Semiparametric Nonlinear Least Square Estimation of Truncated Regression Models
This article provides a semiparametric method for the estimation of truncated regression models where the disturbances are independent of the regressors before truncation. This independence property provides useful information on model identification and estimation. Our estimate is shown to be null-consistent and asymptotically normal. A consistent estimate of the asymptotic covariance matrix of the estimator is provided. Monte Carlo experiments are performed to investigate some finite sample properties of the estimator.
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|Date of creation:||1990|
|Date of revision:|
|Contact details of provider:|| Postal: UNIVERSITY OF MINNESOTA, CENTER FOR ECONOMIC RESEARCH, DEPARTMENT OF ECONOMICS, MINNEAPOLIS MINNESOTA 35455 U.S.A.|
Web page: http://www.econ.umn.edu/
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