The Dynamic Canadian Debt Strategy Model Technical report No. 127 Nicolas Audet, Joe Ning, Adam Epp, Jeffrey Gao We present a dynamic debt strategy model framework designed to assist sovereign debt portfolio managers in choosing an optimal debt issuance strategy. The main innovation of this framework is the introduction of dynamic issuance strategies, which allow issuance decisions to vary over time based on the model’s simulated state variables. Content Type(s): Staff research, Technical reports JEL Code(s): C, C6, C61, G, G1, G11, G17, H, H6, H63, H68 Research Theme(s): Financial markets and funds management, Funds management, Models and tools, Econometric, statistical and computational methods
Examining the Links Between Firm Performance and Insolvency Staff discussion paper 2025-10 Dylan Hogg, Hossein Hosseini Jebeli Assessing insolvency dynamics is essential for evaluating the financial health of non-financial corporations and mitigating macroeconomic and financial stability risks. This study leverages a newly created Statistics Canada dataset linking insolvency records with firm-level financial data to develop a robust framework for monitoring insolvency risk Content Type(s): Staff research, Staff discussion papers JEL Code(s): D, D2, D22, G, G3, G33, L, L2, L20 Research Theme(s): Financial system, Financial stability and systemic risk, Household and business credit, Models and tools, Econometric, statistical and computational methods
Correcting Selection Bias in a Non-Probability Two-Phase Payment Survey Staff working paper 2025-17 Heng Chen, John Tsang We develop statistical inferences for a non-probability two-phase survey sample when relevant auxiliary information is available from a probability survey sample. The proposed method is assessed by simulation studies and used to analyze a non-probability two phase payment survey. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C8, C83 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Retail payments
Partial Identification of Heteroskedastic Structural Vector Autoregressions: Theory and Bayesian Inference Staff working paper 2025-14 Helmut Lütkepohl, Fei Shang, Luis Uzeda, Tomasz Woźniak We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C11, C12, C3, C32, E, E6, E62 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Real economy and forecasting
Low Response Rate from Merchants? Sample and Ask Consumers! An Application of Indirect Sampling Under a Consumer-Merchant Bipartite Network Technical report No. 126 Heng Chen, Joy Wu Under the consumer-merchant bipartite network, we apply the indirect sampling approach to estimate merchant payment acceptance through a consumer payment diary. Content Type(s): Staff research, Technical reports JEL Code(s): C, C8, C80, C83, E, E5 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Retail payments
Estimating Discrete Choice Demand Models with Sparse Market-Product Shocks Staff working paper 2025-10 Zhentong Lu, Kenichi Shimizu We propose a novel approach to estimating consumer demand for differentiated products. We eliminate the need for instrumental variables by assuming demand shocks are sparse. Our empirical applications reveal strong evidence of sparsity in real-world datasets. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C3, D, D1, L, L0, L00 Research Theme(s): Financial markets and funds management, Market structure, Models and tools, Econometric, statistical and computational methods
Canadian Bitcoin Ownership in 2023: Key Takeaways Staff discussion paper 2025-4 Daniela Balutel, Marie-Hélène Felt, Doina Rusu The Bitcoin Omnibus Survey is an important tool for monitoring Canadians’ awareness and ownership of bitcoin and other cryptoassets over time. In this paper, we present data highlights from the 2023 survey. Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C8, C81, E, E4, O, O5, O51 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Digital assets and fintech
Estimating the inflation risk premium Staff analytical note 2025-9 Bruno Feunou, Gitanjali Kumar Is there a risk of de-anchoring of inflation expectations in the near term? We estimate the inflation risk premium using traditional asset pricing models to answer this question. The risk of de-anchoring is elevated compared with the period before the COVID-19 pandemic and is higher in the United States than in Canada. Content Type(s): Staff research, Staff analytical notes JEL Code(s): C, C2, C22, C5, C58, G, G1, G12 Research Theme(s): Financial markets and funds management, Market functioning, Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Inflation dynamics and pressures
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
Differentiable, Filter Free Bayesian Estimation of DSGE Models Using Mixture Density Networks Staff working paper 2025-3 Chris Naubert I develop a method for Bayesian estimation of globally solved, non-linear macroeconomic models. The method uses a mixture density network to approximate the initial state distribution. The mixture density network results in more reliable posterior inference compared with the case when the initial states are set to their steady-state values. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C6, C61, C63, E, E3, E37, E4, E47 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models