Abstract Given the popularity of nonparametric methods in applied econometric research, it is beneficial if students have exposure to these methods. We provide a simple, heuristic overview that can be used to discuss smoothing and nonparametric density and regression estimation suitable for an...

Estimating gradients is of crucial importance across a broad range of applied economic domains. Here we consider data-driven bandwidth selection based on the gradient of an unknown regression function. This is a difficult problem given that direct observation of the value of the gradient is...

type="main" xml:id="obes12025-abs-0001" <title type="main">Abstract</title> <p>Empirical growth regressions typically include mean years of schooling as a proxy for human capital. However, empirical research often finds that the sign and significance of schooling depends on the sample of observations or the specification of...</p>

In this note we present νth-order kernel density derivative estimators using canonical higher-order kernels. These canonical rescalings uncouple the choice of kernel and scale factor. This approach is useful for selection of the order of the kernel in a data-driven procedure as well as for...

This note derives the general form of the asymptotic approximate mean integrated squared error for the q-variate, νth-order kernel density rth derivative estimator. This formula allows for normal reference rule-of-thumb bandwidths to be derived. We give tables for some of the most common cases...

Rosen's (1974) theory of hedonic prices is implemented econometrically using recently developed nonparametric techniques to examine the influence of qualitative factors on the price of a house. Our ability to smooth categorical variables leads to greater generalization in the valuation process...

A simple graphical approach to presenting results from nonlinear regression models is described. In the face of multiple covariates, ‘partial mean’ plots may be unattractive. The approach here is portable to a variety of settings and can be tailored to the specific application at hand. A...

Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparametric...

Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparamteric...