In this article we overview nonparametric (spline and kernel) regression methods and illustrate how they may be used in …

We demonstrate that regression models can be estimated by working independently in a row-wise fashion. We document a … understanding the mechanics of many common regression models. These procedures can be used to speed up the computation of estimates … computed via OLS, IV, Ridge regression, LASSO, Elastic Net, and Non-linear models including probit and logit, with all common …

. -- Intergenerational income mobility ; top incomes ; earnings inequality ; income inequality ; welfare state ; non-linear regression … ; quantile regression …

This note argues that nonparametric regression not only relaxes functional form assumptions vis-a-vis parametric … regression, but that it also permits endogenous control variables. To control for selection bias or to make an exclusion … restriction in instrumental variables regression valid, additional control variables are often added to a regression. If any of …

In this paper we describe an alternative iterative approach for the estimation of linear regression models with high …

We investigate the problem of optimal choice of the smoothing parameter (bandwidth) for the regression discontinuity … estimator. We focus on estimation by local linear regression, which was shown to be rate optimal (Porter, 2003). Investigation … selection ; local linear regression ; regression discontinuity designs …