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.

翻译：Binscatter是一种用于可视化双变量关系并进行非正式设定检验的流行方法。本文对该方法的性质进行了形式化研究，并开发了增强型可视化与计量经济学binscatter工具，包括通过最优分箱估计条件均值及量化不确定性。我们同时揭示了一个与协变量调整相关的方法论问题——该问题可能导致错误结论。使用本文方法重新审视两项应用研究后，我们发现相较于采用先前非正式binscatter方法所得结果存在显著差异。我们还提供了Python、R及Stata通用软件实现。本文的技术工作对于基于非参数分区估计的文献具有独立参考价值。