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Dealing with spatial data pooled over time in statistical models

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  • Jean Dubé

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  • Diègo Legros

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Abstract

Recent developments in spatial econometrics have been devoted to spatio-temporal data and how spatial panel data structure should be modeled. Little effort has been devoted to the way one must deal with spatial data pooled over time. This paper presents the characteristics of spatial data pooled over time and proposes a simple way to take into account unidirectional temporal effect as well as multidirectional spatial effect in the estimation process. An empirical example, using data on 25,357 single family homes sold in Lucas County, OH (USA), between 1993 and 1998 (available in the MatLab library), is used to illustrate the potential of the approach proposed. Copyright Springer-Verlag 2013

Suggested Citation

  • Jean Dubé & Diègo Legros, 2013. "Dealing with spatial data pooled over time in statistical models," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 1-18, March.
  • Handle: RePEc:spr:lsprsc:v:6:y:2013:i:1:p:1-18 DOI: 10.1007/s12076-012-0082-3
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Basile, Roberto & Durbán, María & Mínguez, Román & María Montero, Jose & Mur, Jesús, 2014. "Modeling regional economic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 229-245.
    2. Thanos, Sotirios & Dubé, Jean & Legros, Diègo, 2016. "Putting time into space: the temporal coherence of spatial applications in the housing market," Regional Science and Urban Economics, Elsevier, pages 78-88.
    3. Dubé, Jean & Legros, Diègo & Thériault, Marius & Des Rosiers, François, 2014. "A spatial Difference-in-Differences estimator to evaluate the effect of change in public mass transit systems on house prices," Transportation Research Part B: Methodological, Elsevier, pages 24-40.
    4. Jean Dubé & Cédric Brunelle, 2014. "Dots to dots: a general methodology to build local indicators using spatial micro-data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 245-272, August.
    5. Sunak, Yasin & Madlener, Reinhard, 2016. "The impact of wind farm visibility on property values: A spatial difference-in-differences analysis," Energy Economics, Elsevier, vol. 55(C), pages 79-91.
    6. Dubé, Jean & Thériault, Marius & Des Rosiers, François, 2013. "Commuter rail accessibility and house values: The case of the Montreal South Shore, Canada, 1992–2009," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 49-66.

    More about this item

    Keywords

    Spatio-temporal data; Weights matrix; Spatial econometrics; C21; C23; C51; C81; R15;

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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