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High-Frequency Cross-Sectional Identification of Military News Shocks
This study develops a two-step procedure to identify and quantify fiscal news shocks. First, we augment a narrative identification strategy using large language model searches to compile events (2001？2023) that altered the expected path of U.S. defense expenditure. Second, for each event, we estimate market-implied shifts in expected defense spending with cross-sectional regressions of contractors’ stock returns on their reliance on military revenues. We show that this approach statistically validates each event; quantifies each shock in an intuitive, model-consistent fashion; and readily generalizes to other macroeconomic contexts. Employing the estimated shocks in a shift-share analysis yields a two-year, metropolitan statistical area？level GDP multiplier of approximately 1 for U.S. military build-ups.
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