Staff Reports
On Binscatter
Number 881
February 2019 Revised November 2023

JEL classification: C14, C18, C21

Authors: Matias D. Cattaneo, Richard K. Crump, Max H. Farrell, and Yingjie Feng

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.

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Author Disclosure Statement(s)
Matias D. Cattaneo
I declare that I have no relevant or material financial interests that relate to the research described in my paper entitled “On Binscatter,” joint with Richard Crump, Max Farrell, and Yingjie Feng.

Richard K. Crump
I declare that I have no relevant or material financial interests that relate to the research described in my paper entitled “On Binscatter,” joint with Matias Cattaneo, Max Farrell, and Yingjie Feng.

Max H. Farrell
I declare that I have no relevant or material financial interests that relate to the research described in my paper entitled “On Binscatter,” joint with Richard Crump, Matias Cattaneo, and Yingjie Feng.

Yingjie Feng
I declare that I have no relevant or material financial interests that relate to the research described in my paper entitled “On Binscatter,” joint with Richard Crump, Matias Cattaneo, and Max Farrell.
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