Nonlinear Binscatter Methods

Matias Cattaneo, Richard Crump, Max Farrell and Yingjie Feng

No 1110, Staff Reports from Federal Reserve Bank of New York

Abstract: Binscatters are a powerful tool for empirical work in the social, behavioral, and biomedical sciences. Available tools rely on least squares estimation of the conditional mean. We introduce novel binscatter methods based on nonlinear, possibly nonsmooth M-estimation, covering generalized linear, robust, and quantile regression models. We provide theoretical results and practical tools, including optimal bin selection, confidence bands, and statistical tests regarding functional form or shape restrictions. We demonstrate our methods by studying the relationship of income and (lack of) health insurance. We provide software for Python, R, and Stata. Our technical results may be of independent interest.

Keywords: partition-based semi-linear estimators; Linear models; quantile regression; robust bias correction; uniform inference; binning selection; treatment effect estimation (search for similar items in EconPapers)
JEL-codes: C14 C18 C21 (search for similar items in EconPapers)
Pages: 140
Date: 2024-08-01
New Economics Papers: this item is included in nep-ecm
Note: Revised August 2026.
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Citations: View citations in EconPapers (1)

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Working Paper: Nonlinear Binscatter Methods (2026)
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DOI: 10.59576/sr.1110

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