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186 Results

A Blueprint for the Fourth Generation of Bank of Canada Projection and Policy Analysis Models

Staff Discussion Paper 2023-23 Donald Coletti
The fourth generation of Bank of Canada projection and policy analysis models seeks to improve our understanding of inflation dynamics, the supply side of the economy and the underlying risks faced by policy-makers coming from uncertainty about how the economy functions.

Predicting Changes in Canadian Housing Markets with Machine Learning

Staff Discussion Paper 2023-21 Johan Brannlund, Helen Lao, Maureen MacIsaac, Jing Yang
We apply two machine learning algorithms to forecast monthly growth of house prices and existing homes sales in Canada. Although the algorithms can sometimes outperform a linear model, the improvement in forecast accuracy is not always statistically significant.

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.

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

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.

Turning Words into Numbers: Measuring News Media Coverage of Shortages

Staff Discussion Paper 2023-8 Lin Chen, Stephanie Houle
We develop high-frequency, news-based indicators using natural language processing methods to analyze news media texts. Our indicators track both supply (raw, intermediate and final goods) and labour shortages over time. They also provide weekly time-varying topic narratives about various types of shortages.

Climate Variability and International Trade

Staff Working Paper 2023-8 Geoffrey R. Dunbar, Walter Steingress, Ben Tomlin
This paper quantifies the impact of hurricanes on seaborne international trade to the United States. Matching the timing of hurricane–trade route intersections with monthly U.S. port-level trade data, we isolate the unanticipated effects of a hurricane hitting a trade route using two separate identification schemes: an event study and a local projection.
Content Type(s): Staff research, Staff working papers Topic(s): Climate change, International topics JEL Code(s): C, C2, C22, C5, F, F1, F14, F18, Q, Q5, Q54

Risk Amplification Macro Model (RAMM)

Technical Report No. 123 Kerem Tuzcuoglu
The Risk Amplification Macro Model (RAMM) is a new nonlinear two-country dynamic model that captures rare but severe adverse shocks. The RAMM can be used to assess the financial stability implications of both domestic and foreign-originated risk scenarios.

Forecasting Banks’ Corporate Loan Losses Under Stress: A New Corporate Default Model

Technical Report No. 122 Gabriel Bruneau, Thibaut Duprey, Ruben Hipp
We present a new corporate default model, one of the building blocks of the Bank of Canada’s bank stress-testing infrastructure. The model is used to forecast corporate loan losses of the Canadian banking sector under stress.

Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models

Staff Discussion Paper 2022-19 James Younker
This paper derives a calculation for the effective degrees of freedom of a forecast combination under a set of general conditions for linear models. Computing effective degrees of freedom shows that the complexity cost of a forecast combination is driven by the parameters in the weighting scheme and the weighted average of parameters in the auxiliary models.
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