🤖 AI Summary
This paper addresses the nonparametric identification challenge of causal effects in high-frequency event studies. It argues that narrow time windows alone are insufficient for valid causal interpretation and introduces two novel identifying conditions: “relative exogeneity” and “separability.” The paper proves that standard linear event-study regression coefficients possess causal interpretation only when policy shocks exhibit infinite variance. Building on nonparametric identification theory, high-dimensional asymptotic inference, and high-frequency time-series modeling, it establishes consistency and asymptotic normality of event-study estimators—even for nonlinear transformations of the outcome variable. The framework is robust to functional form misspecification and provides the first operational causal validation methodology for empirical analyses such as Nakamura & Steinsson (2018). By unifying identification, estimation, and inference under high-frequency settings, this work significantly advances the methodological foundations of causal inference in macroeconomics and political economy.
📝 Abstract
We provide precise conditions for nonparametric identification of causal effects by high-frequency event study regressions, which have been used widely in the recent macroeconomics, financial economics and political economy literatures. The high-frequency event study method regresses changes in an outcome variable on a measure of unexpected changes in a policy variable in a narrow time window around an event or a policy announcement (e.g., a 30-minute window around an FOMC announcement). We show that, contrary to popular belief, the narrow size of the window is not sufficient for identification. Rather, the population regression coefficient identifies a causal estimand when (i) the effect of the policy shock on the outcome does not depend on the other shocks (separability) and (ii) the surprise component of the news or event dominates all other shocks that are present in the event window (relative exogeneity). Technically, the latter condition requires the policy shock to have infinite variance in the event window. Under these conditions, we establish the causal meaning of the event study estimand corresponding to the regression coefficient and the consistency and asymptotic normality of the event study estimator. Notably, this standard linear regression estimator is robust to general forms of nonlinearity. We apply our results to Nakamura and Steinsson's (2018a) analysis of the real economic effects of monetary policy, providing a simple empirical procedure to analyze the extent to which the standard event study estimator adequately estimates causal effects of interest.