Quantile VARs and Macroeconomic Risk Forecasting Staff Working Paper 2025-4 Stéphane Surprenant This paper provides an extensive evaluation of the performance of quantile vector autoregression (QVAR) to forecast macroeconomic risk. Generally, QVAR outperforms standard benchmark models. Moreover, QVAR and QVAR augmented with factors perform equally well. Both are adequate for modeling macroeconomic risks. Content Type(s): Staff research, Staff working papers Research Topic(s): Business fluctuations and cycles JEL Code(s): C, C5, C53, C55, E, E3, E37
Decision Synthesis in Monetary Policy Staff Working Paper 2024-30 Tony Chernis, Gary Koop, Emily Tallman, Mike West We use Bayesian predictive decision synthesis to formalize monetary policy decision-making. We develop a case-study of monetary policy decision-making of an inflation-targeting central bank using multiple models in a manner that considers decision goals, expectations and outcomes. Content Type(s): Staff research, Staff working papers Research Topic(s): Econometric and statistical methods, Economic models, Monetary policy JEL Code(s): C, C1, C11, C3, C32, C5, C53
Forecasting Recessions in Canada: An Autoregressive Probit Model Approach Staff Working Paper 2024-10 Antoine Poulin-Moore, Kerem Tuzcuoglu We forecast recessions in Canada using an autoregressive (AR) probit model. The results highlight the short-term predictive power of the US economic activity and suggest that financial indicators are reliable predictors of Canadian recessions. In addition, the suggested model meaningfully improves the ability to forecast Canadian recessions, relative to a variety of probit models proposed in the Canadian literature. Content Type(s): Staff research, Staff working papers Research Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C5, C51, C53, E, E3, E32
Predictive Density Combination Using a Tree-Based Synthesis Function Staff Working Paper 2023-61 Tony Chernis, Niko Hauzenberger, Florian Huber, Gary Koop, James Mitchell 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 Research Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C3, C32, C5, C53
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. Content Type(s): Staff research, Staff discussion papers Research Topic(s): Economic models, Inflation and prices, Labour markets, Monetary policy and uncertainty JEL Code(s): C, C5, C50, C51, C52, C53, C54, C55
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. Content Type(s): Staff research, Staff discussion papers Research Topic(s): Econometric and statistical methods, Financial markets, Housing JEL Code(s): A, C, C4, C45, C5, C53, D, D2, R, R2, R3
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 Research Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C5, C52, C53, E, E3, E37
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. Content Type(s): Staff research, Technical reports Research Topic(s): Economic models, Financial institutions, Financial stability, Financial system regulation and policies JEL Code(s): C, C2, C22, C5, C52, C53, G, G1, G17, G2, G21, G28
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. Content Type(s): Staff research, Staff discussion papers Research Topic(s): Econometric and statistical methods JEL Code(s): C, C0, C01, C02, C1, C13, C5, C50, C51, C52, C53
Nowcasting Canadian GDP with Density Combinations Staff Discussion Paper 2022-12 Tony Chernis, Taylor Webley We present a tool for creating density nowcasts for Canadian real GDP growth. We demonstrate that the combined densities are a reliable and accurate tool for assessing the state of the economy and risks to the outlook. Content Type(s): Staff research, Staff discussion papers Research Topic(s): Econometric and statistical methods JEL Code(s): C, C5, C52, C53, E, E3, E7