We study the dynamic impact of Covid-19, economic mobility, and containment policy shocks. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through sign and zero restrictions. Incidence and mobility shocks raise cases and deaths significantly...

We develop a vector autoregressive framework for combining the information in an external instrument with the information in the second moments of the data to identify latent monetary shocks in the United States. We show that the framework improves the identification of the structural model and...

We develop a vector autoregressive framework that combines an external instrument and heteroskedasticity for the identification of monetary policy shocks. We show that exploiting both types of information sharpens structural inference, allows testing both the relevance and exogeneity condition...

Evidence on the effectiveness of FX interventions is either limited to short horizons or hampered by debatable identification. We address these limitations by identifying a structural vector autoregressive model for the daily frequency with an external instrument. Generally, we find, for freely...

We construct a news-based viral disease index and study the dynamic impact of epidemics on the world economy, using structural vector autoregressions. Epidemic shocks have persistently negative effects, both directly and indirectly, on affected countries and on world output. The shocks lead to a...

This paper studies the effects of financial speculation on commodity futures returns, using publicly available data from the US Commodity Futures Trading Commission, aggregated by trader groups. We exploit the heteroskedasticity in the weekly data to identify exogenous variation in speculators'...