C - Mathematical and Quantitative Methods
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Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis
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. -
Digitalization: Implications for Monetary Policy
We explore the implications of digitalization for monetary policy, both in terms of how monetary policy affects the economy and in terms of data analysis and communication with the public. -
Unmet Payment Needs and a Central Bank Digital Currency
We discuss the payment habits of Canadians both in the current payment environment and in a hypothetical cashless environment. -
Generalized Autoregressive Gamma Processes
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. -
Is Money Essential? An Experimental Approach
Monetary theory says that money is essential if it helps to achieve better incentive-feasible outcomes. We test this in the laboratory. -
Cryptoasset Ownership and Use in Canada: An Update for 2022
We find that Bitcoin ownership declined from 13% in 2021 to 10% in 2022. This drop occurred against a background of steep price declines and an increasingly tight regulatory atmosphere. -
Is Climate Transition Risk Priced into Corporate Credit Risk? Evidence from Credit Default Swaps
We study whether the credit derivatives of firms reflect the risk from climate transition. We find that climate transition risk has asymmetric and significant economic impacts on the credit risk of more vulnerable firms, and negligible effects on other firms. -
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.