On Binscatter
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2019
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| _version_ | 1866910429375102976 |
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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 | Binscatter is a popular method for visualizing bivariate relationships and conducting informal specification testing. We study the properties of this method formally and develop enhanced visualization and econometric binscatter tools. These include estimating conditional means with optimal binning and quantifying uncertainty. We also highlight a methodological problem related to covariate adjustment that can yield incorrect conclusions. We revisit two applications using our methodology and find substantially different results relative to those obtained using prior informal binscatter methods. General purpose software in Python, R, and Stata is provided. Our technical work is of independent interest for the nonparametric partition-based estimation literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1902_09608 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | On Binscatter Cattaneo, Matias D. Crump, Richard K. Farrell, Max H. Feng, Yingjie Econometrics Methodology Machine Learning Binscatter is a popular method for visualizing bivariate relationships and conducting informal specification testing. We study the properties of this method formally and develop enhanced visualization and econometric binscatter tools. These include estimating conditional means with optimal binning and quantifying uncertainty. We also highlight a methodological problem related to covariate adjustment that can yield incorrect conclusions. We revisit two applications using our methodology and find substantially different results relative to those obtained using prior informal binscatter methods. General purpose software in Python, R, and Stata is provided. Our technical work is of independent interest for the nonparametric partition-based estimation literature. |
| title | On Binscatter |
| topic | Econometrics Methodology Machine Learning |
| url | https://arxiv.org/abs/1902.09608 |