C55 - Large Data Sets: Modeling and Analysis

• 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 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 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

• 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 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 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 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 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

• Business Closures and (Re)Openings in Real Time Using Google Places

  Staff Working Paper 2022-1 Thibaut Duprey, Daniel E. Rigobon, Philip Schnattinger, Artur Kotlicki, Soheil Baharian, T. R. Hurd

  The COVID-19 pandemic highlighted the need for policy-makers to closely monitor disruptions to the retail and food business sectors. We present a new method to measure business opening and closing rates using real-time data from Google Places, the dataset behind the Google Maps service.

  Content Type(s): Staff research, Staff working papers Topic(s): Firm dynamics, Recent economic and financial developments JEL Code(s): C, C5, C55, C8, C81, D, D2, D22, E, E3, E32

• Payment Habits During COVID-19: Evidence from High-Frequency Transaction Data

  Staff Working Paper 2021-43 Tatjana Dahlhaus, Angelika Welte

  We examine how consumers have adjusted their payment habits during the COVID-19 pandemic. They seem to perform fewer transactions, spend more in each transaction, use less cash at the point of sale and withdraw cash from ATMs linked to their financial institution more often than from other ATMs.

  Content Type(s): Staff research, Staff working papers Topic(s): Coronavirus disease (COVID-19), Domestic demand and components, Payment clearing and settlement systems, Recent economic and financial developments JEL Code(s): C, C2, C22, C5, C55, D, D1, D12, E, E2, E21, E4, E42, E5, E52