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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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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.

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  • Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2013. "Spatial Lag Models with Nested Random Effects: An Instrumental Variable Procedure with an Application to English House Prices," Center for Policy Research Working Papers 161, Center for Policy Research, Maxwell School, Syracuse University.
  • Handle: RePEc:max:cprwps:161
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    1. Baltagi, Badi H. & Liu, Long, 2011. "Instrumental variable estimation of a spatial autoregressive panel model with random effects," Economics Letters, Elsevier, vol. 111(2), pages 135-137, May.
    2. Bernard Fingleton, 2008. "A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(1), pages 27-44.
    3. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    4. Lauridsen, J. & Kosfeld, R., 2004. "A wald Test for Spatial Nonstationarity," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 22, pages 1-12, Diciembre.
    5. Bernard Fingleton & Julie Le Gallo, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: Finite sample properties," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 319-339, August.
    6. Bernard Fingleton, 2008. "A generalized method of moments estimator for a spatial model with moving average errors, with application to real estate prices," Empirical Economics, Springer, vol. 34(1), pages 35-57, February.
    7. Lung-Fei Lee & Jihai Yu, 2013. "Near Unit Root in the Spatial Autoregressive Model," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 314-351, September.
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    26. Antweiler, Werner, 2001. "Nested random effects estimation in unbalanced panel data," Journal of Econometrics, Elsevier, vol. 101(2), pages 295-313, April.
    27. Badi Baltagi & Seuck Heun Song & Byoung Cheol Jung, 2002. "Simple Lm Tests For The Unbalanced Nested Error Component Regression Model," Econometric Reviews, Taylor & Francis Journals, vol. 21(2), pages 167-187.
    28. Bernard Fingleton, 2006. "A cross-sectional analysis of residential property prices: the effects of income, commuting, schooling, the housing stock and spatial interaction in the English regions," Papers in Regional Science, Wiley Blackwell, vol. 85(3), pages 339-361, August.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Gupta, A, 2015. "Estimation of Spatial Autoregressions with Stochastic Weight Matrices," Economics Discussion Papers 15617, University of Essex, Department of Economics.
    6. 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.
    7. 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.
    8. Bo Pieter Johannes Andree & Francisco Blasques & Eric Koomen, 2017. "Smooth Transition Spatial Autoregressive Models," Tinbergen Institute Discussion Papers 17-050/III, Tinbergen Institute.
    9. repec:esx:essedp:772 is not listed on IDEAS

    More about this item

    Keywords

    House Prices; Panel Data; Spatial Lag; Nested Random Effects; In-strumental 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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