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QML Estimation of Spatial Dynamic Panel Data Models with Time Varying Spatial Weights Matrices

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  • Lung-fei Lee
  • Jihai Yu

Abstract

This paper investigates the quasi-maximum likelihood estimation of spatial dynamic panel data models where spatial weights matrices can be time varying. We find that QML estimate is consistent and asymptotically normal. We investigate marginal impacts of explanatory variables in this system via space--time multipliers. Monte Carlo results are reported to investigate the finite sample properties of QML estimates and marginal effects. When spatial weights matrices are substantially varying over time, a model misspecification of a time invariant spatial weights matrix may cause substantial bias in estimation. Slowly time varying spatial weights matrices would be of less concern. RÉSUMÉ la présente communication se penche sur l'estimation du quasi maximum de vrai semblance de modèles de données du groupe des dynamiques spatiales, où les matrices de poids spatiales peuvent varier en fonction du temps. Nous relevons que l'estimation de QML est homogène et normale sur un plan asymptotique. Nous nous penchons sur des impacts marginaux de variables causales dans ce système, par le biais de multiplicateurs spatio-temporels. Des résultats Monte Carlo sontfournis pour l'examen d’échantillons finis d'estimations QML et d'effets marginaux. Lorsque les matrices de poids spatiales varient de façon substantielle avec le temps, une erreur de spécification de modèle d'une matrice de poids spatiale ne variant pas avec le temps risquerait de fausser sensiblement les estimations. Les matrice de poids spatiale variant avec le temps auraientune importance moindre. RESUMEN Este estudio investiga la estimación casi-máxima de probabilidad de semejanza de modelos dinámicos de datos de panel en donde las matrices ponderadas espaciales pueden variar con el tiempo. Indicamos que la estimación QML es constante y asimptóticamente normal. Investigamos impactos marginales de variables explicativas en este sistema mediante multiplicadores espacio-temporales. Se informan los resultados de Monte Carlo para investigar las propiedades de muestra finitas de las estimaciones QML y los efectos marginales. Cuando las matrices ponderadas espaciales varían considerablemente en el tiempo, los errores de especificación del modelo para una matriz ponderada espacial invariable en el tiempopodrían causar una considerable parcialidad en la estimación. Las matrices de pesos espaciales variables lentos serían menos preocupantes.

Suggested Citation

  • Lung-fei Lee & Jihai Yu, 2012. "QML Estimation of Spatial Dynamic Panel Data Models with Time Varying Spatial Weights Matrices," Spatial Economic Analysis, Taylor & Francis Journals, vol. 7(1), pages 31-74, March.
  • Handle: RePEc:taf:specan:v:7:y:2012:i:1:p:31-74
    DOI: 10.1080/17421772.2011.647057
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    References listed on IDEAS

    as
    1. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2012. "Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration," Journal of Econometrics, Elsevier, vol. 167(1), pages 16-37.
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    Cited by:

    1. Lee, Lung-fei & Yu, Jihai, 2015. "Estimation of fixed effects panel regression models with separable and nonseparable space–time filters," Journal of Econometrics, Elsevier, vol. 184(1), pages 174-192.
    2. Zhou, Yiwei & Wang, Xiaokun & Holguín-Veras, José, 2016. "Discrete choice with spatial correlation: A spatial autoregressive binary probit model with endogenous weight matrix (SARBP-EWM)," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 440-455.
    3. Ana Angulo & Peter Burridge & Jesús Mur, 2017. "Testing for breaks in the weighting matrix," Documentos de Trabajo dt2017-01, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    4. Herrera Gómez, Marcos, 2017. "Fundamentos de Econometría Espacial Aplicada
      [Fundamentals of Applied Spatial Econometrics]
      ," MPRA Paper 80871, University Library of Munich, Germany.
    5. Júlia Gallego Ziero Uhr & André Luis Squarize Chagas, Daniel de Abreu Pereira Uhr, Renan Porn Peres, 2017. "A study on environmental infractions for Brazilian municipalities: a spatial dynamic panel approach," Working Papers, Department of Economics 2017_13, University of São Paulo (FEA-USP).
    6. repec:eee:regeco:v:69:y:2018:i:c:p:48-68 is not listed on IDEAS
    7. Clément Gorin, 2017. "Accessibility, absorptive capacity and innovation in European urban areas," Working Papers 1722, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    8. Marcos Herrera & Jesus Mur & Manuel Ruiz-Marin, 2017. "A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix," Working Papers 18, Instituto de Estudios Laborales y del Desarrollo Económico (IELDE) - Universidad Nacional de Salta - Facultad de Ciencias Económicas, Jurídicas y Sociales.
    9. repec:eee:regeco:v:68:y:2018:i:c:p:115-129 is not listed on IDEAS
    10. Qu, Xi & Lee, Lung-fei & Yu, Jihai, 2017. "QML estimation of spatial dynamic panel data models with endogenous time varying spatial weights matrices," Journal of Econometrics, Elsevier, vol. 197(2), pages 173-201.
    11. Dou, Baojun & Parrella, Maria Lucia & Yao, Qiwei, 2016. "Generalized Yule–Walker estimation for spatio-temporal models with unknown diagonal coefficients," Journal of Econometrics, Elsevier, vol. 194(2), pages 369-382.
    12. Sun, Yiguo & Malikov, Emir, 2018. "Estimation and inference in functional-coefficient spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 203(2), pages 359-378.
    13. Wang, Wei & Yu, Jihai, 2015. "Estimation of spatial panel data models with time varying spatial weights matrices," Economics Letters, Elsevier, vol. 128(C), pages 95-99.
    14. repec:eee:regeco:v:65:y:2017:i:c:p:65-88 is not listed on IDEAS
    15. repec:spr:anresc:v:60:y:2018:i:1:d:10.1007_s00168-016-0789-y is not listed on IDEAS
    16. Clément Gorin, 2017. "Accessibility, absorptive capacity and innovation in European urban areas," Working Papers halshs-01584111, HAL.
    17. Ho, Chun-Yu & Wang, Wei & Yu, Jihai, 2013. "Growth spillover through trade: A spatial dynamic panel data approach," Economics Letters, Elsevier, vol. 120(3), pages 450-453.
    18. repec:eee:ecolet:v:162:y:2018:i:c:p:62-68 is not listed on IDEAS
    19. Han, Xiaoyi & Hsieh, Chih-Sheng & Lee, Lung-fei, 2017. "Estimation and model selection of higher-order spatial autoregressive model: An efficient Bayesian approach," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 97-120.
    20. Dou, Baojun & Parrella, Maria Lucia & Yao, Qiwei, 2016. "Generalized Yule–Walker estimation for spatio-temporal models with unknown diagonal coefficients," LSE Research Online Documents on Economics 67151, London School of Economics and Political Science, LSE Library.
    21. Wang, Wei & Lee, Lung-fei, 2013. "Estimation of spatial panel data models with randomly missing data in the dependent variable," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 521-538.
    22. Han, Jaepil & Ryu, Deockhyun & Sickles, Robin, 2015. "How to Measure Spillover Effects of Public Capital Stock: A Spatial Autoregressive Stochastic Frontier Model," Working Papers 15-015, Rice University, Department of Economics.
    23. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
    24. Taspinar, Suleyman & Dogan, Osman & Bera, Anil K., 2017. "GMM Gradient Tests for Spatial Dynamic Panel Data Models," MPRA Paper 82830, University Library of Munich, Germany.
    25. Burridge, Peter & Iacone, Fabrizio & Lazarová, Štěpána, 2015. "Spatial effects in a common trend model of US city-level CPI," Regional Science and Urban Economics, Elsevier, vol. 54(C), pages 87-98.

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