%0 Journal Article %1 gorgens1999sec %A Gørgens, T. %A Horowitz, J.L. %D 1999 %I Elsevier %J Journal of Econometrics %K Semiparametricestimation;Transformationmodel;Empiricalprocess;Kaplan–Meierestimator;Proportionalhazardsmodel %N 2 %P 155--191 %R 10.1016/S0304-4076(98)00040-2 %T Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable %U http://linkinghub.elsevier.com/retrieve/pii/S0304407698000402 %V 90 %X This paper presents a method for estimating the model \bigwedge(Y)=min(\beta'X+U, C), where Y is a scalar, \bigwedge is an unknown increasing function, X is a vector of explanatory variables, \beta is a vector of unknown parameters, U has unknown cumulative distribution function F, and C is a censoring threshold. It is not assumed that \bigwedge and F belong to known parametric families; they are estimated nonparametrically. This model includes many widely used models as special cases, including the proportional hazards model with unobserved heterogeneity. The paper develops n1/2-consistent, asymptotically normal estimators of \bigwedge and F. Estimators of \beta that are n1/2-consistent and asymptotically normal already exist. The results of Monte Carlo experiments illustrate the finite-sample behavior of the estimators.

@article{gorgens1999sec, abstract = {This paper presents a method for estimating the model {\bigwedge}(Y)=min({\beta}'X+U, C), where Y is a scalar, {\bigwedge} is an unknown increasing function, X is a vector of explanatory variables, {\beta} is a vector of unknown parameters, U has unknown cumulative distribution function F, and C is a censoring threshold. It is not assumed that {\bigwedge} and F belong to known parametric families; they are estimated nonparametrically. This model includes many widely used models as special cases, including the proportional hazards model with unobserved heterogeneity. The paper develops n1/2-consistent, asymptotically normal estimators of {\bigwedge} and F. Estimators of {\beta} that are n1/2-consistent and asymptotically normal already exist. The results of Monte Carlo experiments illustrate the finite-sample behavior of the estimators.}, added-at = {2010-11-15T23:04:25.000+0100}, author = {G{\o}rgens, T. and Horowitz, J.L.}, biburl = {https://www.bibsonomy.org/bibtex/26ae0560836deb7d458b7a90995f8970d/usman.qadir}, doi = {10.1016/S0304-4076(98)00040-2}, file = {:E\:\\Users\\Schwan\\Documents\\Personal Digital Library\\Mincerian Wage Earnings\\9603001.pdf:PDF}, interhash = {fd9cf77089d1e08b824d807b32c1dd4d}, intrahash = {6ae0560836deb7d458b7a90995f8970d}, journal = {Journal of Econometrics}, keywords = {Semiparametricestimation;Transformationmodel;Empiricalprocess;Kaplan–Meierestimator;Proportionalhazardsmodel}, number = 2, pages = {155--191}, publisher = {Elsevier}, timestamp = {2010-11-15T23:04:25.000+0100}, title = {Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0304407698000402}, volume = 90, year = 1999 }