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Spatial Methods

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  • Steve Gibbons
  • Henry G. Overman
  • Eleonora Patacchini

Abstract

This paper is concerned with methods for analysing spatial data. After initial discussion on the nature of spatial data, including the concept of randomness, we focus most of our attention on linear regression models that involve interactions between agents across space. The introduction of spatial variables in to standard linear regression provides a flexible way of characterising these interactions, but complicates both interpretation and estimation of parameters of interest. The estimation of these models leads to three fundamental challenges: the reflection problem, the presence of omitted variables and problems caused by sorting. We consider possible solutions to these problems, with a particular focus on restrictions on the nature of interactions. We show that similar assumptions are implicit in the empirical strategies - fixed effects or spatial differencing - used to address these problems in reduced form estimation. These general lessons carry over to the policy evaluation literature.

Suggested Citation

  • Steve Gibbons & Henry G. Overman & Eleonora Patacchini, 2014. "Spatial Methods," SERC Discussion Papers 0162, Spatial Economics Research Centre, LSE.
  • Handle: RePEc:cep:sercdp:0162
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    Cited by:

    1. Combes, Pierre-Philippe & Gobillon, Laurent, 2015. "The Empirics of Agglomeration Economies," Handbook of Regional and Urban Economics, Elsevier.
    2. Chirinko, Robert S. & Wilson, Daniel J., 2017. "Tax competition among U.S. states: Racing to the bottom or riding on a seesaw?," Journal of Public Economics, Elsevier, vol. 155(C), pages 147-163.
    3. Haller, Peter & Heuermann, Daniel F., 2016. "Job search and hiring in local labor markets: Spillovers in regional matching functions," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 125-138.
    4. Potnuru Kishen Suraj & Ankesh Gupta & Makkunda Sharma & Sourabh Bikas Paul & Subhashis Banerjee, 2017. "On monitoring development indicators using high resolution satellite images," Papers 1712.02282, arXiv.org, revised Jun 2018.
    5. Marcon, Eric & Puech, Florence, 2017. "A typology of distance-based measures of spatial concentration," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 56-67.
    6. Salvati, Luca & Sateriano, Adele & Grigoriadis, Efstathios & Carlucci, Margherita, 2017. "New wine in old bottles: The (changing) socioeconomic attributes of sprawl during building boom and stagnation," Ecological Economics, Elsevier, vol. 131(C), pages 361-372.
    7. repec:eee:regeco:v:65:y:2017:i:c:p:89-103 is not listed on IDEAS

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    Keywords

    Spatial analysis; spatial econometrics; neighbourhood effects; agglomeration; weights matrix;

    JEL classification:

    • R - Urban, Rural, Regional, Real Estate, and Transportation Economics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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