A review of spatial econometric models for count data

Stephanie Glaser

No 19-2017, Hohenheim Discussion Papers in Business, Economics and Social Sciences from University of Hohenheim, Faculty of Business, Economics and Social Sciences

Abstract: Despite the increasing availability of spatial count data in research areas like technology spillovers, patenting activities, insurance payments, and crime forecasting, specialized models for analysing such data have received little attention in econometric literature so far. The few existing approaches can be broadly classified into observation-driven models, where the random spatial effects enter the moments of the dependent variable directly, and parameterdriven models, where the random spatial effects are unobservable and induced via a latent process. Moreover, within these groups the modelling approaches (and therefore the interpretation) of spatial effects are quite heterogeneous, stemming in part from the nonlinear structure of count data models. The purpose of this survey is to compare and contrast the various approaches for econometric modelling of spatial counts discussed in the literature.

Date: 2017
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hohdps:192017

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