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Bootstrap J -Test for Panel Data Models with Spatially Dependent Error Components, a Spatial Lag and Additional Endogenous Variables


  • Bernard Fingleton
  • Silvia Palombi


We develop a bootstrap J -test method for testing a panel model against one non-nested alternative when the competing specifications are estimated by Feasible Generalised Spatial Two Stage Least Squares/Generalised Method of Moments (FGS2SLS/GMM). Both models incorporate spatially correlated error components, thus accounting for spatial heterogeneity via random effects, and accommodate endogenous regressors other than the spatially lagged dependent variable. The proposed scheme is applied to a testing problem involving non-nested wage equations as motivated by the Wage Curve literature and the New Economic Geography theory. Results show that our bootstrap test is a reliable and effective procedure for correcting asymptotic reference critical values and distinguishing between the two rival hypotheses.

Suggested Citation

  • Bernard Fingleton & Silvia Palombi, 2016. "Bootstrap J -Test for Panel Data Models with Spatially Dependent Error Components, a Spatial Lag and Additional Endogenous Variables," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(1), pages 7-26, March.
  • Handle: RePEc:taf:specan:v:11:y:2016:i:1:p:7-26 DOI: 10.1080/17421772.2016.1102960

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    References listed on IDEAS

    1. Giuseppe Arbia, 2001. "articles: Modelling the geography of economic activities on a continuous space," Papers in Regional Science, Springer;Regional Science Association International, vol. 80(4), pages 411-424.
    2. Gilles Duranton & Henry G. Overman, 2005. "Testing for Localization Using Micro-Geographic Data," Review of Economic Studies, Oxford University Press, vol. 72(4), pages 1077-1106.
    3. A. J. Baddeley, 2000. "Non- and semi-parametric estimation of interaction in inhomogeneous point patterns," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 54(3), pages 329-350.
    4. Giuseppe Espa & Diego Giuliani & Giuseppe Arbia, 2010. "Weighting Ripley�s K-function to account for the firm dimension in the analysis of spatial concentration," Department of Economics Working Papers 1012, Department of Economics, University of Trento, Italia.
    5. repec:eee:ecomod:v:222:y:2011:i:23:p:3888-3894 is not listed on IDEAS
    6. Eric Marcon & Florence Puech, 2010. "Measures of the geographic concentration of industries: improving distance-based methods," Journal of Economic Geography, Oxford University Press, vol. 10(5), pages 745-762, September.
    7. Arbia, G. & Espa, G. & Giuliani, D. & Mazzitelli, A., 2012. "Clusters of firms in an inhomogeneous space: The high-tech industries in Milan," Economic Modelling, Elsevier, vol. 29(1), pages 3-11.
    8. Eric Marcon & Florence Puech, 2003. "Evaluating the geographic concentration of industries using distance-based methods," Journal of Economic Geography, Oxford University Press, vol. 3(4), pages 409-428, October.
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