Add to ORKGWe consider a semiparametric transformation model, in which the regression function has an additive nonparametric structure and the transformation of the response is assumed to belong to some parametric family. We suppose that endogeneity is present in the explanatory variables. Using a control function approach, we show that the pro- posed model is identified under suitable assumptions, and propose a profile likelihood estimation method for the transformation. The proposed estimator is shown to be asymptotically normal under certain regularity conditions. A small simulation study shows that the estimator behaves well in practice
This paper considers a linear regression model with an endogenous regressor which is not normally di...
This paper considers a linear regression model with an endogenous regressor which arises from a nonl...
This paper considers a linear regression model with an endogenous regressor which arises from a nonl...
We consider a semiparametric transformation model, in which the regression function has an additive ...
We consider a semiparametric transformation model, in which the regression function has an additive ...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression function has an additive ...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression function has an additive ...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
© 2018 Cambridge University Press. We consider a semiparametric transformation model, in which the r...
This paper considers a linear regression model with an endogenous regressor which is not normally di...
This paper considers a linear regression model with an endogenous regressor which arises from a nonl...
This paper considers a linear regression model with an endogenous regressor which arises from a nonl...
We consider a semiparametric transformation model, in which the regression function has an additive ...
We consider a semiparametric transformation model, in which the regression function has an additive ...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression function has an additive ...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression function has an additive ...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
We consider a semiparametric transformation model, in which the regression func- tion has an additiv...
© 2018 Cambridge University Press. We consider a semiparametric transformation model, in which the r...
This paper considers a linear regression model with an endogenous regressor which is not normally di...
This paper considers a linear regression model with an endogenous regressor which arises from a nonl...
This paper considers a linear regression model with an endogenous regressor which arises from a nonl...