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Fixed Effects and Random Effects Estimation of Higher-Order Spatial Autoregressive Models with Spatial Autoregressive and Heteroskedastic Disturbances

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  • Harald Badinger

    (Department of Economics, Vienna University of Economics and Business)

  • Peter Egger

    (Department of Management, Technology and Economics at ETH Zürich)

Abstract

This paper develops a unified framework for fixed and random effects estimation of higher-order spatial autoregressive panel data models with spatial autoregressive disturbances and heteroskedasticity of unknown form in the idiosyncratic error component. 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 both a random effects and a fixed effects spatial generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators and derive their joint asymptotic distribution, which is robust to heteroskedasticity of unknown form in the idiosyncratic error component. Finally, we derive a robust Hausman-test of the spatial random against the spatial fixed effects model.

Suggested Citation

  • Harald Badinger & Peter Egger, 2014. "Fixed Effects and Random Effects Estimation of Higher-Order Spatial Autoregressive Models with Spatial Autoregressive and Heteroskedastic Disturbances," Department of Economics Working Papers wuwp173, Vienna University of Economics and Business, Department of Economics.
    • Handle: RePEc:wiw:wiwwuw:wuwp173
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    Cited by:

    1. Baltagi, Badi H. & Pirotte, Alain & Yang, Zhenlin, 2021. "Diagnostic tests for homoskedasticity in spatial cross-sectional or panel models," Journal of Econometrics, Elsevier, vol. 224(2), pages 245-270.
    2. 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.
    3. Mitze, Timo & Naveed, Amjad & Ahmad, Nisar, 2016. "International, intersectoral, or unobservable? Measuring R&D spillovers under weak and strong cross-sectional dependence," Journal of Macroeconomics, Elsevier, vol. 50(C), pages 259-272.
    4. Peter Egger & Andreas Lindenblatt, 2015. "Endogenous risk-taking and physical appearance of sex workers," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(9), pages 941-949, December.
    5. Harald Badinger & Peter Egger, 2016. "Productivity Spillovers Across Countries and Industries: New Evidence From OECD Countries," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(4), pages 501-521, August.
    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. Simon Bösenberg & Peter H. Egger & Valeria Merlo & Georg Wamser, 2018. "Measuring The Interdependence Of Multinational Firms' Foreign Investments," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 1064-1088, April.
    8. Chongshi Gu & Xiao Fu & Chenfei Shao & Zhongwen Shi & Huaizhi Su, 2020. "Application of Spatiotemporal Hybrid Model of Deformation in Safety Monitoring of High Arch Dams: A Case Study," IJERPH, MDPI, vol. 17(1), pages 1-25, January.
    9. 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; Heteroskedasticity; Two-stage least squares; Asymptotic statistics;
    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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