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 Research Topic(s): Central bank research, Firm dynamics, Fiscal policy, Inflation and prices JEL Code(s): C, C5, C55, C8, C81, D, D2, D22, E, E3, E32
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 Research Topic(s): Bank notes, Digital currencies and fintech, Econometric and statistical methods, Financial services JEL Code(s): C, C5, C51, D, D1, D12, E, E4, E42, L, L1, L14, L5, L52
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 Research Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C4, C5, C52, C8, E, E0, E01, E2, E21
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
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 Research Topic(s): Econometric and statistical methods JEL Code(s): C, C5, C58, E, E4, E43, E47, G, G1, G12
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 Research Topic(s): Econometric and statistical methods, Economic models JEL Code(s): C, C1, C14, C5, C57, C9, C92
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 Research Topic(s): Econometric and statistical methods, Economic models, Financial institutions, Financial stability JEL Code(s): C, C3, C32, C5, C51, G, G2, G21, L, L1, L14
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 Research Topic(s): Digital currencies and fintech, Financial institutions, Financial services, Financial system regulation and policies, Payment clearing and settlement systems JEL Code(s): C, C4, C45, C5, C55, D, D8, D83, E, E4, E42
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