Semiparametric Spatial Autoregressive Models With Endogenous Regressors: With an Application to Crime Data

    研究成果: Article

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    This study considers semiparametric spatial autoregressive models that allow for endogenous regressors, as well as the heterogenous effects of these regressors across spatial units. For the model estimation, we propose a semiparametric series generalized method of moments estimator. We establish that the proposed estimator is both consistent and asymptotically normal. As an empirical illustration, we apply the proposed model and method to Tokyo crime data to estimate how the existence of a neighborhood police substation (NPS) affects the household burglary rate. The results indicate that the presence of an NPS helps reduce household burglaries, and that the effects of some variables are heterogenous with respect to residential distribution patterns. Furthermore, we show that using a model that does not adjust for the endogeneity of NPS does not allow us to observe the significant relationship between NPS and the household burglary rate. Supplementary materials for this article are available online.

    元の言語English
    ページ(範囲)160-172
    ページ数13
    ジャーナルJournal of Business and Economic Statistics
    36
    発行部数1
    DOI
    出版物ステータスPublished - 2018 1 2

    ASJC Scopus subject areas

    • Statistics and Probability
    • Social Sciences (miscellaneous)
    • Economics and Econometrics
    • Statistics, Probability and Uncertainty

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