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Testing for random effects and spatial lag dependence in panel data models

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  • Baltagi, Badi H.
  • Liu, Long

Abstract

This paper derives a joint Lagrange Multiplier (LM) test which simultaneously tests for the absence of spatial lag dependence and random individual effects in a panel data regression model. It turns out that this LM statistic is the sum of two standard LM statistics. The first one tests for the absence of spatial lag dependence ignoring the random individual effects, and the second one tests for the absence of random individual effects ignoring the spatial lag dependence. This paper also derives two conditional LM tests. The first one tests for the absence of random individual effects without ignoring the possible presence of spatial lag dependence. The second one tests for the absence of spatial lag dependence without ignoring the possible presence of random individual effects.

Suggested Citation

  • Baltagi, Badi H. & Liu, Long, 2008. "Testing for random effects and spatial lag dependence in panel data models," Statistics & Probability Letters, Elsevier, vol. 78(18), pages 3304-3306, December.
  • Handle: RePEc:eee:stapro:v:78:y:2008:i:18:p:3304-3306
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    References listed on IDEAS

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    1. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 239-253.
    2. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    3. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
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    Citations

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    Cited by:

    1. Salima Bouayad Agha & Nadine Turpin & Lionel Védrine, 2010. "Fostering the potential endogenous development of European regions: a spatial dynamic panel data analysis of the Cohesion Policy on regional convergence over the period 1980-2005," Working Papers halshs-00812077, HAL.
    2. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
    3. Shengrong Lu & Yanwu Wang, 2015. "Convergence, technological interdependence and spatial externalities: a spatial dynamic panel data analysis," Applied Economics, Taylor & Francis Journals, vol. 47(18), pages 1833-1846, April.
    4. Franziska Lottmann, 2012. "Explaining regional unemployment differences in Germany: a spatial panel data analysis," SFB 649 Discussion Papers SFB649DP2012-026, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Piras, Gianfranco & Prucha, Ingmar R., 2014. "On the finite sample properties of pre-test estimators of spatial models," Regional Science and Urban Economics, Elsevier, vol. 46(C), pages 103-115.
    6. Debarsy, Nicolas & Ertur, Cem, 2010. "Testing for spatial autocorrelation in a fixed effects panel data model," Regional Science and Urban Economics, Elsevier, vol. 40(6), pages 453-470, November.
    7. Filippini, M. & Heimsch, F. & Masiero, G., 2014. "Antibiotic consumption and the role of dispensing physicians," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 242-251.
    8. 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.
    9. Sorana VĂTAVU, 2015. "Determinants of Return on Assets in Romania: a Principal Component Analysis," Timisoara Journal of Economics and Business, West University of Timisoara, Romania, Faculty of Economics and Business Administration, vol. 8(1s), pages 32-47, February.
    10. Baylis, Katherine R. & Paulson, Nicholas D. & Piras, Gianfranco, 2011. "Spatial Approaches to Panel Data in Agricultural Economics: A Climate Change Application," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(3), pages 1-14, August.
    11. repec:rri:wpaper:201307 is not listed on IDEAS
    12. bouayad-agha-Hamouche, salima & turpin, nadine & védrine, lionel, 2012. "Fostering the potential endogenous development of European regions: a spatial dynamic panel data analysis of the Cohesion Policy," MPRA Paper 65470, University Library of Munich, Germany.
    13. Franziska Lottmann, 2012. "Regional Unemployment in Germany: a spatial panel data analysis," ERSA conference papers ersa12p53, European Regional Science Association.
    14. Merkel, Axel, 2017. "Spatial competition and complementarity in European port regions," Journal of Transport Geography, Elsevier, vol. 61(C), pages 40-47.
    15. Jouida, Sameh & Hellara, Slaheddine, 2018. "Diversification and target leverage of financial institutions," Journal of Multinational Financial Management, Elsevier, vol. 46(C), pages 11-35.
    16. He, Ming & Lin, Kuan-Pin, 2013. "Locally adjusted LM test for spatial dependence in fixed effects panel data models," Economics Letters, Elsevier, vol. 121(1), pages 59-63.
    17. dos Santos, Gervásio & Faria, Weslem, 2012. "Spatial Panel Data Models and Fuel Demand in Brazil," TD NEREUS 10-2012, Núcleo de Economia Regional e Urbana da Universidade de São Paulo (NEREUS).
    18. Harry H. Kelejian & Gianfranco Piras, 2016. "A J test for dynamic panel model with fixed effects, and nonparametric spatial and time dependence," Empirical Economics, Springer, vol. 51(4), pages 1581-1605, December.
    19. Bruno Lanz & Thomas F. Rutherford & John E. Tilton, 2013. "Subglobal Climate Agreements and Energy-intensive Activities: An Evaluation of Carbon Leakage in the Copper Industry," The World Economy, Wiley Blackwell, vol. 36(3), pages 254-279, March.
    20. Gianfranco Piras, 2013. "Efficient GMM Estimation of a Cliff and Ord Panel Data Model with Random Effects," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 370-388, September.
    21. Filippini, Massimo & Heimsch, Fabian, 2016. "The regional impact of a CO2 tax on gasoline demand: A spatial econometric approach," Resource and Energy Economics, Elsevier, vol. 46(C), pages 85-100.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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