Binscatter Regressions

TL;DR

The paper introduces Binsreg, a comprehensive software package implementing advanced binscatter methods for regression analysis, hypothesis testing, and data visualization, enhancing analysis of treatment effects and heterogeneity.

Contribution

It provides a unified implementation of binscatter techniques, including nonlinear models, inference procedures, and data-driven bin selection, advancing tools for empirical regression analysis.

Findings

Provides tools for binscatter plotting and estimation

Includes hypothesis testing and comparison features

Supports covariate adjustment and multi-sample analysis

Abstract

We introduce the package Binsreg, which implements the binscatter methods developed by Cattaneo, Crump, Farrell, and Feng (2024b,a). The package includes seven commands: binsreg, binslogit, binsprobit, binsqreg, binstest, binspwc, and binsregselect. The first four commands implement binscatter plotting, point estimation, and uncertainty quantification (confidence intervals and confidence bands) for least squares linear binscatter regression (binsreg) and for nonlinear binscatter regression (binslogit for Logit regression, binsprobit for Probit regression, and binsqreg for quantile regression). The next two commands focus on pointwise and uniform inference: binstest implements hypothesis testing procedures for parametric specifications and for nonparametric shape restrictions of the unknown regression function, while binspwc implements multi-group pairwise statistical comparisons. Finally, the command binsregselect implements data-driven number of bins selectors. The commands offer binned scatter plots, and allow for covariate adjustment, weighting, clustering, and multi-sample analysis, which is useful when studying treatment effect heterogeneity in randomized and observational studies, among many other features.