Prima: The Bank of Canada’s New Projection and Policy-Analysis Model—An Overview Staff analytical paper 2026-47 Donald Coletti, Martin Kuncl, Martin Harding, Dmitry Matveev, Tolga Özden, Yang Zhang Prima is the Bank of Canada’s new model for projection and policy analysis. It builds on the economic foundations of earlier Bank models, adding detail on how sectoral pressures affect production costs and their pass-through to consumer prices. Content Type(s): Staff research, Staff analytical paper JEL Code(s): C, C4, C43, C8, C82, E, E3, E30, E31, E37 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Inflation dynamics and pressures, Real economy and forecasting
Sector-based producer price indexes: New measures of producer price pressures in Canada Staff analytical paper 2026-46 Yena Joo, Ali Rouhghalandari, Vivian Chu, Xin Ha Producer price indexes measure changes in the prices that producers receive for their outputs or that they pay for their inputs. Statistics Canada publishes a range of producer price indexes for specific products and industries. For the first time, these series have been combined into a small set of broad sectoral measures. Content Type(s): Staff research, Staff analytical paper JEL Code(s): C, C4, C43, C8, C82, E, E3, E30, E31, E37 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Inflation dynamics and pressures, Real economy and forecasting
Monte Carlo Likelihood-Ratio Tests for Markov Switching Models Staff working paper 2026-23 Gabriel Rodriguez Rondon, Jean-Marie Dufour This paper develops Monte Carlo likelihood-ratio tests for determining the number of regimes in Markov switching models. Unlike most existing procedures, which focus on testing one versus two regimes, the proposed methods allow testing an arbitrary number of regimes. They are valid in finite samples, robust to identification problems, and applicable to nonstationary, multivariate, and Markov switching GARCH models. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C12, C15, C2, C22, C3, C32, C4, C46, C5, C52, E, E3, E32 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Real economy and forecasting
Measuring the AI Economy Staff working paper 2026-20 Anton Korinek, Patrick McKelvey We construct a macroeconomic estimate of total AI production in the United States, combining inference and R&D/training activities with quality adjustments to account for algorithmic progress. We then develop a nascent framework for "AI GDP" that tracks the AI economy as a coherent whole, complementing traditional national accounts. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C4, C43, E, E0, E01, E2, E22, E3, E31, O, O3, O33, O4, O47 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting, Structural challenges, Digitalization and productivity
Integrating Non-traditional Data and AI into Central Banking: A Canadian Perspective Staff analytical paper 2026-17 James Chapman, Ajit Desai, Maryam Haghighi, James (Jim) C. MacGee This paper reviews how central banks are integrating non traditional data and artificial intelligence (AI) into policy analysis and operations. Using the Bank of Canada’s experience, it examines emerging applications, governance challenges, and strategic choices for responsibly scaling AI to enhance insight, efficiency, and institutional resilience. Content Type(s): Staff research, Staff analytical paper JEL Code(s): C, C4, C45, C5, C55, C8, C88, L, L2, L23, M, M1, M15, O, O3, O33 Research Theme(s): Financial system, Financial stability and systemic risk, Monetary policy, Monetary policy tools and implementation, Money and payments, Payment and financial market infrastructures
Seasonal Adjustment of Weekly Data Staff discussion paper 2024-17 Jeffrey Mollins, Rachit Lumb The industry standard for seasonally adjusting data, X-13ARIMA-SEATS, is not suitable for high-frequency data. We summarize and assess several of the most popular seasonal adjustment methods for weekly data given the increased availability and promise of non-traditional data at higher frequencies. Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C1, C4, C5, C52, C8, E, E0, E01, E2, E21 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting
Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems Staff working paper 2024-15 Ajit Desai, Anneke Kosse, Jacob Sharples Our layered machine learning framework can enhance real-time transaction monitoring in high-value payment systems, which are a central piece of a country’s financial infrastructure. When tested on data from Canadian payment systems, it demonstrated potential for accurately identifying anomalous transactions. This framework could help improve cyber and operational resilience of payment systems. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C4, C45, C5, C55, D, D8, D83, E, E4, E42 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Payment and financial market infrastructures
Machine learning for economics research: when, what and how Staff analytical note 2023-16 Ajit Desai This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used. Content Type(s): Staff research, Staff analytical notes JEL Code(s): A, A1, A10, B, B2, B23, C, C4, C45, C5, C55 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Structural challenges, Digitalization and productivity
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 JEL Code(s): A, C, C4, C45, C5, C53, D, D2, R, R2, R3 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting
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 JEL Code(s): C, C4, C8, E, E3, E31, E32, E5, E52 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Monetary policy framework and transmission, Structural challenges, Digitalization and productivity