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Binscatter Regressions

Matias Cattaneo (), Richard Crump (), Max H. Farrell and Yingjie Feng

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Abstract: We introduce the \texttt{Stata} package \textsf{Binsreg}, which implements the binscatter methods developed in \citet*{Cattaneo-Crump-Farrell-Feng_2021_Binscatter}. The package includes seven commands: \texttt{binsreg}, \texttt{binslogit}, \texttt{binsprobit}, \texttt{binsqreg}, \texttt{binstest}, \texttt{binspwc}, and \texttt{binsregselect}. The first four commands implement point estimation and uncertainly quantification (confidence intervals and confidence bands) for canonical and extended least squares binscatter regression (\texttt{binsreg}) as well as generalized nonlinear binscatter regression (\texttt{binslogit} for Logit regression, \texttt{binsprobit} for Probit regression, \texttt{binsqreg} for quantile regression). These commands also offer binned scatter plots, allowing for one- and multi-sample settings. The next two commands focus on pointwise and uniform inference: \texttt{binstest} implements hypothesis testing procedures for parametric specification and for nonparametric shape restrictions of the unknown regression function, while \texttt{binspwc} implements multi-group pairwise statistical comparisons. These two commands cover both least squares as well as generalized nonlinear binscatter methods. All our methods allow for multi-sample analysis, which is useful when studying treatment effect heterogeneity in randomized and observational studies. Finally, the command \texttt{binsregselect} implements data-driven number of bins selectors for binscatter methods using either quantile-spaced or evenly-spaced binning/partitioning. All the commands allow for covariate adjustment, smoothness restrictions, weighting and clustering, among many other features. Companion \texttt{Python} and \texttt{R} packages with similar syntax and capabilities are also available.

Date: 2019-02, Revised 2021-08
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