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 JEL Code(s): C, C5, C53, C55, E, E3, E37 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting
CBDC in the Market for Payments at the Point of Sale: Equilibrium Impact and Incumbent Responses Staff working paper 2024-52 Walter Engert, Oleksandr Shcherbakov, André Stenzel We simulate introducing a central bank digital currency (CBDC) and consider consumer adoption, merchant acceptance and usage at the point of sale. Modest adoption frictions significantly inhibit CBDC market penetration along all three dimensions. Incumbent responses to restore pre-CBDC market shares are moderate to small and further reduce the impact of a CBDC. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C5, C51, D, D1, D12, E, E4, E42, L, L1, L14, L5, L52 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Digital assets and fintech, Payment and financial market infrastructures, Retail payments
Bouncing Back: How Mothballing Curbs Prices Staff working paper 2024-51 Thibaut Duprey, Artur Kotlicki, Daniel E. Rigobon, Philip Schnattinger We investigate the macroeconomic impacts of mothballed businesses—those that closed temporarily—on sectoral equilibrium prices after a negative demand shock. Our results suggest that pandemic fiscal support for temporary closures may have eased inflationary pressures. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C5, C55, C8, C81, D, D2, D22, E, E3, E32 Research Theme(s): Models and tools, Economic models, Monetary policy, Inflation dynamics and pressures, Real economy and forecasting
The impact of a central bank digital currency on payments at the point of sale Staff analytical note 2024-27 Walter Engert, Oleksandr Shcherbakov, André Stenzel We simulate the impact of a central bank digital currency (CBDC) on consumer adoption, merchant acceptance and use of different payment methods. Modest frictions that deter consumer adoption of a CBDC inhibit its market penetration. Minor pricing responses by financial institutions and payment service providers further reduce the impact of a CBDC. Content Type(s): Staff research, Staff analytical notes JEL Code(s): C, C5, C51, D, D1, D12, E, E4, E42, L, L1, L14, L5, L52 Research Theme(s): Money and payments, Digital assets and fintech, Retail payments
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
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 JEL Code(s): C, C1, C11, C3, C32, C5, C53 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Monetary policy framework and transmission
Deriving Longer-Term Inflation Expectations and Inflation Risk Premium Measures for Canada Staff discussion paper 2024-9 Bruno Feunou, Zabi Tarshi We present two models for long-term inflation expectations and inflation risk premiums for Canada. Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C5, C58, E, E4, E43, E47, G, G1, G12 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Inflation dynamics and pressures, Monetary policy framework and transmission
Non-Parametric Identification and Testing of Quantal Response Equilibrium Staff working paper 2024-24 Johannes Hoelzemann, Ryan Webb, Erhao Xie We show that the utility function and the error distribution are non-parametrically over-identified under Quantal Response Equilibrium (QRE). This leads to a simple test for QRE. We illustrate our method in a Monte Carlo exercise and a laboratory experiment. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C14, C5, C57, C9, C92 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models
Decomposing Systemic Risk: The Roles of Contagion and Common Exposures Staff working paper 2024-19 Grzegorz Halaj, Ruben Hipp We examine systemic risks within the Canadian banking sector, decomposing them into three contribution channels: contagion, common exposures, and idiosyncratic risk. Through a structural model, we dissect how interbank relationships and market conditions contribute to systemic risk, providing new insights for financial stability. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C3, C32, C5, C51, G, G2, G21, L, L1, L14 Research Theme(s): Financial system, Financial stability and systemic risk, Models and tools, Econometric, statistical and computational methods, Economic models
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