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- W2083979183 abstract "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 1n." @default.
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- W2083979183 date "1986-07-01" @default.
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- W2083979183 title "Asymptotic efficiency in semi-parametric models with censoring" @default.
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- W2083979183 doi "https://doi.org/10.1016/0304-4076(86)90038-2" @default.
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