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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.

We measure systemic risk in the network of financial market infrastructures (FMIs) as the probability that two or more FMIs have a large credit risk exposure to the same FMI participant.

We study how the tightening of US immigration policy affects the Canadian economy and American workers. After the reduction in H-1B visa admissions in 2017, more immigrants came to Canada, and Canadian firms expanded their employment, sales and exports. The close trade link between the United States and Canada dampens the benefit American workers derive from this policy change.

This paper studies how the outcome of Bayesian persuasion depends on a sender’s information. I study a game in which, prior to the sender’s information disclosure, the designer can restrict the most informative signal that the sender can generate.

We show that issuing a deposit-like central bank digital currency (CBDC) with a proper interest rate would encourage banks to pay higher interest to keep their customers. Banks would then attract more deposits and offer more loans. Hence, a CBDC would not necessarily crowd out private banking.

We explore the idea that the financial sector can be a source of non-fundamental risk to the rest of the economy. We also consider whether policy can be used to reduce this risk—either by increasing the supply of publicly backed safe assets or by reducing the demand for safe assets.

As the economy emerges from a crisis, macroeconomic policy confronts a dilemma: a protracted stimulus can foster a more inclusive labor market recovery, yet risks igniting inflation that ultimately undermines workers’ welfare through real income erosion. This tension amplifies in the presence of the ZLB and aggregate capacity constraints. We embed this insight into a quantitative model of the US economy.

We document a new empirical finding in the foreign exchange market: currency returns show systematic reversals around the benchmark fixings. Specifically, the US dollar, on average, appreciates in the hours before fixes and depreciates after fixes.

I develop a method for Bayesian estimation of globally solved, non-linear macroeconomic models. The method uses a mixture density network to approximate the initial state distribution. The mixture density network results in more reliable posterior inference compared with the case when the initial states are set to their steady-state values.

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.