When comparing two competing approximate models using a particular loss function, the one having smallest `expected true error' for that loss function is expected to lie closest to the underlying data generating process (DGP) given this loss function and is therefore to be preferred. In this...

Local polynomial regression is extremely popular in applied settings. Recent developments in shape constrained nonparametric regression allow practitioners to impose constraints on local polynomial estimators thereby ensuring that the resulting estimates are consistent with underlying theory....

We apply parametric and nonparametric regression discontinuity methodology within a multinomial choice setting to examine the impact of public health care user fee abolition on health facility choice using data from South Africa. The nonparametric model is found to outperform the parametric...

We consider the problem of estimating a relationship using semiparametric additive regression splines when there exist both continuous and categorical regressors, some of which are irrelevant but this is not known a priori. We show that choosing the spline degree, number of subintervals, and...

Nonparametric smoothing under shape constraints has recently received much well-deserved attention. Powerful methods have been proposed for imposing a single shape constraint such as monotonicity and concavity on univariate functions. In this paper, we extend the monotone kernel regression...

We consider the problem of estimating a relationship nonparametrically using regression splines when there exist both continuous and categorical predictors. We combine the global properties of regression splines with the local properties of categorical kernel functions to handle the presence of...

We propose a data-driven least squares cross-validation method to optimally select smoothing parameters for the nonparametric estimation of conditional cumulative distribution functions and conditional quantile functions. We allow for general multivariate covariates that can be continuous,...

In recent years, estimators for nonseparable models have been developed that rely on (an) instrumental variable(s) for identification. The exclusion restriction in triangular models can be reformulated and causally decomposed under the Settable Systems extension to the Pearl Causal Model due to...

Many practical problems require nonparametric estimates of regression functions, and local polynomial regression has emerged as a leading approach. In applied settings practitioners often adopt either the local constant or local linear variants, or choose the order of the local polynomial to be...

A class of kernel regression estimators is developed for a broad class of hierarchical models including the pooled regression estimator, the fixed-effect model familiar from panel data, etc. Separate shrinking is allowed for each coefficient. Regressors may be continuous or discrete. The...