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Spatial Methods

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  • Gibbons, Steve
  • Overman, Henry G.
  • Patacchini, Eleonora

Abstract

This chapter is concerned with methods for analyzing spatial data. After initial discussion of 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 into standard linear regression provides a flexible way of characterizing 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

  • Gibbons, Steve & Overman, Henry G. & Patacchini, Eleonora, 2015. "Spatial Methods," Handbook of Regional and Urban Economics, Elsevier.
  • Handle: RePEc:eee:regchp:5-115
    DOI: 10.1016/B978-0-444-59517-1.00003-9
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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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. repec:eee:regeco:v:65:y:2017:i:c:p:89-103 is not listed on IDEAS
    7. 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.

    More about this item

    Keywords

    Spatial analysis; Spatial econometrics; Neighborhood effects; Agglomeration; Weights matrix; R; C1; C5;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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