Staff research

When estimating earnings losses upon job separations, existing strategies focus on separations in mass layoffs to distinguish involuntary separations from voluntary separations. We revisit the measurement of the sources and consequences of involuntary job separations using Canadian job separation records.

Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data.

Several measures suggest economic outcomes have improved for Indigenous Peoples in recent decades. Yet, institutional settings and gaps in infrastructure and financing continue to hinder their economic progress. Recent efforts have helped address some data gaps, and new institutions are helping Indigenous communities to overcome historic barriers to growth.

This paper examines factors that affect the transmission of fluctuations in global agricultural commodity prices to domestic food inflation.

The fourth generation of Bank of Canada projection and policy analysis models seeks to improve our understanding of inflation dynamics, the supply side of the economy and the underlying risks faced by policy-makers coming from uncertainty about how the economy functions.

We examine how intermediary capitalization affects asset prices in a framework that allows for intermediary market power. We introduce a model in which capital-constrained intermediaries buy or trade an asset in an imperfectly competitive market, and we show that weaker capital constraints lead to both higher prices and intermediary markups.

This paper provides an overview of digitalization and its economic implications. We assess the scope of digitalization in Canada as well as the challenges related to its measurement.

This paper quantifies tail risks in the outlooks for Canadian inflation and real GDP growth by estimating their conditional distributions at a daily frequency. We show that the tail risk probabilities derived from the conditional distributions accurately reflect realized outcomes during the sample period from 2002 to 2022.

I show how to combine large numbers of forecasts using several approaches within the framework of a Bayesian predictive synthesis. I find techniques that choose and combine a handful of forecasts, known as global-local shrinkage priors, perform best.

We introduce generalized autoregressive gamma (GARG) processes, a class of autoregressive and moving-average processes in which each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. We show that using GARG processes reduces pricing errors by substantially more than using existing autoregressive gamma processes does.