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  • 标题:Nonparametric Censored and Truncated Regression
  • 本地全文:下载
  • 作者:Arthur Lewbel ; Oliver Linton
  • 期刊名称:Distributional Analysis Publications
  • 印刷版ISSN:1352-2469
  • 出版年度:2000
  • 卷号:2000
  • 出版社:Suntory Toyota International Centres for Economics and Related Disciplines
  • 摘要:The nonparametric censored regression model, with a fixed, known censoringpoint (normalized to zero), is y = max[0,m(x) + e], where both the regressionfunction m(x) and the distribution of the error e are unknown. This paperprovides estimators of m(x) and its derivatives. The convergence rate is thesame as for an uncensored nonparametric regression and its derivatives. Wealso provide root n estimates of weighted average derivatives of m(x), whichequal the coefficients in linear or partly linearr specifications for m(x). Anextension permits estimation in the presence of a general form ofheteroscedasticity. We also extend the estimator to the nonparametrictruncated regression model, in which only uncensored data points areobserved. The estimators are based on the relationship.E(yk\x)/.m(x) = kE[yk-1/(y > 0)x ], which we show holds for positive integers k
  • 关键词:Semiparametric; nonparametric; censored regression; truncated;regression; Tobit; latent variable
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