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Estimation and testing of higher-order spatial autoregressive panel data error component models

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  • Harald Badinger
  • Peter Egger

Abstract

This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define a generalized two-stage 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. Copyright Springer-Verlag 2013

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  • Harald Badinger & Peter Egger, 2013. "Estimation and testing of higher-order spatial autoregressive panel data error component models," Journal of Geographical Systems, Springer, vol. 15(4), pages 453-489, October.
    • Handle: RePEc:kap:jgeosy:v:15:y:2013:i:4:p:453-489
    DOI: 10.1007/s10109-012-0174-z
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    Cited by:

    1. Gupta, Abhimanyu & Robinson, Peter M., 2015. "Inference on higher-order spatial autoregressive models with increasingly many parameters," Journal of Econometrics, Elsevier, vol. 186(1), pages 19-31.
    2. Badi Baltagi & Peter Egger & Michaela Kesina, 2015. "Sources of productivity spillovers: panel data evidence from China," Journal of Productivity Analysis, Springer, vol. 43(3), pages 389-402, June.
    3. Gupta, Abhimanyu & Robinson, Peter M., 2018. "Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension," Journal of Econometrics, Elsevier, vol. 202(1), pages 92-107.
    4. Jakub Olejnik & Alicja Olejnik, 2017. "Improved asymptotic analysis of Gaussian QML estimators in spatial models," Lodz Economics Working Papers 9/2017, University of Lodz, Faculty of Economics and Sociology.
    5. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2022. "Bayesian estimation of multivariate panel probits with higher‐order network interdependence and an application to firms' global market participation in Guangdong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1356-1378, November.
    6. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2016. "Firm‐Level Productivity Spillovers in China's Chemical Industry: A Spatial Hausman‐Taylor Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 214-248, January.
    7. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2018. "Generalized spatial autocorrelation in a panel-probit model with an application to exporting in China," Empirical Economics, Springer, vol. 55(1), pages 193-211, August.

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    More about this item

    Keywords

    Higher-order spatial dependence; Generalized moments estimation; Two-stage least squares; Asymptotic statistics; C13; C21; C23;
    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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