  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.
