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127 Results

Parallel Tempering for DSGE Estimation

Staff Working Paper 2024-13 Joshua Brault
I develop a population-based Markov chain Monte Carlo algorithm known as parallel tempering to estimate dynamic stochastic general equilibrium models. Parallel tempering approximates the posterior distribution of interest using a family of Markov chains with tempered posteriors.

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.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C3, C32, C5, C53

Three things we learned about the Lynx payment system

Staff Analytical Note 2023-14 Nikil Chande, Zhentong Lu, Hiru Rodrigo, Phoebe Tian
Canada transitioned to a new wholesale payment system, Lynx, in August 2021. Lynx is based on a real-time settlement model that eliminates credit risk in the system. This model can require more liquidity; however, Lynx’s design allows Canada’s wholesale payments to settle efficiently.

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

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.

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.

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.

From LVTS to Lynx: Quantitative Assessment of Payment System Transition

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.

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.

Private Digital Cryptoassets as Investment? Bitcoin Ownership and Use in Canada, 2016-2021

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. 
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