Alessandro Casini, Adam McCloskey
arXiv 21 Jun 2024 · Econometrics
arXiv:2406.15667 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Nakamura and Steinsson (2018) High Frequency Identification of Monetary Non-Neutrality: The Information Effect | 1.000 | 27 | 6 | 100% |
| 2 | Bauer and Swanson (2023) An Alternative Explanation for the "Fed Information Effect" | 1.000 | 17 | 3 | 100% |
| 3 | Lucca and Moench (2015) The Pre-FOMC Announcement Drift | 0.874 | 8 | 2 | 100% |
| 4 | Cieslak, Morse, and Vissing-Jorgensen (2019) Stock Returns over the FOMC Cycle | 0.874 | 5 | 2 | 100% |
| 5 | Cieslak and Schrimpf (2019) Non-Monetary News in Central Bank Communication | 0.737 | 3 | 2 | 100% |
| 6 | Bauer and Swanson (2023) A Reassessment of Monetary Policy Surprises and High-Frequency Identification | 0.693 | 6 | 1 | 100% |
| 7 | Bernile, Hu, and Tang (2016) Can Information be Locked Up? Informed Trading Ahead of Macro-News Announcements | 0.644 | 4 | 1 | 100% |
| 8 | Gürkaynak, Sack, and Swanson (2005) Do Actions Speak Louder Than Words? The Response of Asset Prices to Monetary Policy Actions and Statements | 0.644 | 4 | 1 | 100% |
| 9 | Hu, Pan, Wang, and Zhu (2022) Premium for Heightened Uncertainty: Explaining Pre-Announcement Market Returns | 0.644 | 4 | 1 | 100% |
| 10 | Trapani (2016) Testing for (In)finite Moments | 0.644 | 2 | 2 | 100% |
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