Identification and Estimation of Causal Effects in High-Frequency Event Studies

TL;DR

This paper establishes precise conditions under which high-frequency event study regressions can identify causal effects, clarifying when the standard linear regression estimator provides valid causal estimates in macroeconomic and financial contexts.

Contribution

It provides the first clear set of conditions for nonparametric identification of causal effects in high-frequency event studies, including the role of shock dominance and separability.

Findings

The population regression coefficient can be interpreted causally under specified conditions.

The standard linear regression estimator is robust to certain nonlinearities.

Application to monetary policy analysis demonstrates practical relevance.

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 variables (separability) and (ii) the surprise component of the news or event dominates all other variables that are present in the event window (relative exogeneity). Technically, the latter condition requires the ratio between the variance of the policy shock and that of the other variables to be infinite 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.