TY - JOUR
T1 - Semiparametric Spatial Autoregressive Models With Endogenous Regressors
T2 - With an Application to Crime Data
AU - Hoshino, Tadao
N1 - Funding Information:
The author is grateful to Editor Shakeeb Khan, the associate editor, and two anonymous referees for their constructive comments that greatly improved the article. The author also thanks Mamoru Amemiya for allowing him to use his dataset, and the participants of the IAAE 2015 for valuable suggestions. This work was supported financially by JSPS Grant-in-Aid for Young Scientists B-15K17039.
Publisher Copyright:
© 2018 American Statistical Association.
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
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U2 - 10.1080/07350015.2016.1146145
DO - 10.1080/07350015.2016.1146145
M3 - Article
AN - SCOPUS:85018190628
SN - 0735-0015
VL - 36
SP - 160
EP - 172
JO - Journal of Business and Economic Statistics
JF - Journal of Business and Economic Statistics
IS - 1
ER -
