 1407-1425, March.
    7. Herculano, Miguel C., 2018. "The role of contagion in the transmission of financial stress," ESRB Working Paper Series 81, European Systemic Risk Board.

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    More about this item

    Keywords

    Unbalanced panel data; spatially autocorrelated disturbances; maximum likelihood estimation; C21; C23;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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