Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis Staff working paper 2023-45 Tony Chernis 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. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C11, C5, C52, C53, E, E3, E37 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting
Unmet Payment Needs and a Central Bank Digital Currency Staff discussion paper 2023-15 Christopher Henry, Walter Engert, Alexandra Sutton-Lalani, Sebastian Hernandez, Darcey McVanel, Kim Huynh We discuss the payment habits of Canadians both in the current payment environment and in a hypothetical cashless environment. Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C1, C12, C9, E, E4, O, O5, O54 Research Theme(s): Money and payments, Cash and bank notes, Digital assets and fintech, Retail payments
Global Demand and Supply Sentiment: Evidence from Earnings Calls Staff working paper 2023-37 Temel Taskin, Franz Ulrich Ruch 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. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C11, C3, C32, E, E3, E32, G, G1, G10 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Inflation dynamics and pressures, Real economy and forecasting
What Can Earnings Calls Tell Us About the Output Gap and Inflation in Canada? Staff discussion paper 2023-13 Marc-André Gosselin, Temel Taskin 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. Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C1, C3, E, E3, E5 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Inflation dynamics and pressures, Real economy and forecasting
From LVTS to Lynx: Quantitative Assessment of Payment System Transition Staff working paper 2023-24 Ajit Desai, Zhentong Lu, Hiru Rodrigo, Jacob Sharples, Phoebe Tian, Nellie Zhang We quantitatively assess the changes in participants’ payment behaviour from modernizing Canada's high-value payments system to Lynx. Our analysis suggests that Lynx's liquidity-saving mechanism encourages liquidity pooling and early payments submission, resulting in improved efficiency for participants but with slightly increased payment delays. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C10, E, E4, E42, G, G2, G28 Research Theme(s): Money and payments, Payment and financial market infrastructures
Core inflation over the COVID-19 pandemic Staff analytical note 2022-17 Mikael Khan, Elyse Sullivan We assess the usefulness of various measures of core inflation over the COVID-19 pandemic. We find that Cpi-trim and CPI-median provided the best signal of underlying inflation. The favourable performance of these measures stems from their lack of reliance on historical experience, an especially valuable feature in unprecedented times. Content Type(s): Staff research, Staff analytical notes JEL Code(s): C, C1, C18, E, E3, E31 Research Theme(s): Monetary policy, Inflation dynamics and pressures, Monetary policy framework and transmission
Private Digital Cryptoassets as Investment? Bitcoin Ownership and Use in Canada, 2016-2021 Staff working paper 2022-44 Daniela Balutel, Walter Engert, Christopher Henry, Kim Huynh, Marcel Voia We report on the dynamics of Bitcoin awareness and ownership from 2016 to 2021, using the Bank of Canada's Bitcoin Omnibus Surveys (BTCOS). Our analysis also helps understand Bitcoin owners who adopted during the COVID-19 and how they differ from long-term owners. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C12, E, E4, O, O5, O51 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Digital assets and fintech
October 12, 2022 Five things we learned about Canadian Bitcoin owners in 2021 Daniela Balutel, Walter Engert, Christopher Henry, Kim Huynh, Marcel Voia We present key findings from the 2021 Bitcoin Omnibus Survey on Canadians’ awareness and ownership of Bitcoin. Most Canadians have heard of Bitcoin, which remains primarily used as an investment. Ownership jumped in 2021, reflecting increased savings during the pandemic and greater availability of user-friendly platforms to buy Bitcoin. Content Type(s): Publications, Financial System Hub articles JEL Code(s): C, C1, C12, E, E4, O, O5, O51
Examining recent revisions to CPI-common Staff analytical note 2022-15 Elyse Sullivan Unusually large revisions to CPI-common in recent months stem from increased common movements across consumer price index components amid broad inflationary pressures. With recent revisions, CPI-common is more closely aligned with the Bank of Canada’s other two preferred measures of core inflation. However, caution is necessary when interpreting real-time estimates of CPI-common in the current environment. Content Type(s): Staff research, Staff analytical notes JEL Code(s): C, C1, C13, C18, E, E3, E31 Research Theme(s): Models and tools, Economic models, Monetary policy, Inflation dynamics and pressures
Behavioral Learning Equilibria in New Keynesian Models Staff working paper 2022-42 Cars Hommes, Kostas Mavromatis, Tolga Özden, Mei Zhu We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C11, D, D8, D83, D84, E, E3, E6, E62 Research Theme(s): Models and tools, Economic models, Monetary policy, Inflation dynamics and pressures, Monetary policy framework and transmission