Aggregation Bias in Estimates of Conditional Conservatism: Theory and Evidence
研究了聚合效应对基于Basu模型的条件保守主义估计的影响，发现其导致高估坏消息及时性并低估好消息及时性，使用更好的代理变量后，条件保守主义的影响趋于零。
Abstract This paper documents a study about the influence of the aggregation effect on the estimates of models based on the original Basu model – specifically the Ball, Kothari and Nikolaev model (Ball et al., 2013b). We provide an analytical study of the effect, showing that it can produce two biases: an omitted‐variable bias and a truncated‐sample bias. Using separate proxies for good and bad news for each company and year, we estimate the empirical sign and magnitude of those biases. Our results show that the estimates of conditional conservatism based on regressions of (unexpected) earnings on (unexpected) returns, as in the paper by Ball et al., are contaminated by substantial aggregation bias. More specifically, the aggregation effect causes these models to underestimate good‐news timeliness and overestimate bad‐news timeliness, thereby overestimating differential timeliness. Moreover, when we use proxies that provide better control for the aggregation effect, the differential timeliness coefficient tends to 0, showing that the influence of conditional conservatism on the returns–earnings relationship is, at best, marginal.