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On binscatter


  • Cattaneo, Matias D.

    (University of Michigan)

  • Crump, Richard K.

    (Federal Reserve Bank of New York)

  • Farrell, Max H.

    (University of Chicago)

  • Feng , Yingjie

    (University of Michigan)


Binscatter is very popular in applied microeconomics. It provides a flexible, yet parsimonious way of visualizing and summarizing “big data” in regression settings, and it is often used for informal testing of substantive hypotheses such as linearity or monotonicity of the regression function. This paper presents a foundational, thorough analysis of binscatter: We give an array of theoretical and practical results that aid both in understanding current practices (that is, their validity or lack thereof) and in offering theory-based guidance for future applications. Our main results include principled number of bins selection, confidence intervals and bands, hypothesis tests for parametric and shape restrictions of the regression function, and several other new methods, applicable to canonical binscatter as well as higher-order polynomial, covariate-adjusted, and smoothness-restricted extensions thereof. In particular, we highlight important methodological problems related to covariate adjustment methods used in current practice. We also discuss extensions to clustered data. Our results are illustrated with simulated and real data throughout. Companion general-purpose software packages for Stata and R are provided. Finally, from a technical perspective, new theoretical results for partitioning-based series estimation are obtained that may be of independent interest.

Suggested Citation

  • Cattaneo, Matias D. & Crump, Richard K. & Farrell, Max H. & Feng , Yingjie, 2019. "On binscatter," Staff Reports 881, Federal Reserve Bank of New York.
  • Handle: RePEc:fip:fednsr:881

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    • Matias D. Cattaneo & Richard K. Crump & Max H. Farrell & Yingjie Feng, 2019. "On Binscatter," Papers 1902.09608,

    References listed on IDEAS

    1. Raj Chetty & John N. Friedman & Tore Olsen & Luigi Pistaferri, 2011. "Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: Evidence from Danish Tax Records," The Quarterly Journal of Economics, Oxford University Press, vol. 126(2), pages 749-804.
    2. Michael Stepner, 2014. "Binned Scatterplots: introducing -binscatter- and exploring its applications," 2014 Stata Conference 4, Stata Users Group.
    3. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Kato, Kengo, 2015. "Some new asymptotic theory for least squares series: Pointwise and uniform results," Journal of Econometrics, Elsevier, vol. 186(2), pages 345-366.
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    More about this item


    binned scatter plot; regressogram; piecewise polynomials; splines; partitioning estimators; nonparametric regression; robust bias correction; uniform inference; binning selection;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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