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Panel Data Inference under Spatial Dependence

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Abstract

This paper focuses on inference based on the usual panel data estimators of a one-way error component regression model when the true specification is a spatial error component model. Among the estimators considered, are pooled OLS, random and fixed effects, maximum likelihood under normality, etc. The spatial effects capture the cross-section dependence, and the usual panel data estimators ignore this dependence. Two popular forms of spatial autocorrelation are considered, namely, spatial auto-regressive random effects (SAR-RE) and spatial moving average random effects (SMA-RE). We show that when the spatial coefficients are large, test of hypothesis based on the usual panel data estimators that ignore spatial dependence can lead to misleading inference.

Suggested Citation

  • Badi H. Baltagi & Alain Pirotte, 2010. "Panel Data Inference under Spatial Dependence," Center for Policy Research Working Papers 123, Center for Policy Research, Maxwell School, Syracuse University.
  • Handle: RePEc:max:cprwps:123
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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Baltagi, Badi H., 1981. "Pooling : An experimental study of alternative testing and estimation procedures in a two-way error component model," Journal of Econometrics, Elsevier, vol. 17(1), pages 21-49, September.
    3. Bernard Fingleton, 2008. "A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(1), pages 27-44.
    4. Georges Bresson & Badi H. Baltagi & Alain Pirotte, 2007. "Panel unit root tests and spatial dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 339-360.
    5. Bernard Fingleton, 2008. "A generalized method of moments estimator for a spatial model with moving average errors, with application to real estate prices," Empirical Economics, Springer, vol. 34(1), pages 35-57, February.
    6. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    7. Anselin, Luc & Moreno, Rosina, 2003. "Properties of tests for spatial error components," Regional Science and Urban Economics, Elsevier, vol. 33(5), pages 595-618, September.
    8. Nerlove, Marc, 1971. "A Note on Error Components Models," Econometrica, Econometric Society, vol. 39(2), pages 383-396, March.
    9. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
    10. Swamy, P A V B & Arora, S S, 1972. "The Exact Finite Sample Properties of the Estimators of Coefficients in the Error Components Regression Models," Econometrica, Econometric Society, vol. 40(2), pages 261-275, March.
    11. Amemiya, Takeshi, 1971. "The Estimation of the Variances in a Variance-Components Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 12(1), pages 1-13, February.
    12. Wallace, T D & Hussain, Ashiq, 1969. "The Use of Error Components Models in Combining Cross Section with Time Series Data," Econometrica, Econometric Society, vol. 37(1), pages 55-72, January.
    13. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    14. Breusch, Trevor S., 1987. "Maximum likelihood estimation of random effects models," Journal of Econometrics, Elsevier, vol. 36(3), pages 383-389, November.
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    Cited by:

    1. Badi H. Baltagi & Long Liu, 2015. "Testing for Spacial Lag and Spatial Error Dependence in a Fixed Effects Panel Data Model Using Double Length Artificial Regressions," Center for Policy Research Working Papers 183, Center for Policy Research, Maxwell School, Syracuse University.
    2. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, vol. 191(1), pages 176-195.
    3. Guilherme Mendes Resende & Alexandre Xavier Ywata de Carvalho & Patrícia Alessandra Morita Sakowski, 2013. "Evaluating Multiple Spatial Dimensions of Economic Growth in Brazil Using Spatial Panel Data Models (1970 - 2000)," Discussion Papers 1830a, Instituto de Pesquisa Econômica Aplicada - IPEA.
    4. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    5. Baltagi, Badi H. & Pirotte, Alain, 2014. "Prediction in a spatial nested error components panel data model," International Journal of Forecasting, Elsevier, vol. 30(3), pages 407-414.

    More about this item

    Keywords

    Panel data; Hausman test; Random effect; Spatial autocorrelation; Maximum Likelihood.;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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