Heterogeneous effects are prevalent in many economic settings. As the functional form between outcomes and regressors is generally unknown a priori, a semiparametric negative binomial count data model is proposed which is based on the local likelihood approach and generalized product kernels....

This paper deals with LASSO regression in high-dimensional sparse linear models with time series data. We propose heteroskedasticity and autocorrelation consistent (HAC) and heteroskedasticity and autocorrelation robust (HAR) estimates for the penalty loadings and evaluate the in-sample fitting...

In this article the package High-dimensional Metrics (hdm) is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for...