C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Analysis - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-28T18:03:10+00:00Flood risk and residential lending
https://www.bankofcanada.ca/2024/01/flood-risk-and-residential-lending/
We present key findings of a recent study that evaluates the credit risk that flooding poses to the residential lending activities of Canadian banks and credit unions. Results show that such risk currently appears modest but could become larger with climate change.2024-01-15T15:00:26+00:00enFlood risk and residential lending2024-01-15Climate-Related Flood Risk to Residential Lending Portfolios in Canada
https://www.bankofcanada.ca/2023/12/staff-discussion-paper-2023-33/
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.2023-12-20T15:56:24+00:00enClimate-Related Flood Risk to Residential Lending Portfolios in Canada2023-12-20Central bank researchClimate changeCredit risk managementEconometric and statistical methodsFinancial institutionsFinancial stabilityStaff Discussion Paper 2023-33https://www.bankofcanada.ca/wp-content/uploads/2023/12/sdp2023-33.pdfClimate-Related Flood Risk to Residential Lending Portfolios in CanadaCraig JohnstonGeneviève ValléeHossein HosseiniBrett LindsayMiguel MolicoMarie-Christine TremblayAidan WittsDecember 2023CC8C81GG2G21QQ5Q54Identifying Nascent High-Growth Firms Using Machine Learning
https://www.bankofcanada.ca/2023/10/staff-working-paper-2023-53/
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.2023-10-16T15:24:11+00:00enIdentifying Nascent High-Growth Firms Using Machine Learning2023-10-16Econometric and statistical methodsFirm dynamicsStaff Working Paper 2023-53https://www.bankofcanada.ca/wp-content/uploads/2023/10/swp2023-53.pdfIdentifying Nascent High-Growth Firms Using Machine LearningStephanie HouleRyan MacdonaldOctober 2023CC5C55C8C81LL2L25Cryptoasset Ownership and Use in Canada: An Update for 2022
https://www.bankofcanada.ca/2023/07/staff-discussion-paper-2023-14/
We find that Bitcoin ownership declined from 13% in 2021 to 10% in 2022. This drop occurred against a background of steep price declines and an increasingly tight regulatory atmosphere.2023-07-26T13:38:30+00:00enCryptoasset Ownership and Use in Canada: An Update for 20222023-07-26Bank notesDigital currencies and fintechEconometric and statistical methodsStaff Discussion Paper 2023-14https://www.bankofcanada.ca/wp-content/uploads/2023/07/sdp2023-14.pdfStaff Discussion Paper 2023-14Daniela BalutelChristopher HenryDoina RusuJuly 2023CC8C81EE4OO5O51Historical Data on Repurchase Agreements from the Canadian Depository for Securities
https://www.bankofcanada.ca/2022/05/technical-report-121/
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.2022-05-03T08:57:44+00:00enHistorical Data on Repurchase Agreements from the Canadian Depository for Securities2022-05-03Econometric and statistical methodsFinancial marketsTechnical Report 121https://www.bankofcanada.ca/wp-content/uploads/2022/05/tr121.pdfHistorical Data on Repurchase Agreements from the Canadian Depository for SecuritiesMaxim RalchenkoAdrian WaltonMay 2022CC5C55C8C81GG1G10Business Closures and (Re)Openings in Real Time Using Google Places
https://www.bankofcanada.ca/2022/01/staff-working-paper-2022-1/
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.2022-01-04T13:42:26+00:00enBusiness Closures and (Re)Openings in Real Time Using Google Places2022-01-04Firm dynamicsRecent economic and financial developmentsStaff Working Paper 2022-1https://www.bankofcanada.ca/wp-content/uploads/2022/01/swp2022-1.pdfBusiness Closures and (Re)Openings in Real Time Using Google PlacesThibaut DupreyDaniel E. RigobonPhilip SchnattingerArtur KotlickiSoheil BaharianT. R. HurdJanuary 2022CC5C55C8C81DD2D22EE3E32Survival Analysis of Bank Note Circulation: Fitness, Network Structure and Machine Learning
https://www.bankofcanada.ca/2020/08/staff-working-paper-2020-33/
Using the Bank of Canada's Currency Information Management Strategy, we analyze the network structure traced by a bank note’s travel in circulation and find that the denomination of the bank note is important in our potential understanding of the demand and use of cash.2020-08-19T08:55:16+00:00enSurvival Analysis of Bank Note Circulation: Fitness, Network Structure and Machine Learning2020-08-19Bank notesEconometric and statistical methodsPayment clearing and settlement systemsStaff Working Paper 2020-33https://www.bankofcanada.ca/wp-content/uploads/2020/08/swp2020-33.pdfStaff Working Paper 2020-33Diego RojasJuan EstradaKim HuynhDavid T. Jacho-ChávezAugust 2020CC5C52C6C65C8C81EE4E42E5E51Sample Calibration of the Online CFM Survey
https://www.bankofcanada.ca/2020/08/technical-report-118/
The Canadian Financial Monitor (CFM) survey uses non-probability sampling for data collection, so selection bias is likely. We outline methods for obtaining survey weights and discuss the conditions necessary for these weights to eliminate selection bias. We obtain calibration weights for the 2018 and 2019 online CFM samples.2020-08-12T08:56:17+00:00enSample Calibration of the Online CFM Survey2020-08-12Econometric and statistical methodsTechnical Report 2020-118https://www.bankofcanada.ca/wp-content/uploads/2020/08/tr118.pdfTechnical Report 2020-118Marie-Hélène FeltDavid LaferrièreAugust 2020CC8C81C832017 Methods-of-Payment Survey: Sample Calibration and Variance Estimation
https://www.bankofcanada.ca/2018/12/technical-report-114/
This technical report describes sampling, weighting and variance estimation for the Bank of Canada’s 2017 Methods-of-Payment Survey. Under quota sampling, a raking ratio method is implemented to generate weights with both post-stratification and nonparametric nonresponse weight adjustments.2018-12-14T09:59:55+00:00en2017 Methods-of-Payment Survey: Sample Calibration and Variance Estimation2018-12-14Econometric and statistical methodsTechnical Report 114https://www.bankofcanada.ca/wp-content/uploads/2018/12/tr114.pdfBond Funds and Fixed-Income Market Liquidity: A Stress-Testing ApproachHeng ChenMarie-Hélène FeltChristopher HenryDecember 2018CC8C81C83What’s Up with Unit Non-Response in the Bank of Canada’s Business Outlook Survey? The Effect of Staff Tenure
https://www.bankofcanada.ca/2017/09/staff-discussion-paper-2017-11/
Since 1997, the Bank of Canada’s regional offices have been conducting the Business Outlook Survey (BOS), a quarterly survey of business conditions. Survey responses are gathered through face-to-face, confidential consultations with a sample of private sector firms representative of the various sectors, firm sizes and regions across Canada.2017-09-27T11:42:51+00:00enWhat’s Up with Unit Non-Response in the Bank of Canada’s Business Outlook Survey? The Effect of Staff Tenure2017-09-27Econometric and statistical methodsFirm dynamicsRegional economic developmentsStaff Discussion Paper 2017-11https://www.bankofcanada.ca/wp-content/uploads/2017/09/sdp2017-11.pdfWhat’s Up with Unit Non-Response in the Bank of Canada’s Business Outlook Survey? The Effect of Staff TenureSarah MillerDavid AmiraultLaurent MartinSeptember 2017CC2C21C8C81DD2D22