Nonparametric and semiparametric methods in R

Abstract

The R environment for statistical computing and graphics (R Development Core Team, 2008) offers practitioners a rich set of statistical methods ranging from random number generation and optimization methods through regression, panel data, and time series methods, by way of illustration. The standard R distribution (base R) comes preloaded with a rich variety of functionality useful for applied econometricians. This functionality is enhanced by user-supplied packages made available via R servers that are mirrored around the world. Of interest in this chapter are methods for estimating nonparametric and semiparametric models. We summarize many of the facilities in R and consider some tools that might be of interest to those wishing to work with nonparametric methods who want to avoid resorting to programming in C or Fortran but need the speed of compiled code as opposed to interpreted code such as Gauss or Matlab by way of example. We encourage those working in the field to strongly consider implementing their methods in the R environment thereby making their work accessible to the widest possible audience via an open collaborative forum.

Citation

Racine, J.S. (2009), "Nonparametric and semiparametric methods in R", Li, Q. and Racine, J.S. (Ed.) Nonparametric Econometric Methods (Advances in Econometrics, Vol. 25), Emerald Group Publishing Limited, Leeds, pp. 335-375. https://doi.org/10.1108/S0731-9053(2009)0000025014