A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances

  • Harry H. Kelejian
  • Ingmar R. Prucha
Article

Abstract

Cross-sectional spatial models frequently contain a spatial lag of the dependent variable as a regressor or a disturbance term that is spatially autoregressive. In this article we describe a computationally simple procedure for estimating cross-sectional models that contain both of these characteristics. We also give formal large-sample results.

Spatial autoregressive model two-stage least squares generalized moments estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amemiya, T. (1985). Advanced Econometrics. Cambridge, MA: Harvard University Press.Google Scholar
  2. Anselin, L. A. (1982) “New Look at Statistical Model Identification,” IEEE Transactions on Automatic Control AC 19, 716-723.Google Scholar
  3. Anselin, L. (1988). Spatial Econometrics: Methods and Models. Boston: Kluwer.Google Scholar
  4. Anselin, L. (1990). “Some Robust Approaches to Testing and Estimation in Spatial Econometrics,” Regional Science and Urban Economics 20, 141-163.Google Scholar
  5. Anselin, L., A. Bera, R. Florax, and M. Yoon. (1994). “Simple Diagnostic Tests for Spatial Dependence,” Regional Science and Urban Economics 26, 77-104.Google Scholar
  6. Anselin, L., and S. Rey. (1991). “Properties of Tests for Spatial Dependence in Linear Regression Models,” Geographical Analysis 23, 110-131.Google Scholar
  7. Bell, K., and N. Bockstael. (1997). “Applying the Generalized Method of Moments Approach to Spatial Problems Involving Micro-Level Data.” Department of Agricultural and Resource Economics Working Paper 97-03, University of Maryland.Google Scholar
  8. Bierens, H. J. (1981). Robust Methods and Asymptotic Theory in Nonlinear Econometrics. Lecture Notes in Economics and Mathematical Systems 192. New York: Springer-Verlag.Google Scholar
  9. Blommestein, H. (1983). “Specification and Estimation of Spatial Econometric Models,” Regional Science and Urban Economics 13, 251-270.Google Scholar
  10. Case, A. (1991). “Spatial Patterns in Household Demand,” Econometrica 59, 953-966.Google Scholar
  11. Case, A. (1992). “Neighborhood Influence and Technological Change,” Regional Science and Urban Economics 22, 491-508.Google Scholar
  12. Case, A., J. Hines, Jr., and H. Rosen. (1993). “Budget Spillovers and Fiscal Policy Independence; Evidence from the States,” Journal of Public Economics 52, 285-307.Google Scholar
  13. Horn, R., and C. Johnson. (1985). Matrix Analysis. New York: Cambridge University Press.Google Scholar
  14. Kelejian, H. H., and D. Robinson. (1993). “A Suggested Method of Estimation for Spatial Interdependent Models with Autocorrelated Errors, and an Application to a County Expenditure Model,” Papers in Regional Science 72, 297-312.Google Scholar
  15. Kelejian, H. H., and I. R. Prucha. (1995). “A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model.” Department of Economics, University of Maryland, Working Paper 95-03 (forthcoming in International Economic Review).Google Scholar
  16. Ord, J. (1975). “Estimation Methods for Models of Spatial Interaction,” Journal of the American Statistical Association 70, 120-126.Google Scholar
  17. Pace, R., and R. Barry. (1996). “Sparse Spatial Autoregressions,” Statistics and Probability Letters 2158, 1-7.Google Scholar
  18. Pötscher, B. M., and I. R. Prucha. (1997). Dynamic Nonlinear Econometric Models, Asymptotic Theory. New York: Springer Verlag.Google Scholar
  19. Schmidt, P. (1976). Econometrics. New York: Marcel Dekker.Google Scholar
  20. Whittle, P. (1954). “On Stationary Processes in the Plane,” Biometrica 41, 434-449.Google Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Harry H. Kelejian
    • 1
  • Ingmar R. Prucha
    • 1
  1. 1.Department of EconomicsUniversity of MarylandCollege Park

Personalised recommendations