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110 Results

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

Making It Real: Bringing Research Models into Central Bank Projections

Staff Discussion Paper 2023-29 Marc-André Gosselin, Sharon Kozicki
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.
Content Type(s): Staff research, Staff discussion papers Topic(s): Economic models, Monetary policy JEL Code(s): C, C3, C32, C5, C51, E, E3, E37, E4, E47, E5, E52

Forecasting Risks to the Canadian Economic Outlook at a Daily Frequency

Staff Discussion Paper 2023-19 Chinara Azizova, Bruno Feunou, James Kyeong
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

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.

Supply Drivers of US Inflation Since the COVID-19 Pandemic

Staff Working Paper 2023-19 Serdar Kabaca, Kerem Tuzcuoglu
This paper examines the contribution of several supply factors to US headline inflation since the start of the COVID-19 pandemic. We identify six supply shocks using a structural VAR model: labor supply, labor productivity, global supply chain, oil price, price mark-up and wage mark-up shocks.

Comparison of Bayesian and Sample Theory Parametric and Semiparametric Binary Response Models

We use graphic processing unit computing to compare Bayesian and sample theory semiparametric binary response models. Our findings show that optimal bandwidth does not outperform regular bandwidth in binary semiparametric models.

Quantifying the Economic Benefits of Payments Modernization: the Case of the Large-Value Payment System

Staff Working Paper 2021-64 Neville Arjani, Fuchun Li, Zhentong Lu
Canada is undertaking a major initiative to modernize its payments ecosystem. The modernized ecosystem is expected to bring significant benefits to Canadian financial markets and the overall economy. We develop an empirical framework to quantify the economic benefits of modernizing the payment system in Canada.
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