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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.

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  • 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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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Antara Bhattacharyya & Sushil Kr. Haldar, 2020. "Socio-economic development and child sex ratio in India: revisiting the debate using spatial panel data regression," Journal of Social and Economic Development, Springer;Institute for Social and Economic Change, vol. 22(2), pages 305-327, December.
    6. Guilherme Mendes Resende & Alexandre Xavier Ywata Carvalho & Patrícia Alessandra Morita Sakowski & Túlio Antonio Cravo, 2016. "Evaluating multiple spatial dimensions of economic growth in Brazil using spatial panel data models," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 1-31, January.
    7. 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.
    8. 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.
    9. Chor Foon Tang & Mannir Salisu, 2023. "A note on national leadership and technology in moderating finance-growth nexus," Economics and Business Letters, Oviedo University Press, vol. 12(1), pages 68-74.
    10. Giuseppe Arbia, 2011. "A Lustrum of SEA: Recent Research Trends Following the Creation of the Spatial Econometrics Association (2007--2011)," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(4), pages 377-395, July.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. James E Payne & Junsoo Lee, 2024. "Global perspective on the permanent or transitory nature of shocks to tourist arrivals: Evidence from new unit root tests with structural breaks and factors," Tourism Economics, , vol. 30(1), pages 67-103, February.
    16. 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).
    17. 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.
    18. 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.
    19. Tao, Jiancong & Wang, Zhe & Xu, Yujie & Zhao, Boyu & Liu, Jiaqi, 2024. "Can the digital economy boost rural residents’ income? Evidence from China based on the spatial Durbin model," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 856-872.
    20. Payne, James E. & Lee, Junsoo & Islam, Md. Towhidul & Nazlioglu, Saban, 2022. "Stochastic convergence of per capita greenhouse gas emissions: New unit root tests with breaks and a factor structure," Energy Economics, Elsevier, vol. 113(C).

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    More about this item

    Keywords

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

    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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