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Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.

Standard theories of price adjustment are based on the problem of a single-product firm, and therefore they may not be well suited to analyze price dynamics in the economy with multiproduct firms.

We propose a novel theory of financial contagion. We study global coordination games of regime change in two regions with an initially uncertain correlation of regional fundamentals.

Models for macroeconomic forecasts do not usually take into account the risk of a crisis—that is, a sudden large decline in gross domestic product (GDP). However, policy-makers worry about such GDP tail risk because of its large social and economic costs.

We study consumer cash inventory behavior by developing a dynamic model of forward-looking consumers and estimating structural parameters of the model using detailed consumer survey data. Consumers facing holding and withdrawal costs solve a discrete-time continuous-control dynamic programming problem to optimally use cash at the point of sale.

We provide a decomposition of nominal yields into real yields, expectations of future inflation and inflation risk premiums when real bonds or inflation swaps are unavailable or unreliable due to their relative illiquidity.

We analyze the impact of interest rate policy on financial stability in an environment where banks can experience runs on their short-term liabilities, forcing them to sell assets at fire-sale prices.

We study how price variation within a store changes with inflation, and whether households exploit these changes to reduce the burden of inflation. We find that price changes from discounts mitigated the inflation burden while cheapflation exacerbated it.

Implications for signal extraction from specifying unobserved components (UC) models with correlated or orthogonal innovations have been well investigated. In contrast, the forecasting implications of specifying UC models with different state correlation structures are less well understood.

Using U.S. county-level data from 1990 to 2010, we study the causal impact of immigration on the provision of local public goods. We uncover substantial heterogeneity across immigrants with different skills and immigrants of different generations, which leads to unequal fiscal effects across U.S. counties.