Binscatter regressions

Matias Cattaneo, Richard Crump, Max Farrell and Yingjie Feng ()
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Yingjie Feng: Tsinghua University

Stata Journal, 2025, vol. 25, issue 1, 3-50

Abstract: In this article, we introduce the package binsreg, which implements the binscatter methods developed by Cattaneo et al. (2024a, arXiv:2407.15276 [stat.EM]; 2024b, American Economic Review 114: 1488–1514). The package com- prises seven commands: binsreg, binslogit, binsprobit, binsqreg, binstest, binspwc, and binsregselect. The first four commands implement binscatter plot- ting, 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 test- ing procedures for parametric specifications and for nonparametric shape restric- tions of the unknown regression function, while binspwc implements multigroup pairwise statistical comparisons. The last command, binsregselect, implements data-driven number-of-bins selectors. The commands offer binned scatterplots and allow for covariate adjustment, weighting, clustering, and multisample anal- ysis, which is useful when studying treatment-effect heterogeneity in randomized and observational studies, among many other features.

Keywords: binsreg; binslogit; binsprobit; binsqreg; binstest; binspwc; bin- sregselect; binscatter; binned scatterplot; nonparametrics; semiparametrics; parti- tioning estimators; B-splines; tuning parameter selection; confidence bands; shape and specification testing (search for similar items in EconPapers)
Date: 2025
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http://hdl.handle.net/10.1177/1536867X241233672

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Working Paper: Binscatter Regressions (2024)
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DOI: 10.1177/1536867X251322960

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