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

Tadao Hoshino*

*この研究の対応する著者

研究成果: Article査読

13 被引用数 (Scopus)

抄録

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.

本文言語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
1
DOI
出版ステータスPublished - 2018 1月 2

ASJC Scopus subject areas

  • 統計学および確率
  • 社会科学(その他)
  • 経済学、計量経済学
  • 統計学、確率および不確実性

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