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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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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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    More about this item

    Keywords

    House Prices; Panel Data; Spatial Lag; Nested Random Effects; In-strumental Variables; Spatial Dependence;
    All these keywords.

    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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