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The Spatial Random Effects and the Spatial Fixed Effects Model. The Hausman Test in a Cliff and Ord Panel Model

Author

Listed:
  • Mutl, Jan

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

  • Pfaffermayr, Michael

    (Department of Economics, University of Innsbruck, Innsbruck, Austria and Austrian Institute of Economic Research, Vienna, Austria and CESIFO, Germany)

Abstract

This paper studies the spatial random effects and spatial fixed effects model. The model includes a Cliff and Ord type spatial lag of the dependent variable as well as a spatially lagged one-way error component structure, accounting for both heterogeneity and spatial correlation across units. We discuss instrumental variable estimation under both the fixed and the random effects specification and propose a spatial Hausman test which compares these two models accounting for spatial autocorrelation in the disturbances. We derive the large sample properties of our estimation procedures and show that the test statistic is asymptotically chi-square distributed. A small Monte Carlo study demonstrates that this test works well even in small panels.

Suggested Citation

  • Mutl, Jan & Pfaffermayr, Michael, 2008. "The Spatial Random Effects and the Spatial Fixed Effects Model. The Hausman Test in a Cliff and Ord Panel Model," Economics Series 229, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:229
    as

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    File URL: https://irihs.ihs.ac.at/id/eprint/1869
    File Function: First version, 2008
    Download Restriction: no
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    Citations

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    Cited by:

    1. Südekum Jens, 2010. "Human Capital Externalities and Growth of High- and Low-Skilled Jobs," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(1), pages 92-114, February.
    2. Fritz Breuss & Peter Egger & Michael Pfaffermayr, 2010. "Structural funds, EU enlargement, and the redistribution of FDI in Europe," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 146(3), pages 469-494, September.
    3. Jihai Yu & Lung-Fei Lee, 2012. "Convergence: A Spatial Dynamic Panel Data Approach," Global Journal of Economics (GJE), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 1-36.
    4. Fingleton, Bernard, 2010. "Predicting the geography of house prices," LSE Research Online Documents on Economics 33507, London School of Economics and Political Science, LSE Library.
    5. B. Merlevede & G. Rayp & S. Van Parys & T. Verbeke, 2011. "Do EU15 countries compete over labour taxes?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/750, Ghent University, Faculty of Economics and Business Administration.
    6. Ilaria Petrarca & Fabio Padovano, 2011. "From Taxes to Politics, from Politics to Taxes: Evidence of Yardstick Competition in the Italian Municipalities," Economics Working Paper from Condorcet Center for political Economy at CREM-CNRS 2011-01-ccr, Condorcet Center for political Economy.
    7. Moscone, Francesco & Tosetti, Elisa, 2010. "GMM estimation of Spatial Panels with Fixed Effects," MPRA Paper 20152, University Library of Munich, Germany.
    8. Mutl, Jan & Pfaffermayr, Michael, 2010. "A note on the Cliff and Ord test for spatial correlation in panel models," Economics Letters, Elsevier, vol. 108(2), pages 225-228, August.
    9. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.

    More about this item

    Keywords

    Spatial econometrics; Panel data; Random effects estimator; Within estimator; Hausman test;
    All these keywords.

    JEL classification:

    • 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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