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

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  • Tadao Hoshino

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.

Suggested Citation

  • Tadao Hoshino, 2018. "Semiparametric Spatial Autoregressive Models With Endogenous Regressors: With an Application to Crime Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 160-172, January.
  • Handle: RePEc:taf:jnlbes:v:36:y:2018:i:1:p:160-172
    DOI: 10.1080/07350015.2016.1146145
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    Cited by:

    1. Luisa Alamá-Sabater & Teresa Fernández-Núñez & Miguel Ángel Márquez & Javier Salinas-Jimenez, 2020. "Do Countries with Similar Levels of Corruption Compete to Attract Foreign Investment? Evidence Using World Panel Data," Sustainability, MDPI, Open Access Journal, vol. 12(15), pages 1-1, July.
    2. Román Mínguez & Roberto Basile & María Durbán, 2020. "An alternative semiparametric model for spatial panel data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 669-708, December.
    3. Bo Pieter Johannes Andree & Francisco Blasques & Eric Koomen, 2017. "Smooth Transition Spatial Autoregressive Models," Tinbergen Institute Discussion Papers 17-050/III, Tinbergen Institute.

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