Abstract

Nonparametric kernel methods have become an integral part of the applied econometrician’s toolkit. Their appeal, for applied researchers at least, lies in their ability to reveal structure in data that might be missed by classical parametric methods. Basic kernel methods are now found in virtually all popular statistical and econometric software programs. Such programs contain routines for the estimation of an unknown density function defined over a real-valued continuous random variable, or for the estimation of an unknown bivariate regression model defined over a real-valued continuous regressor. For example, the R platform for statistical computing and graphics (R Development Core Team 2008) includes the function density that computes a univariate kernel density estimate supporting a variety of kernel functions and bandwidth methods, while the locpoly function in the R “KernSmooth” package (Wand and Ripley 2008) can be used to estimate a bivariate regression function and its derivatives using a local polynomial kernel estimator with a fast binned bandwidth selector.