Does the information content of payout initiations and omissions influence firm risks?
首次派付与停止派付的信息含量是否影响企业风险?AI 解读 ↓
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首次派付与停止派付的信息含量是否影响企业风险?AI 解读 ↓
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We study the influence on firm risks of NASDAQ and NYSE firm payout initiations and omissions. These payout events can be interpreted as managerial signals of firm financial life-cycle maturation resulting in concomitant changes in firm risks. We remove confounding payout types and we match on the propensity to initiate or omit informed by determinants of payout known to investors in advance. For payout event and matched firms, we apply the difference-in-differences method to estimate the effect of the information content of actual initiations and omissions on firm risks. We find consistent significant declines in total, aggregate systematic, and idiosyncratic firm risks after cash dividend initiations and increases after dividend omissions, but only incidentally after share repurchase initiations and omissions.
We study difference-in-differences designs in which all units are initially untreated and later receive heterogeneous, strictly positive treatment doses. When quasi-untreated units receive doses close to zero, a difference-in-difference estimand using those units as the control group identifies a weighted average treatment effect per unit of treatment, under a parallel-trends assumption. We develop a nonparametric estimator and bias-corrected confidence intervals for this…
伪面板事件研究:可信应用的计量经济学基础
Event studies with panel data are widely used to estimate treatment effects. With repeated cross-sections, future treatment dates are unobserved for untreated individuals. Pseudo-panel event studies impute pre-treatment outcomes of the treated using matched untreated individuals. This paper establishes identification in such designs and characterizes bias when identification conditions fail. I show that the pseudo-panel regression is equivalent to weighted least squares with…
We develop the first empirically implementable selective-inference framework for difference-in-differences with controls selected by pre-treatment fit. We construct confidence intervals for the average treatment effect on the treated (ATT), incorporating that selection and estimating the unknown full covariance from the same observations. Counterexamples establish undercoverage from Roth's fixed-comparison pretest adjustment and direct Gaussian-score application of…
Researchers often report an estimate and confidence interval only if a specification test does not reject - for example, only if a pre-trend test supports a difference-in-differences design. Does this make the reported confidence interval less reliable? We show that when the identifying assumptions hold, the answer is no: conditional on passing the pre-test, the confidence interval may over-cover but cannot under-cover. When the assumptions fail, pre-testing can reduce…
以模型平均替代处理前趋势检验
We study difference-in-differences (DID) estimation of average treatment effects on the treated when the researcher is uncertain about which pre-treatment periods satisfy the parallel trends assumption. Roth (2022) shows that pre-trend testing can induce bias, complementing the statistical literature on post-selection inference. We propose the model averaged difference-in-differences (MADID) estimator, a weighted average of the candidate $2\times2$ DID estimators. The…
利用辅助事件信息进行删失结局的因果推断
Clinical trials with survival endpoints lose information when participants are censored before death is observed. We develop target-preserving estimators that use posttreatment disease history, such as recurrence or progression, to recover information lost to censoring for marginal survival and restricted mean survival effects. The difficulty is that the intermediate event is downstream of treatment: naive adjustment can change the causal estimand, and the useful information…