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Spatial lag models with nested random effects: An instrumental variable procedure with an application to English house prices

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  • Baltagi, Badi H.
  • Fingleton, Bernard
  • Pirotte, Alain

Abstract

This paper sets up a nested random effects spatial autoregressive panel data model to explain annual house price variation for 2000–2007 across 353 local authority districts in England. The estimation problem posed is how to allow for the endogeneity of the spatial lag variable producing the simultaneous spatial spillover of prices across districts together with the nested random effects in a panel data setting. To achieve this, the paper proposes new estimators based on the instrumental variable approaches of Kelejian and Prucha (1998) and Lee (2003) for the cross-sectional spatial autoregressive model. Monte Carlo results show that our estimators perform well relative to alternative approaches and produces estimates based on real data that are consistent with the theoretical house price model underpinning the reduced form.

Suggested Citation

  • Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2014. "Spatial lag models with nested random effects: An instrumental variable procedure with an application to English house prices," Journal of Urban Economics, Elsevier, vol. 80(C), pages 76-86.
  • Handle: RePEc:eee:juecon:v:80:y:2014:i:c:p:76-86
    DOI: 10.1016/j.jue.2013.10.006
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    References listed on IDEAS

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    6. Badi H. Baltagi & Chihwa Kao & Long Liu, 2013. "The Estimation and Testing of a Linear Regression with Near Unit Root in the Spatial Autoregressive Error Term," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 241-270, September.
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    Citations

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    Cited by:

    1. Gupta, A, 2015. "Estimation of Spatial Autoregressions with Stochastic Weight Matrices," Economics Discussion Papers 15617, University of Essex, Department of Economics.
    2. He, Ming & Lin, Kuan-Pin, 2015. "Testing spatial effects and random effects in a nested panel data model," Economics Letters, Elsevier, vol. 135(C), pages 85-91.
    3. Badi H. BALTAGI & Bernard FINGLETON & Alain PIROTTE, 2014. "Multilevel And Spillover Effects Estimated For Spatial Panel Data, With Application To English House Prices," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 40, pages 25-36.
    4. Cristina Bernini & Alessandro Tampieri, 2017. "The Happiness Function in Italian Cities," CREA Discussion Paper Series 17-07, Center for Research in Economic Analysis, University of Luxembourg.
    5. Łaszkiewicz Edyta & Dong Guanpeng & Harris Richard, 2014. "The Effect Of Omitted Spatial Effects And Social Dependence In The Modelling Of Household Expenditure For Fruits And Vegetables," Comparative Economic Research, De Gruyter Open, vol. 17(4), pages 155-172, December.
    6. Bo Pieter Johannes Andree & Francisco Blasques & Eric Koomen, 2017. "Smooth Transition Spatial Autoregressive Models," Tinbergen Institute Discussion Papers 17-050/III, Tinbergen Institute.
    7. Dobis, Elizabeth A. & Delgado, Michael S. & Florax, Raymond J.G.M & Mulder, Peter, 2015. "The Significance of Urban Hierarchy in Explaining Population Dynamics in the United States," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205869, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    8. repec:esx:essedp:772 is not listed on IDEAS
    9. Elizabeth Dobis & Michael Delgado & Raymond Florax & Peter Mulder, 2015. "Population Growth in American Cities between 1990 and 2010: True Contagion and Urban Hierarchy," ERSA conference papers ersa15p1128, European Regional Science Association.

    More about this item

    Keywords

    House prices; Panel data; Spatial lag; Nested random effects; Instrumental variables; Spatial dependence;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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