On binscatter

Matias Cattaneo, Richard Crump, Max Farrell and Yingjie Feng

No 881, Staff Reports from Federal Reserve Bank of New York

Abstract: 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.

Keywords: binned scatter plot; regressogram; piecewise polynomials; partitioning estimators; nonparametric regressions; robust bias correction; uniform inference; binning selection (search for similar items in EconPapers)
JEL-codes: C14 C18 C21 (search for similar items in EconPapers)
Pages: 96 pages
Date: 2019-02-01
New Economics Papers: this item is included in nep-big
Note: Revised November 2023.
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Related works:
Journal Article: On Binscatter (2024)
Working Paper: On Binscatter (2024)
Working Paper: On binscatter (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fednsr:881

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