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193 Results

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 Topic(s): Business fluctuations and cycles JEL Code(s): C, C5, C53, C55, E, E3, E37

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.

Bouncing Back: How Mothballing Curbs Prices

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.

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.

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 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.

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.

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.

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.
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