Semiparametric estimation of a regression model with an unknown transformation of the dependent variable
Semiparametric estimation of a regression model with an unknown transformation of the dependent variable
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DOI：
10.2307/2171926
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发表时间：
1996-01-01
期刊：
影响因子：
6.1
通讯作者：
Horowitz, JL
中科院分区：
文献类型：
--
作者：
Horowitz, JL
This paper presents a method for estimating the model Lambda(Y) = beta'X + U, where Y is a scalar, Lambda is an unknown increasing function, X is a vector of explanatory variables, beta is a vector of unknown parameters, and U has unknown cumulative distribution function F. It is not assumed that Lambda and F belong to known parametric families; they are estimated nonparametrically. This model generalizes a large number of widely used models that make stronger a priori assumptions about Lambda and/or F. The paper develops n(1/2)-consistent, asymptotically normal estimators of Lambda, F, and quantiles of the conditional distribution of Y. Estimators of beta that are n(1/2)-consistent and asymptotically normal already exist. The results of Monte Carlo experiments indicate that the new estimators work reasonably well in samples of size 100.
