ASYMPTOTIC EFFICIENCY IN SEMIPARAMETRIC MODELS WITH CENSORING
ASYMPTOTIC EFFICIENCY IN SEMIPARAMETRIC MODELS WITH CENSORING
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DOI：
10.1016/0304-4076(86)90038-2
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发表时间：
1986-07-01
影响因子：
6.3
通讯作者：
CHAMBERLAIN, G
中科院分区：
文献类型：
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作者：
CHAMBERLAIN, G
我们考虑了一个双变量截尾的回归模型，其中误差与解释变量无关。我们的目标是在误差分布除光滑性和正则性条件外不受限制的情况下，放弃双变量正态分布的假设，得到渐近有效性的一个信息界。这个界限具有简单的形式；如果对选择方程没有排除限制，那么正信息需要对回归斜率参数和解释变量的一个分量的连续分布的限制。我们还考虑了在解释变量的误差分布中值为零的弱假设下的二元选择模型。这里的半参数信息界为零。因此，尽管存在一致的估计量，但不可能以1n的速度收敛。
We consider a regression model subject to bivariate censoring in which the errors are independent of the explanatory variables. Our objective is to drop the assumption of bivaraite normality and obtain an information bound on asymptotic efficiency when the error distribution is unrestricted except for smoothness and regularity conditions. This bound has simple form; if there are no exclusion restrictions on the selection equation, then positive information requires a restriction on the regression slope parameters and a continuous distribution for a component of the explanatory variables. We also consider a binary choice model under the weak assumption that the error distribution has zero median conditional on the explanatory variables. Here the semi-parametric information bound is zero. Hence, although a consistent estimator exists, it is not possible to attain covergence at rate 1 n.