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We provide empirical evidence of effects to the aggregate economy from surprises about financial intermediaries’ net worth based on a high-frequency identification strategy. We estimate that news of a 1% decline in intermediaries’ net worth leads to a 0.2%–0.4% decrease in the market value of nonfinancial firms.

Should a CBDC be more like cash or bank deposits? An interest-bearing, cash-like CBDC not only makes payments more efficient but also increases total demand. This has positive effects on other transactions, inducing more deposit taking and lending and, thus, bank intermediation.

Business cycles are substantially correlated across countries. Yet most existing models are not able to generate substantial transmission through international trade. We show that the nature of such transmission depends fundamentally on the features determining the responsiveness of labor supply and labor demand to international relative prices.

Although the number of job applications has risen, job-finding rates remain relatively unchanged while job-separation rates have significantly declined. Rather than raising the probability of finding a job, we find that a rise in applications raises the probability of finding a good match, as evidenced by the decline in separation rates.

DeFi lending with proper governance is operationally viable, but it also faces constraints related to capital efficiency, liquidation risk, and systemic fragility within the crypto ecosystem.

This paper studies exchange rate dynamics by incorporating bounded rationality, that is, limited foresight, in a small open-economy model. This behavior of limited foresight helps explain several observations and puzzles in the data of exchange rate movements.

I study the welfare consequences of regulations on high-cost consumer credit in the United States and find that borrowing limits have distributional impacts on households with self-control issues.

We consider introducing an expiry date for offline digital currency balances. Consumers whose digital cash expired would automatically receive the funds back into their online account. This functionality could increase demand for digital cash, with the time to expiry playing a key role.

We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions.

Understanding the size of sectoral links is crucial to predicting the impact of a crisis on the whole economy. We show that statistical learning techniques substantially outperform traditional estimation techniques when measuring large networks of these links.