Evaluating Firm-Level Expected-Return Proxies: Implications for Estimating Treatment Effects
提出一个简洁框架，用于在估计处理效应时选择预期收益率代理变量，通过比较截面和时间序列上的测量误差方差，评估了多种代理变量的相对表现，并重新审视了四项先前研究，发现该框架可能改变其推断的符号或大小。
Abstract We introduce a parsimonious framework for choosing among alternative expected-return proxies (ERPs) when estimating treatment effects. By comparing ERPs’ measurement error variances in the cross-section and in the time series, we provide new evidence on the relative performance of firm-level ERPs nominated by recent studies. Generally, “implied-costs-of-capital” metrics perform best in the time series, whereas “characteristic-based” proxies perform best in the cross-section. Factor-based ERPs, even the latest renditions, perform poorly. We revisit four prior studies that use ex ante ERPs and illustrate how this framework can potentially alter either the sign or the magnitude of prior inferences.