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SEMIPARAMETRIC ESTIMATION OF CENSORED SPATIAL AUTOREGRESSIVE MODELS

  • Tadao Hoshino (a1)
Abstract

This study considers the estimation of spatial autoregressive models with censored dependent variables, where the spatial autocorrelation exists within the uncensored latent dependent variables. The estimator proposed in this paper is semiparametric, in the sense that the error distribution is not parametrically specified and can be heteroskedastic. Under a median restriction, we show that the proposed estimator is consistent and asymptotically normally distributed. As an empirical illustration, we investigate the determinants of the risk of assault and other violent crimes including injury in the Tokyo metropolitan area.

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Corresponding author
*Address correspondence to Tadao Hoshino, School of Political Science and Economics, Waseda University, 1-6-1 Nishi-waseda, Shinjuku-ku, Tokyo 169-8050, Japan; e-mail: thoshino@waseda.jp.
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I thank Mamoru Amemiya for allowing me to use his data set, and the participants of the ESEM 2016 and the CUHK econometrics seminar 2017 for valuable suggestions. This work was supported financially by JSPS Grant-in-Aid for Young Scientists B-15K17039.

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References
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Econometric Theory
  • ISSN: 0266-4666
  • EISSN: 1469-4360
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