C - Mathematical and Quantitative Methods

• 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 Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C5, C52, C53, E, E3, E37

• Digitalization: Implications for Monetary Policy

  Staff Discussion Paper 2023-18 Vivian Chu, Tatjana Dahlhaus, Christopher Hajzler, Pierre-Yves Yanni

  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.

  Content Type(s): Staff research, Staff discussion papers Topic(s): Digitalization, Inflation and prices, Market structure and pricing, Monetary policy, Monetary policy communications, Monetary policy transmission JEL Code(s): C, C4, C8, E, E3, E31, E32, E5, E52

• 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 Topic(s): Bank notes, Central bank research, Digital currencies and fintech, Financial services JEL Code(s): C, C1, C12, C9, E, E4, O, O5, O54

• Generalized Autoregressive Gamma Processes

  Staff Working Paper 2023-40 Bruno Feunou

  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.

  Content Type(s): Staff research, Staff working papers Topic(s): Asset pricing, Econometric and statistical methods JEL Code(s): C, C5, C58, G, G1, G12