This paper introduces Stata commands [R] npiv and [R] npivcv, which implement nonparametric instrumental variable (NPIV) estimation methods without and with a cross-validated choice of tuning parameters, respectively. Both commands are able to impose the constraint that the resulting estimated function is monotone. The use of such a shape restriction may significantly improve the performance of the NPIV estimator (Chetverikov and Wilhelm 2017). This is because the ill-posedness of the NPIV estimation problem leads to unconstrained estimators that suffer from particularly poor statistical properties such as very high variance. The constrained estimator that imposes the monotonicity, on the other hand, significantly reduces variance ...
We study a Tikhonov Regularized (TiR) estimator of a functional parameter identified by conditional ...
The focus of this paper is the nonparametric estimation of an instrumental regression function ϕ def...
We consider the nonparametric regression model with an additive error that is correlated with the ex...
The ill-posedness of the inverse problem of recovering a regression function in a nonparametric inst...
In my PhD thesis, I develop nonparametric instrumental variable methods for two different settings. ...
In this paper we examine the finite sample performance of two estimators one developed by Blundell, ...
Instrumental variables are widely used in applied statistics and econometrics to achieve identificat...
This paper considers asymptotically efficient instrumental variables estimation of nonlinear models ...
We consider the problem of estimating the value `(ϕ) of a linear functional, where the structural fu...
This paper considers the problem of choosing the regularization parameter and the smoothing paramete...
We consider nonparametric kernel estimation of an instrumental regression function φ defined by cond...
Instrumental variable estimators can be severely biased in finite samples when the degree of overide...
This paper considers the nonparametric regression model with an additive error that is dependent on ...
We consider nonparametric estimation of a regression function that is identified by requiring a spec...
The use of a nonparametrically generated instrumental variable in estimating a single-equation linea...
We study a Tikhonov Regularized (TiR) estimator of a functional parameter identified by conditional ...
The focus of this paper is the nonparametric estimation of an instrumental regression function ϕ def...
We consider the nonparametric regression model with an additive error that is correlated with the ex...
The ill-posedness of the inverse problem of recovering a regression function in a nonparametric inst...
In my PhD thesis, I develop nonparametric instrumental variable methods for two different settings. ...
In this paper we examine the finite sample performance of two estimators one developed by Blundell, ...
Instrumental variables are widely used in applied statistics and econometrics to achieve identificat...
This paper considers asymptotically efficient instrumental variables estimation of nonlinear models ...
We consider the problem of estimating the value `(ϕ) of a linear functional, where the structural fu...
This paper considers the problem of choosing the regularization parameter and the smoothing paramete...
We consider nonparametric kernel estimation of an instrumental regression function φ defined by cond...
Instrumental variable estimators can be severely biased in finite samples when the degree of overide...
This paper considers the nonparametric regression model with an additive error that is dependent on ...
We consider nonparametric estimation of a regression function that is identified by requiring a spec...
The use of a nonparametrically generated instrumental variable in estimating a single-equation linea...
We study a Tikhonov Regularized (TiR) estimator of a functional parameter identified by conditional ...
The focus of this paper is the nonparametric estimation of an instrumental regression function ϕ def...
We consider the nonparametric regression model with an additive error that is correlated with the ex...