American Economic Review · 2024 · Matias D. Cattaneo、Richard K. Crump、Max H. Farrell、Yingjie Feng
中文摘要
分箱散点图是一种用于可视化双变量关系和进行非正式设定检验的流行方法。我们正式研究了该方法的性质，并开发了增强的可视化和计量经济学分箱散点图工具。这些工具包括使用最优分箱估计条件均值以及量化不确定性。我们还着重指出了一个与协变量调整相关、可能导致错误结论的方法论问题。我们运用本文方法重新审视了两个应用，发现与使用先前非正式分箱散点图方法所得结果存在实质性差异。我们提供了适用于 Python、R 和 Stata 的通用软件。我们的技术工作对非参数基于分区的估计文献具有独立的意义。（JEL C13, C14, C18, C51, O31, R32）
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. (JEL C13, C14, C18, C51, O31, R32)