Staff working papers

This paper theoretically and empirically examines the price impacts of liquidations in DeFi and how different liquidation mechanisms affect the price impacts.

Exporters frequently change their market destinations. This paper introduces a new approach to identifying the drivers of these decisions over time. Analysis of customs data from China and the UK shows most changes are driven by demand rather than supply-related shocks.

We propose a novel approach to estimating consumer demand for differentiated products. We eliminate the need for instrumental variables by assuming demand shocks are sparse. Our empirical applications reveal strong evidence of sparsity in real-world datasets.

Can regulators keep pace with banks’ creative regulatory workarounds? Our analysis unpacks the trade-offs between fixed regulations and crisis-triggered rules, showing that the latter are especially prone to circumvention—and can trigger larger, costlier bailouts.

We construct a new dataset of unanticipated contracts and examine their effects on employment growth. We find positive, significant and persistent effects on firms with fewer than 150 employees and estimate a cost-per­job that is an order of magnitude lower than previous estimates.

Market power and pass-through of cost and demand shocks are studied in a market with free entry of heterogeneous firms and consumer mixed search. Equilibrium prices and markups are driven by variation in the elasticity of demand across firms. Improved conditions for buyers can either raise or lower market power.

I study the welfare consequences of regulations on high-cost consumer credit in the United States and find that borrowing limits have distributional impacts on households with self-control issues.

We analyze micro-level data from the Canadian Survey of Consumer Expectations through the lens of a heterogeneous-expectations model to study how inflation expectations form over the business cycle. We provide new insights into how households form expectations, documenting that forecasting behaviours, attention and noise in beliefs vary across socio-demographic groups and correlate with views about monetary policy.

This paper provides an extensive evaluation of the performance of quantile vector autoregression (QVAR) to forecast macroeconomic risk. Generally, QVAR outperforms standard benchmark models. Moreover, QVAR and QVAR augmented with factors perform equally well. Both are adequate for modeling macroeconomic risks.

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.