This paper looks at the implications for monetary policy of the widespread adoption of artificial intelligence and machine learning, which is sometimes called the “fourth industrial revolution.”
I use a structural vector autoregression (SVAR) with sign restrictions to provide conditional evidence on the behavior of the US external finance premium (EFP). The results indicate that the excess bond premium, a proxy for the EFP, reacts countercyclically to supply and monetary policy shocks and procyclically to demand shocks.
This paper presents a structural framework of the global oil market that relies on information on global fuel consumption to identify flow demand for oil. We show that under mild identifying assumptions, data on global fuel consumption help to provide comparatively sharp insights on elasticities and other key structural parameters of the global oil market.
We document a substantial positive correlation of employment status between mothers and their children in the United States, linking data from the National Longitudinal Survey of Youth 1979 (NLSY79) and the NLSY79 Children and Young Adults. After controlling for ability, education and wealth, a one-year increase in a mother’s employment is associated with six weeks more employment of her child on average.
Most models in finance assume that agents make trading plans over the infinite future. We consider instead that they are boundedly rational and may only form forecasts over a limited horizon.
A consumer discloses information to a multi-product seller, which learns about the consumer’s preferences, sets prices, and makes product recommendations. While the consumer benefits from accurate product recommendations, the seller may use the information to price discriminate.
This note presents a structural vector autoregressive (SVAR) model of the global oil market. The model identifies four types of shocks with different economic interpretations: oil supply shocks, oil-market-specific demand shocks, storage demand shocks and shocks to global economic growth.
Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals.
This note provides an update on Bank of Canada staff’s assessment of the Canadian neutral rate. The neutral rate is the policy rate needed to keep output at its potential level and inflation at target once the effects of any cyclical shocks have dissipated. This medium- to long-run concept serves as a benchmark for gauging the degree of monetary stimulus provided by a given policy setting.