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| Main Authors: | , , , |
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| Format: | Preprint |
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2019
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1902.09615 |
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| _version_ | 1866916334333329408 |
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| author | Cattaneo, Matias D. Crump, Richard K. Farrell, Max H. Feng, Yingjie |
| author_facet | Cattaneo, Matias D. Crump, Richard K. Farrell, Max H. Feng, Yingjie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1902_09615 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Binscatter Regressions Cattaneo, Matias D. Crump, Richard K. Farrell, Max H. Feng, Yingjie Econometrics Computation 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. |
| title | Binscatter Regressions |
| topic | Econometrics Computation |
| url | https://arxiv.org/abs/1902.09615 |