censored population. We then correct the derivative for the effects of the selection bias. We propose nonparametric and … semiparametric estimators for the derivative. As extensions, we discuss the cases of discrete regressors, measurement error in …

censored population. We then correct the derivative for the effects of the selection bias. We discuss nonparametric and … semiparametric estimators for the derivative. We also discuss the cases of discrete regressors and of endogenous regressors in both …

This paper develops methodology for nonparametric estimation of a polarization measure due to Anderson (2004) and …

This paper studies nonparametric estimation of conditional moment models in which the residual functions could be … nonsmooth with respect to the unknown functions of endogenous variables. It is a problem of nonparametric nonlinear instrumental … coincide with the known minimax optimal rates for the nonparametric mean IV regression. We illustrate the theory by two …

nonparametrically using instrumental variables. First, we derive the minimax optimal sup-norm convergence rates for nonparametric … functionals in nonparametric demand estimation when prices, and possibly incomes, are endogenous. The second is to establish the … functionals of h_0. Both applications provide new and useful tools for empirical research on nonparametric models with endogeneity …

This paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV …

In this selective review, we first provide some empirical examples that motivate the usefulness of semi-nonparametric … techniques in modelling economic and financial time series. We describe popular classes of semi-nonparametric dynamic models and … some temporal dependence properties. We then present penalized sieve extremum (PSE) estimation as a general method for semi-nonparametric …

nonparametric sample covariances have different uniform convergence rates depending on direction, a result that differs …, we establish uniform consistency of nonparametric kernel estimators of the coefficient functions in nonlinear …

We study the problem of nonparametric regression when the regressor is endogenous, which is an important nonparametric …