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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.

We study how the interaction of market power and nominal price rigidity influences inflation dynamics. We find that pass-through declines with price stickiness when markets are concentrated, which implies a lower slope of the New Keynesian Phillips curve.

The prevalence of entrepreneurs, particularly low-productivity non-employers, declines as economies develop. This decline is more pronounced for women. Relative to men, women are more likely to be entrepreneurs in poor economies but less likely in rich economies.

We break down the exchange rate based on an explicit link between fixed income and currency markets. We isolate a foreign exchange risk premium and show it is the main driver of the exchange rate between the Canadian and US dollars, especially on monetary policy and macroeconomic news announcement days.

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.

For central banks, conducting policy in an environment of uncertainty is a daily fact of life. This uncertainty can take many forms, ranging from incomplete knowledge of the correct economic model and data to future economic and geopolitical events whose precise magnitudes and effects cannot be known with certainty.

We investigate the macroeconomic impacts of mothballed businesses—those that closed temporarily—on sectoral equilibrium prices after a negative demand shock. Our results suggest that pandemic fiscal support for temporary closures may have eased inflationary pressures.

How do households respond when they receive unanticipated income, such as an inheritance or government stimulus cheque? This paper studies these windfall income shocks through a model of household behaviour that generates a realistic consumption response for households along the entire distribution of wealth.

Models for macroeconomic forecasts do not usually take into account the risk of a crisis—that is, a sudden large decline in gross domestic product (GDP). However, policy-makers worry about such GDP tail risk because of its large social and economic costs.

The paper explores the link between financial distress and the commodity price hedging behaviour of Canadian oil firms.