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We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. Our model yields a significant increase in macroeconomic prediction accuracy over a linear benchmark model.

We construct new indicators of demand and supply for the Canadian economy by using natural language processing techniques to analyze earnings calls of publicly listed firms. Our results indicate that the new indicators could help central banks identify inflationary pressures in real time.

The elimination of long-term contracts and early termination fees (ETFs) in the US wireless industry at the end of 2015 increased monthly service fees by 2 to 5 percent. Nevertheless, consumers are clearly better off without ETFs. While firms’ revenues from ETFs vanish, their profits from monthly fees increase. As a result, the overall effect on producer profits is less clear.

Using micro data from the U.S. Consumer Expenditure Survey and Current Population Survey, I document that government spending shocks affect individuals differently over the life cycle.

In this piece we show that a limit on the level of asset encumbrance and minimum capital requirements are effective tools for minimizing the incentive for banks to take excessive risk.

I provide evidence that portfolio equity inflows can have expansionary effects on GDP and inflation if not offset by monetary policy. I use a shift-share instrument to estimate equity inflows based on plausibly exogenous timing of inflows into mutual funds with heterogeneous country portfolios.

We present a model in which banks and other financial intermediaries face both occasionally binding borrowing constraints and costs of equity issuance. Near the steady state, these intermediaries can raise equity finance at no cost through retained earnings.

We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a disaggregated industrial production series for the US economy. We identify unexpected changes in durable goods uncertainty as drivers of downturns, while unexpected hikes in non-durable goods uncertainty are expansionary.

We look at the informational content of consensus pricing in opaque over-the-counter markets. We show that the availability of price data informs participants mainly about other participants’ valuations, rather than about the value of a financial security.

We analyze monetary policy responses to AI in a two-sector New Keynesian model, distinguishing augmentation and automation. Both reduce labor demand, requiring accommodation that creates inflation trade-offs. Automation worsens them. Aggregate inflation depends on AI’s form and breadth, making policy stabilization more complex and aggregate data potentially misleading.