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

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)160-172
    Number of pages13
    JournalJournal of Business and Economic Statistics
    Volume36
    Issue number1
    DOIs
    Publication statusPublished - 2018 Jan 2

    Fingerprint

    Spatial Model
    Autoregressive Model
    police
    offense
    Endogeneity
    Generalized Method of Moments
    Moment Estimator
    Model
    Estimator
    Unit
    Series
    Endogenous regressors
    Crime
    Police
    Spatial autoregressive model
    Estimate
    Household

    Keywords

    • Endogeneity
    • Household burglary
    • Instrumental variables
    • Police
    • Semiparametric series estimation
    • Spatial autoregressive models

    ASJC Scopus subject areas

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

    Cite this

    @article{05f63c42c16a4e9380a8e7ac11fa4026,
    title = "Semiparametric Spatial Autoregressive Models With Endogenous Regressors: With an Application to Crime Data",
    abstract = "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.",
    keywords = "Endogeneity, Household burglary, Instrumental variables, Police, Semiparametric series estimation, Spatial autoregressive models",
    author = "Tadao Hoshino",
    year = "2018",
    month = "1",
    day = "2",
    doi = "10.1080/07350015.2016.1146145",
    language = "English",
    volume = "36",
    pages = "160--172",
    journal = "Journal of Business and Economic Statistics",
    issn = "0735-0015",
    publisher = "American Statistical Association",
    number = "1",

    }

    TY - JOUR

    T1 - Semiparametric Spatial Autoregressive Models With Endogenous Regressors

    T2 - With an Application to Crime Data

    AU - Hoshino, Tadao

    PY - 2018/1/2

    Y1 - 2018/1/2

    N2 - 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.

    AB - 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.

    KW - Endogeneity

    KW - Household burglary

    KW - Instrumental variables

    KW - Police

    KW - Semiparametric series estimation

    KW - Spatial autoregressive models

    UR - http://www.scopus.com/inward/record.url?scp=85018190628&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85018190628&partnerID=8YFLogxK

    U2 - 10.1080/07350015.2016.1146145

    DO - 10.1080/07350015.2016.1146145

    M3 - Article

    AN - SCOPUS:85018190628

    VL - 36

    SP - 160

    EP - 172

    JO - Journal of Business and Economic Statistics

    JF - Journal of Business and Economic Statistics

    SN - 0735-0015

    IS - 1

    ER -