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Important overlooked IVs in spatial models

Author

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  • Harry H. Kelejian

    (University of Maryland College Park)

  • Gianfranco Piras

    (The Catholic University of America)

Abstract

Spatial models often contain additional endogenous variables as regressors. The complete system determining these variables is typically not known to the researcher, and so maximum likelihood or Bayesian estimation methods are precluded. This leaves instrumental variable estimation. In all likelihood, the system may contain certain forms of nonlinearities. These nonlinearities might arise because of endogenous weighting matrices, functional form differences in the endogenous variables, etc. The existence of such nonlinearities strongly suggests the use of nonlinear forms of the instruments. Issues of this sort were pointed out in Kelejian and Piras (Spatial econometrics, Elsevier, Amsterdam, 2017) and Kelejian (Lett Spat Resour Sci 9(1):113–136, 2016). However, thus far Monte Carlo results relating to efficiencies gained by the use of nonlinear instrumental variables are not available. This is unfortunate because these efficiencies can be quite extensive. The purpose of this paper is to fill this void.

Suggested Citation

  • Harry H. Kelejian & Gianfranco Piras, 2018. "Important overlooked IVs in spatial models," Empirical Economics, Springer, vol. 55(1), pages 69-83, August.
    • Handle: RePEc:spr:empeco:v:55:y:2018:i:1:d:10.1007_s00181-017-1414-3
    DOI: 10.1007/s00181-017-1414-3
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    References listed on IDEAS

    as
    1. Kelejian, Harry H. & Piras, Gianfranco, 2014. "Estimation of spatial models with endogenous weighting matrices, and an application to a demand model for cigarettes," Regional Science and Urban Economics, Elsevier, vol. 46(C), pages 140-149.
    2. 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.
    3. Bernard Fingleton & Julie Le Gallo, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: Finite sample properties," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 319-339, August.
    4. Julie Le Gallo & Bernard Fingleton, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances : finite sample properties," Post-Print hal-00485035, HAL.
    5. Harry H. Kelejian & Gianfranco Piras, 2016. "An Extension of the J‐Test to a Spatial Panel Data Framework," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 387-402, March.
    6. Mukerji, Purba, 2009. "Ready for capital account convertibility?," Journal of International Money and Finance, Elsevier, vol. 28(6), pages 1006-1021, October.
    7. Jeanty, P. Wilner & Partridge, Mark & Irwin, Elena, 2010. "Estimation of a spatial simultaneous equation model of population migration and housing price dynamics," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 343-352, September.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Harry Kelejian & Gianfranco Piras, 2020. "Spillover effects in spatial models: Generalizations and extensions," Journal of Regional Science, Wiley Blackwell, vol. 60(3), pages 425-442, June.
    2. Bernard Fingleton, 2023. "Estimating dynamic spatial panel data models with endogenous regressors using synthetic instruments," Journal of Geographical Systems, Springer, vol. 25(1), pages 121-152, January.

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    More about this item

    Keywords

    Spatial econometrics; Nonlinear IV; Endogeneity;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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