On Binscatter

• 成果类型: Article
• 署名作者: Cattaneo, Matias d.; Crump, Richard k.; Farrell, Max h.; Feng, Yingjie
• 署名单位: Princeton University; Federal Reserve System - USA; Federal Reserve Bank - New York; University of California System; University of California Santa Barbara; Tsinghua University
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20221576
• 发表日期: 2024
• 页码: 1488-1514
• 关键词: LOCAL ASYMPTOTICS

摘要

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. ( JEL C13, C14, C18, C51, O31, R32 )