Beating the “pros” with a semi-structural model of their own inflation forecasts Staff working paper 2026-11 Sergio A. Lago Alves, Waldyr Dutra Areosa, Carlos Viana de Carvalho How can Surveys of Professional Forecasters (SPF) be used to improve inflation forecasts? By using US historical quarterly data on SPF forecasts, we provide better understanding of how we can use forecast disagreement to improve our own forecasts. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C11, C5, C53, E, E3, E31, E37 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Inflation dynamics and pressures, Real economy and forecasting
Estimation and Inference for Stochastic Volatility Models with Heavy-Tailed Distributions Staff working paper 2026-8 Gabriel Rodriguez Rondon, Jean-Marie Dufour, Md. Nazmul Ahsan Statistical inference--both estimation and testing--for stochastic volatility (SV) models is known to be challenging and computationally demanding. We propose simple and efficient estimators for SV models with conditionally heavy-tailed error distributions, particularly the Student’s t and Generalized Exponential Distributions (GED). The estimators rely on a small set of moment conditions derived from ARMA-type representations of SV models, with an option to apply “winsorization” to improve stability and finite-sample performance. Except for the degrees of-freedom parameter, closed-form expressions are available for all other parameters, extending Ahsan and Dufour (2019, 2021), thus eliminating the need for numerical optimization or initial values. We derive the estimators’ asymptotic distribution and show that, due to their analytical tractability, they support reliable, and even exact, simulation-based inference via Monte Carlo or bootstrap methods. We assess their performance through extensive simulations and demonstrate their practical relevance in financial return data, which strongly reject the normality assumption in favor of heavy-tailed models. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C12, C13, C15, C2, C22, C5, C51, C53, C58 Research Theme(s): Financial markets and funds management, International markets and currencies, Models and tools, Econometric, statistical and computational methods, Economic models
MSTest: An R-Package for Testing Markov Switching Models Staff working paper 2026-7 Gabriel Rodriguez Rondon, Jean-Marie Dufour We present the R package MSTest, which implements hypothesis testing procedures to determine the number of regimes in Markov switching models. The package provides several testing frameworks, including Monte Carlo likelihood ratio tests, moment-based tests, parameter stability tests, and classical likelihood ratio procedures. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C12, C15, C18, C6, C63, C8, C87 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Real economy and forecasting
Net Send Limits in the Lynx Payment System: Usage and Implications Staff discussion paper 2025-13 Virgilio B Pasin, Anna Wyllie We study how participants in the Lynx payment system use the net send limit (NSL) tool to control their intraday payment outflow levels. Our results show that participants typically adopt a “set it and forget it” approach to scheduling NSLs and sometimes have distinct intraday NSL adjustment behaviours. Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C1, C10, D, D8, D82, E, E4, E42, E5, E58, G, G2, G21, G4, G41 Research Theme(s): Financial system, Financial institutions and intermediation, Money and payments, Payment and financial market infrastructures
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
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
Estimating the impacts on GDP of natural disasters in Canada Staff analytical note 2025-5 Tatjana Dahlhaus, Thibaut Duprey, Craig Johnston Extreme weather events contribute to increased volatility in both economic activity and prices, interfering with the assessment of the true underlying trends of the economy. With this in mind, we conduct a timely assessment of the impact of natural disasters on Canadian gross domestic product (GDP). Content Type(s): Staff research, Staff analytical notes JEL Code(s): B, B2, B23, C, C1, C13, C2, C23, E, E1, E17, E3, E37, E6, E62, H, H6 Research Theme(s): Monetary policy, Real economy and forecasting, Structural challenges, Climate change
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
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