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Equilibrium bond-pricing models rely on inflation being bad news for future growth to generate upward-sloping nominal yield curves. We develop a model that can generate upward-sloping nominal and real yield curves by instead using ambiguity about inflation and growth.

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.

Raising liquidity when funding is stressed creates pressure on the financial market. Liquidating large quantities of assets depresses their prices and may amplify funding shocks. How do banks weathering a funding crisis contribute to contagion risk?

We estimate the effects of economic uncertainty on time use and discuss its macroeconomic implications. We develop a model to demonstrate that substitution between market and non-market work provides an additional insurance margin to households, weakening precautionary savings and labour supply and lowering aggregate demand, ultimately amplifying the contractionary effects of uncertainty.

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.

We analyze the transmission of monetary policy during different phases of a sectoral demand reallocation episode when there are frictions to increasing production in a sector. Monetary policy is more effective in reducing inflation when a larger proportion of sectors are expanding or expect to expand in the near future.

Statistical inference--both estimation and testing--for stochastic volatility (SV) models is known to be challenging and computationally demanding. We propose simple and efficient estimators for SV models with conditionally heavy-tailed error distributions, particularly the Student’s t and Generalized Exponential Distributions (GED). The estimators rely on a small set of moment conditions derived from ARMA-type representations of SV models, with an option to apply “winsorization” to improve stability and finite-sample performance. Except for the degrees of-freedom parameter, closed-form expressions are available for all other parameters, extending Ahsan and Dufour (2019, 2021), thus eliminating the need for numerical optimization or initial values. We derive the estimators’ asymptotic distribution and show that, due to their analytical tractability, they support reliable, and even exact, simulation-based inference via Monte Carlo or bootstrap methods. We assess their performance through extensive simulations and demonstrate their practical relevance in financial return data, which strongly reject the normality assumption in favor of heavy-tailed models.

The uncertainty around future changes to the Federal Reserve target rate varies over time. In our results, the main driver of uncertainty is a “path” factor signaling information about future policy actions, which is filtered from federal funds futures data.

Using the Bank of Canada's Currency Information Management Strategy, we analyze the network structure traced by a bank note’s travel in circulation and find that the denomination of the bank note is important in our potential understanding of the demand and use of cash.

We model the asset-opacity choice of an intermediary subject to rollover risk in wholesale funding markets. Greater opacity means investors form more dispersed beliefs about an intermediary’s profitability.