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Panel data inference under spatial dependence

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  • Baltagi, Badi H.
  • Pirotte, Alain

Abstract

This paper focuses on inference based on the standard 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 autoregressive 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 standard panel data estimators that ignore spatial dependence can lead to misleading inference.

Suggested Citation

  • Baltagi, Badi H. & Pirotte, Alain, 2010. "Panel data inference under spatial dependence," Economic Modelling, Elsevier, vol. 27(6), pages 1368-1381, November.
  • Handle: RePEc:eee:ecmode:v:27:y:2010:i:6:p:1368-1381
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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, 2009. "A generalized method of moments estimator for a spatial model with moving average errors, with application to real estate prices," Studies in Empirical Economics, in: Giuseppe Arbia & Badi H. Baltagi (ed.), Spatial Econometrics, pages 35-57, Springer.
    6. 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.
    7. 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.
    8. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    9. 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.
    10. 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.
    11. 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.
    12. Nerlove, Marc, 1971. "A Note on Error Components Models," Econometrica, Econometric Society, vol. 39(2), pages 383-396, March.
    13. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
    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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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Mao, Guangyu & Shen, Yan, 2019. "Bubbles or fundamentals? Modeling provincial house prices in China allowing for cross-sectional dependence," China Economic Review, Elsevier, vol. 53(C), pages 53-64.
    4. Akgun, Oguzhan & Pirotte, Alain & Urga, Giovanni, 2020. "Forecasting using heterogeneous panels with cross-sectional dependence," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1211-1227.
    5. 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.
    6. Alain Pirotte & Jesús Mur, 2017. "Neglected dynamics and spatial dependence on panel data: consequences for convergence of the usual static model estimators," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(2-3), pages 202-229, July.
    7. Wolfgang Dauth & Reinhard Hujer & Katja Wolf, 2016. "Do Regions Benefit from Active Labour Market Policies? A Macroeconometric Evaluation Using Spatial Panel Methods," Regional Studies, Taylor & Francis Journals, vol. 50(4), pages 692-708, April.
    8. 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.
    9. 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.
    10. B. Fingleton & P. Cheshire & H. Garretsen & D. Igliori & J. Le Gallo & P. McCann & J. McCombie & V. Monastiriotis & B. Moore & M. Roberts, 2011. "Editorial," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(3), pages 243-248, September.
    11. Cheng, Yuanyuan & Yao, Xin, 2021. "Carbon intensity reduction assessment of renewable energy technology innovation in China: A panel data model with cross-section dependence and slope heterogeneity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    12. Fadhuile, A., 2018. "Can we explain pesticide price trend by the regulation changes ?," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277112, International Association of Agricultural Economists.
    13. Helmut Herwartz & Florian Siedenburg & Yabibal M. Walle, 2016. "Heteroskedasticity Robust Panel Unit Root Testing Under Variance Breaks in Pooled Regressions," Econometric Reviews, Taylor & Francis Journals, vol. 35(5), pages 727-750, May.

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    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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