SEMIPARAMETRIC ESTIMATION of CENSORED SPATIAL AUTOREGRESSIVE MODELS

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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.

Original languageEnglish
Pages (from-to)48-85
Number of pages38
JournalEconometric Theory
Volume36
Issue number1
DOIs
Publication statusPublished - 2020 Feb 1

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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