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GM Estimation of Higher Order Spatial Autoregressive Processes in Panel Data Error Component Models

Author

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  • Harald Badinger
  • Peter Egger

Abstract

This paper presents a generalized moments (GM) approach to estimating an R-th order spatial regressive process in a panel data error component model. We derive moment conditions to estimate the parameters of the higher order spatial regressive process and the optimal weighting matrix required to achieve asymptotic efficiency. We prove consistency of the proposed GM estimator and provide Monte Carlo evidence that it performs well also in reasonably small samples.

Suggested Citation

  • Harald Badinger & Peter Egger, 2008. "GM Estimation of Higher Order Spatial Autoregressive Processes in Panel Data Error Component Models," CESifo Working Paper Series 2301, CESifo.
    • Handle: RePEc:ces:ceswps:_2301
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    References listed on IDEAS

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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. 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.
    3. Kathleen P. Bell & Nancy E. Bockstael, 2000. "Applying the Generalized-Moments Estimation Approach to Spatial Problems Involving Microlevel Data," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 72-82, February.
    4. Harald Badinger & Peter Egger, 2008. "Intra- and Inter-Industry Productivity Spillovers in OECD Manufacturing: A Spatial Econometric Perspective," CESifo Working Paper Series 2181, CESifo.
    5. Baltagi, Badi H. & Heun Song, Seuck & Cheol Jung, Byoung & Koh, Won, 2007. "Testing for serial correlation, spatial autocorrelation and random effects using panel data," Journal of Econometrics, Elsevier, vol. 140(1), pages 5-51, September.
    6. Kelejian, Harry H. & Robinson, Dennis P., 1992. "Spatial autocorrelation : A new computationally simple test with an application to per capita county police expenditures," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 317-331, September.
    7. 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.
    8. 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, August.
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    Cited by:

    1. Harald Badinger & Peter Egger, 2008. "Horizontal versus Vertical Interdependence in Multinational Activity," CESifo Working Paper Series 2327, CESifo.

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    More about this item

    Keywords

    spatial models; panel data models; error component models;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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