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

Tadao Hoshino*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


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
Issue number1
Publication statusPublished - 2018 Jan 2


  • 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


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