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GM Estimation of Higher-Order Spatial Autoregressive Processes in Cross-Section Models with Heteroskedastic Disturbances

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Listed:
  • Harald Badinger
  • Peter Egger

Abstract

This paper generalizes the approach to estimating a first-order spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) in a cross-section with heteroskedastic innovations by Kelejian and Prucha (2008) to the case of spatial autoregressive models with spatial autoregressive disturbances of arbitrary (finite) order (SARAR(R,S)). We derive the moment conditions and the optimal weighting matrix for a generalized moments (GM) estimation procedure of the spatial regressive parameters of the disturbance process and define a generalized two-stages least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their (joint) asymptotic distribution, and provide Monte Carlo evidence on their small sample performance.

Suggested Citation

  • Harald Badinger & Peter Egger, 2008. "GM Estimation of Higher-Order Spatial Autoregressive Processes in Cross-Section Models with Heteroskedastic Disturbances," CESifo Working Paper Series 2356, CESifo Group Munich.
  • Handle: RePEc:ces:ceswps:_2356
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    File URL: http://www.cesifo-group.de/DocDL/cesifo1_wp2356.pdf
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    References listed on IDEAS

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    1. Besley, Timothy & Case, Anne, 1995. "Incumbent Behavior: Vote-Seeking, Tax-Setting, and Yardstick Competition," American Economic Review, American Economic Association, vol. 85(1), pages 25-45, March.
    2. Peter Egger & Horst Raff, 2015. "Tax rate and tax base competition for foreign direct investment," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 22(5), pages 777-810, October.
    3. Holtz-Eakin, Douglas, 1994. "Public-Sector Capital and the Productivity Puzzle," The Review of Economics and Statistics, MIT Press, vol. 76(1), pages 12-21, February.
    4. Badi Baltagi & Dong Li, 2000. "LM Tests for Functional Form and Spatial Correlation," Econometric Society World Congress 2000 Contributed Papers 0099, Econometric Society.
    5. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
    6. Joris Pinkse & Margaret E. Slade & Craig Brett, 2002. "Spatial Price Competition: A Semiparametric Approach," Econometrica, Econometric Society, vol. 70(3), pages 1111-1153, May.
    7. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    8. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    9. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    10. Irani Arraiz & David M. Drukker & Harry H. Kelejian & Ingmar R. Prucha, 2010. "A Spatial Cliff-Ord-Type Model With Heteroskedastic Innovations: Small And Large Sample Results," Journal of Regional Science, Wiley Blackwell, vol. 50(2), pages 592-614.
    11. Jeffrey P. Cohen & Catherine J. Morrison Paul, 2004. "Public Infrastructure Investment, Interstate Spatial Spillovers, and Manufacturing Costs," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 551-560, May.
    12. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    13. Baltagi, Badi H. & Egger, Peter & Pfaffermayr, Michael, 2007. "Estimating models of complex FDI: Are there third-country effects?," Journal of Econometrics, Elsevier, vol. 140(1), pages 260-281, September.
    14. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
    15. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    16. Shroder, Mark, 1995. "Games the States Don't Play: Welfare Benefits and the Theory of Fiscal Federalism," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 183-191, February.
    17. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
    18. Lee, Lung-fei, 2007. "GMM and 2SLS estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 137(2), pages 489-514, April.
    19. Jeffrey P. Cohen & Catherine Morrison Paul, 2007. "The Impacts Of Transportation Infrastructure On Property Values: A Higher-Order Spatial Econometrics Approach," Journal of Regional Science, Wiley Blackwell, vol. 47(3), pages 457-478.
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    Citations

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    Cited by:

    1. Harald Badinger & Peter Egger, 2015. "Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(1), pages 11-35, March.
    2. Harald Badinger & Peter Egger, 2009. "Estimation of Higher-Order Spatial Autoregressive Panel Data Error Component Models," CESifo Working Paper Series 2556, CESifo Group Munich.

    More about this item

    Keywords

    higher-order spatial dependence; heteroskedasticity; two-stages least squares; generalized moments estimation; asymptotics;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • 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

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