Outlier Robust Two-Stage Least Squares Inference and Testing

An implementation of easy tools for outlier robust inference in two-stage least squares (2SLS) models. The user specifies a reference distribution against which observations are classified as outliers or not. After removing the outliers, adjusted standard errors are automatically provided. Furthermore, several statistical tests for the false outlier detection rate can be calculated. The outlier removing algorithm can be iterated a fixed number of times or until the procedure converges. The algorithms and robust inference are described in more detail in Jiao (2019) < https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.

R Package “robust2sls”

The goal of robust2sls is to provide easy-to-use tools for outlier-robust inference and outlier testing in two-stage least squares (2SLS) models.

Installation

You can install the released version from CRAN with:

install.packages("robust2sls")

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("jkurle/robust2sls")

Introduction

For a detailed introduction to the model framework, the different trimmed 2SLS algorithms, and examples, see the vignette Introduction to the robust2sls Package.

utils::vignette("overview", package = "robust2sls")