C - Mathematical and Quantitative Methods

• 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 Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C3, C32, C5, C53

• Climate-Related Flood Risk to Residential Lending Portfolios in Canada

  Staff Discussion Paper 2023-33 Craig Johnston, Geneviève Vallée, Hossein Hosseini, Brett Lindsay, Miguel Molico, Marie-Christine Tremblay, Aidan Witts

  We assess the potential financial risks of current and projected flooding caused by extreme weather events in Canada. We focus on the residential real estate secured lending (RESL) portfolios of Canadian financial institutions (FIs) because RESL portfolios are an important component of FIs’ balance sheets and because the assets used to secure such loans are immobile and susceptible to climate-related extreme weather events.

  Content Type(s): Staff research, Staff discussion papers Topic(s): Central bank research, Climate change, Credit risk management, Econometric and statistical methods, Financial institutions, Financial stability JEL Code(s): C, C8, C81, G, G2, G21, Q, Q5, Q54

• Understanding the Systemic Implications of Climate Transition Risk: Applying a Framework Using Canadian Financial System Data

  Staff Discussion Paper 2023-32 Gabriel Bruneau, Javier Ojea Ferreiro, Andrew Plummer, Marie-Christine Tremblay, Aidan Witts

  Our study aims to gain insight on financial stability and climate transition risk. We develop a methodological framework that captures the direct effects of a stressful climate transition shock as well as the indirect—or systemic—implications of these direct effects. We apply this framework using data from the Canadian financial system.

  Content Type(s): Staff research, Staff discussion papers Topic(s): Climate change, Economic models, Financial institutions, Financial markets, Financial stability JEL Code(s): C, C6, C63, G, G0, G01, G1, G10, G2, G20, Q, Q5, Q54

• Finding the balance—measuring risks to inflation and to GDP growth

  Staff Analytical Note 2023-18 Bruno Feunou, James Kyeong

  Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023.

  Content Type(s): Staff research, Staff analytical notes Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C3, C32, C5, C58, E, E4, E44, G, G1, G17

• Making It Real: Bringing Research Models into Central Bank Projections

  Staff Discussion Paper 2023-29 Marc-André Gosselin, Sharon Kozicki

  Macroeconomic projections and risk analyses play an important role in guiding monetary policy decisions. Models are integral to this process. This paper discusses how the Bank of Canada brings research models and lessons learned from those models into the central bank projection environment.

  Content Type(s): Staff research, Staff discussion papers Topic(s): Economic models, Monetary policy JEL Code(s): C, C3, C32, C5, C51, E, E3, E37, E4, E47, E5, E52

• Testing Collusion and Cooperation in Binary Choice Games

  Staff Working Paper 2023-58 Erhao Xie

  This paper studies the testable implication of players’ collusive or cooperative behaviour in a binary choice game with complete information. I illustrate the implementation of this test by revisiting the entry game between Walmart and Kmart.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Market structure and pricing JEL Code(s): C, C5, C57, L, L1, L13

• Machine learning for economics research: when, what and how

  Staff Analytical Note 2023-16 Ajit Desai

  This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used.

  Content Type(s): Staff research, Staff analytical notes Topic(s): Central bank research, Econometric and statistical methods, Economic models JEL Code(s): A, A1, A10, B, B2, B23, C, C4, C45, C5, C55

• Identifying Nascent High-Growth Firms Using Machine Learning

  Staff Working Paper 2023-53 Stephanie Houle, Ryan Macdonald

  Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Firm dynamics JEL Code(s): C, C5, C55, C8, C81, L, L2, L25

• A Blueprint for the Fourth Generation of Bank of Canada Projection and Policy Analysis Models

  Staff Discussion Paper 2023-23 Donald Coletti

  The fourth generation of Bank of Canada projection and policy analysis models seeks to improve our understanding of inflation dynamics, the supply side of the economy and the underlying risks faced by policy-makers coming from uncertainty about how the economy functions.

  Content Type(s): Staff research, Staff discussion papers Topic(s): Economic models, Inflation and prices, Labour markets, Monetary policy and uncertainty JEL Code(s): C, C5, C50, C51, C52, C53, C54, C55

• Three things we learned about the Lynx payment system

  Staff Analytical Note 2023-14 Nikil Chande, Zhentong Lu, Hiru Rodrigo, Phoebe Tian

  Canada transitioned to a new wholesale payment system, Lynx, in August 2021. Lynx is based on a real-time settlement model that eliminates credit risk in the system. This model can require more liquidity; however, Lynx’s design allows Canada’s wholesale payments to settle efficiently.

  Content Type(s): Staff research, Staff analytical notes Topic(s): Financial institutions, Financial services, Financial system regulation and policies, Payment clearing and settlement systems JEL Code(s): C, C1, C10, E, E4, E42, G, G2, G28