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....

This paper addresses the following question: How much information do the kernel function and the bandwidth provide for nonparametric kernel estimation? The question is addressed by showing that kernel estimation of a cumulative distribution function (CDF) is an information transmission procedure...

This paper uses panel data and the Local Linear Kernel Estimator (LLKE) to investigate the effects of aid on economic growth in developing countries. Specifically, we investigate the robustness of a popular parametric specification of the aid/economic growth relationship in Less Developed...

In this paper we examine the net effect of several major tax changes in Australia on residential property prices. Specifically, we consider the announcement and introduction effects that resulted from several policy changes including the introduction of the Goods and Services Tax (GST) and the...

We propose a new technique for the estimation of multidimensional evaluation functions. Technical advances allow nonparametric inference on the joint distribution of continuous and discrete indicators of well-being, such as income and health, conditional on joint values of other continuous and...

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...

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,...

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...