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On model specification and parameter space definitions in higher order spatial econometric models

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Author Info

  • Elhorst, J. Paul
  • Lacombe, Donald J.
  • Piras, Gianfranco

Abstract

Higher-order spatial econometric models that include more than one weights matrix have seen increasing use in the spatial econometrics literature. There are two distinct issues related to the specification of these extended models. The first issue is what form the higher-order spatial econometric model takes, i.e. higher-order polynomials in the spatial weights matrices vs. higher-order spatial autoregressive processes. The second issue relates to the parameter space in such models and how this can affect the choice of model specification, estimation, and inference. We outline a procedure that is simple both mathematically and computationally for finding the stationary region for spatial econometric models with up to K weights matrices for higher-order spatial autoregressive processes. We also compare and contrast this approach with the parameter space for models that incorporate higher-order polynomials in the spatial weights matrices. Regardless of the model utilized in empirical practice, ignoring the relevant parameter region can lead to incorrect inferences regarding both the nature of the spatial autocorrelation process and the effects of changes in covariates on the dependent variable.

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Bibliographic Info

Article provided by Elsevier in its journal Regional Science and Urban Economics.

Volume (Year): 42 (2012)
Issue (Month): 1-2 ()
Pages: 211-220

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Handle: RePEc:eee:regeco:v:42:y:2012:i:1:p:211-220

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Related research

Keywords: Higher order spatial models; Parameter space; Spatial econometrics;

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References

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Citations

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Cited by:
  1. Elena Kotyrlo, 2013. "Stationarity conditions for the spatial first-order and serial second-order model," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 19-29, March.
  2. Nicolas Debarsy & Fei Jin & Lung-Fei Lee, 2013. "Large sample properties of the matrix exponential spatial specification with an application to FDI," Working Papers hal-00858174, HAL.
  3. Doğan, Osman & Taşpınar, Süleyman, 2014. "Spatial autoregressive models with unknown heteroskedasticity: A comparison of Bayesian and robust GMM approach," Regional Science and Urban Economics, Elsevier, vol. 45(C), pages 1-21.
  4. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
  5. Rodolfo Metulini & Paolo Sgrignoli & Stefano Schiavo & Massimo Riccaboni, 2014. "The migration network effect on international trade," Working Papers 5/2014, IMT Institute for Advanced Studies Lucca, revised May 2014.
  6. Doğan, Osman & Taşpınar, Süleyman, 2013. "GMM estimation of spatial autoregressive models with moving average disturbances," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 903-926.
  7. Osman Dogan, 2013. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term," Working Papers 002, City University of New York Graduate Center, Ph.D. Program in Economics.
  8. López-Hernández, Fernando A., 2013. "Second-order polynomial spatial error model. Global and local spatial dependence in unemployment in Andalusia," Economic Modelling, Elsevier, vol. 33(C), pages 270-279.
  9. Osman Dogan & Suleyman Taspinar, 2013. "GMM Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," Working Papers 001, City University of New York Graduate Center, Ph.D. Program in Economics.

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