C3 - Multiple or Simultaneous Equation Models; Multiple Variables
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Decision Synthesis in Monetary Policy
We use Bayesian predictive decision synthesis to formalize monetary policy decision-making. We develop a case-study of monetary policy decision-making of an inflation-targeting central bank using multiple models in a manner that considers decision goals, expectations and outcomes. -
Untapped Potential: Mobile Device Ownership and Mobile Payments in Canada
We present a two-stage model of mobile phone and mobile payment usage that controls for selectivity. This reveals unobserved factors that work against having a mobile phone and toward mobile paying. Therefore, people who are unable to acquire or choose not to own a mobile device might have unmet payment needs. -
Decomposing Systemic Risk: The Roles of Contagion and Common Exposures
We examine systemic risks within the Canadian banking sector, decomposing them into three contribution channels: contagion, common exposures, and idiosyncratic risk. Through a structural model, we dissect how interbank relationships and market conditions contribute to systemic risk, providing new insights for financial stability. -
Predictive Density Combination Using a Tree-Based Synthesis Function
This paper studies non-parametric combinations of density forecasts. We introduce a regression tree-based approach that allows combination weights to vary on the features of the densities, time-trends or economic indicators. In two empirical applications, we show the benefits of this approach in terms of improved forecast accuracy and interpretability. -
Finding the balance—measuring risks to inflation and to GDP growth
Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023. -
Making It Real: Bringing Research Models into Central Bank Projections
Macroeconomic projections and risk analyses play an important role in guiding monetary policy decisions. Models are integral to this process. This paper discusses how the Bank of Canada brings research models and lessons learned from those models into the central bank projection environment. -
Forecasting Risks to the Canadian Economic Outlook at a Daily Frequency
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. -
Global Demand and Supply Sentiment: Evidence from Earnings Calls
This paper quantifies global demand, supply and uncertainty shocks and compares two major global recessions: the 2008–09 Great Recession and the COVID-19 pandemic. We use two alternate approaches to decompose economic shocks: text mining techniques on earnings calls transcripts and a structural Bayesian vector autoregression model. -
What Can Earnings Calls Tell Us About the Output Gap and Inflation in Canada?
We construct new indicators of demand and supply for the Canadian economy by using natural language processing techniques to analyze earnings calls of publicly listed firms. Our results indicate that the new indicators could help central banks identify inflationary pressures in real time.