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 Research Topic(s): Business fluctuations and cycles JEL Code(s): C, C5, C53, C55, E, E3, E37
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
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
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 Research 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 Research 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 Research Topic(s): Economic models, Inflation and prices, Labour markets, Monetary policy and uncertainty JEL Code(s): C, C5, C50, C51, C52, C53, C54, C55
Turning Words into Numbers: Measuring News Media Coverage of Shortages Staff Discussion Paper 2023-8 Lin Chen, Stephanie Houle We develop high-frequency, news-based indicators using natural language processing methods to analyze news media texts. Our indicators track both supply (raw, intermediate and final goods) and labour shortages over time. They also provide weekly time-varying topic narratives about various types of shortages. Content Type(s): Staff research, Staff discussion papers Research Topic(s): Coronavirus disease (COVID-19), Econometric and statistical methods, Monetary policy and uncertainty, Recent economic and financial developments JEL Code(s): C, C5, C55, C8, C82, E, E3, E37
Sectoral Uncertainty Staff Working Paper 2022-38 Efrem Castelnuovo, Kerem Tuzcuoglu, Luis Uzeda We propose a new empirical framework that jointly decomposes the conditional variance of economic time series into a common and a sector-specific uncertainty component. We apply our framework to a disaggregated industrial production series for the US economy. We identify unexpected changes in durable goods uncertainty as drivers of downturns, while unexpected hikes in non-durable goods uncertainty are expansionary. Content Type(s): Staff research, Staff working papers Research Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Monetary policy and uncertainty JEL Code(s): C, C5, C51, C55, E, E3, E32, E4, E44
Historical Data on Repurchase Agreements from the Canadian Depository for Securities Technical Report No. 121 Maxim Ralchenko, Adrian Walton We develop an algorithm that extracts information about sale and repurchase agreements (repos) from disaggregated settlement data in order to generate a new historical dataset for research. Content Type(s): Staff research, Technical reports Research Topic(s): Econometric and statistical methods, Financial markets JEL Code(s): C, C5, C55, C8, C81, G, G1, G10
Macroeconomic Predictions Using Payments Data and Machine Learning Staff Working Paper 2022-10 James Chapman, Ajit Desai We demonstrate the usefulness of payment systems data and machine learning models for macroeconomic predictions and provide a set of econometric tools to overcome associated challenges. Content Type(s): Staff research, Staff working papers Research Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Payment clearing and settlement systems JEL Code(s): C, C5, C53, C55, E, E3, E37, E4, E42, E5, E52