Data and Code for On Binscatter

Principal Investigator(s): View help for Principal Investigator(s) Matias Cattaneo, Princeton University; Richard Crump, Federal Reserve Bank of New York; Max H Farrell, UC Santa Barbara; Yingjie Feng, Tsinghua University

Version: View help for Version V1

Name	File Type	Size	Last Modified
AER-CCFF-ReplicationCode			01/05/2024 12:08:PM

Project Citation:

Cattaneo, Matias , Crump, Richard, Farrell, Max H, and Feng, Yingjie. Data and Code for On Binscatter. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-04-05. https://doi.org/10.3886/E195103V1

Project Description

Summary: 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.

Scope of Project

Subject Terms: Nonparametrics; binscatter; uniform inference

JEL Classification:
C01 Econometrics
C14 Semiparametric and Nonparametric Methods: General

Data Type(s): observational data; program source code

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Published Versions

V1 [2024-04-05]

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