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We study the competition between a welfare-maximizing public payment platform (e.g., CBDC or fast payment system) and a profit-maximizing private platform in a two-sided market, deriving optimal public pricing and showing how network effects, fragmentation, and policy mandates like zero fees or cost recovery shape welfare, usage, and fee incidence.

We study how different types of monetary policy shape the distributional effects of external economic shocks on households’ consumption in a small open economy. Our results present a trade-off between maintaining overall stabilization and controlling consumption inequality.

Monetary theory says that money is essential if it helps to achieve better incentive-feasible outcomes. We test this in the laboratory.

Would a shift in trading in fixed-income markets—from over the counter (bilateral trading) to a centralized electronic platform—improve welfare? We use trade-level data on the secondary market for Government of Canada debt to answer this question.

The Canadian Financial Monitor (CFM) survey uses non-probability sampling for data collection, so selection bias is likely. We outline methods for obtaining survey weights and discuss the conditions necessary for these weights to eliminate selection bias. We obtain calibration weights for the 2018 and 2019 online CFM samples.

How are regional cost shocks passed through into global prices? We investigate the role of short-run capacity constraints and show that they can induce stark non-linearities in the pass-through. We highlight this effect for the market for ammonia, a commodity produced largely from natural gas.

We examine loan insurance—credit risk transfer upon origination—in a model in which lenders can screen, learn loan quality over time, and can sell loans. Some lenders with low screening ability insure, benefiting from higher market liquidity of insured loans while forgoing the option to exploit future information about loan quality.

We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing.

We characterize the bias in cross-sectional Hill estimates caused by common underlying factors and propose two simple-to-implement remedies. To test for the presence, direction and size of the bias, we use monthly US stock returns and annual US Census county population data.