Binscatter regressions

• D. Cattaneo, Matias ^[1] ; K. Crump, Richard ^[3] ; Feng, Yingije ^[2]
  1. [1] Princeton University

     Princeton University

     Estados Unidos

     
  2. [2] Tsinghua University

     Tsinghua University

     China

     
  3. [3] Federal Reserve Bank of New York New York, NY,

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• Localización: The Stata journal, ISSN 1536-867X, Vol. 25, Nº. 1, 2025, págs. 3-50
• Idioma: inglés
• DOI: 10.1177/1536867X251322960
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• Resumen

  • 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 comprises 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 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 analysis, which is useful when studying treatment-effect heterogeneity in randomizec and observational studies, among many other features.