C53 - Forecasting and Prediction Methods; Simulation Methods - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-29T07:59:43+00:00Forecasting Recessions in Canada: An Autoregressive Probit Model Approach
https://www.bankofcanada.ca/2024/03/staff-working-paper-2024-10/
We forecast recessions in Canada using an autoregressive (AR) probit model. The results highlight the short-term predictive power of the US economic activity and suggest that financial indicators are reliable predictors of Canadian recessions. In addition, the suggested model meaningfully improves the ability to forecast Canadian recessions, relative to a variety of probit models proposed in the Canadian literature.2024-03-27T12:42:07+00:00enForecasting Recessions in Canada: An Autoregressive Probit Model Approach2024-03-27Business fluctuations and cyclesEconometric and statistical methodsStaff Working Paper 2024-10https://www.bankofcanada.ca/wp-content/uploads/2024/03/swp2024-10.pdfForecasting Recessions in Canada: An Autoregressive Probit Model ApproachAntoine Poulin-MooreKerem TuzcuogluMarch 2024CC5C51C53EE3E32Predictive Density Combination Using a Tree-Based Synthesis Function
https://www.bankofcanada.ca/2023/12/staff-working-paper-2023-61/
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.2023-12-28T13:20:25+00:00enPredictive Density Combination Using a Tree-Based Synthesis Function2023-12-28Econometric and statistical methodsStaff Working Paper 2023-61https://www.bankofcanada.ca/wp-content/uploads/2023/12/swp2023-61.pdfPredictive Density Combination Using a Tree-Based Synthesis FunctionTony ChernisNiko HauzenbergerFlorian HuberGary KoopJames MitchellDecember 2023CC1C11C3C32C5C53A Blueprint for the Fourth Generation of Bank of Canada Projection and Policy Analysis Models
https://www.bankofcanada.ca/2023/10/staff-discussion-paper-2023-23/
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.2023-10-12T14:56:09+00:00enA Blueprint for the Fourth Generation of Bank of Canada Projection and Policy Analysis Models2023-10-12Economic modelsInflation and pricesLabour marketsMonetary policy and uncertaintyStaff Discussion Paper 2023-23https://www.bankofcanada.ca/wp-content/uploads/2023/10/sdp2023-23.pdfA Blueprint for the Fourth Generation of Bank of Canada Projection and Policy Analysis ModelsDonald ColettiOctober 2023CC5C50C51C52C53C54C55Predicting Changes in Canadian Housing Markets with Machine Learning
https://www.bankofcanada.ca/2023/09/staff-discussion-paper-2023-21/
We apply two machine learning algorithms to forecast monthly growth of house prices and existing homes sales in Canada. Although the algorithms can sometimes outperform a linear model, the improvement in forecast accuracy is not always statistically significant.2023-09-29T13:04:48+00:00enPredicting Changes in Canadian Housing Markets with Machine Learning2023-09-29Econometric and statistical methodsFinancial marketsHousingStaff Discussion Paper 2023-21https://www.bankofcanada.ca/wp-content/uploads/2023/09/sdp2023-21.pdfStaff Discussion Paper 2023-21Johan BrannlundHelen LaoMaureen MacIsaacJing YangSeptember 2023ACC4C45C5C53DD2RR2R3Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis
https://www.bankofcanada.ca/2023/08/staff-working-paper-2023-45/
I show how to combine large numbers of forecasts using several approaches within the framework of a Bayesian predictive synthesis. I find techniques that choose and combine a handful of forecasts, known as global-local shrinkage priors, perform best.2023-08-21T11:45:16+00:00enCombining Large Numbers of Density Predictions with Bayesian Predictive Synthesis2023-08-21Econometric and statistical methodsStaff Working Paper 2023-45https://www.bankofcanada.ca/wp-content/uploads/2023/08/swp2023-45.pdfStaff Working Paper 2023-45Tony ChernisAugust 2023CC1C11C5C52C53EE3E37Forecasting Banks’ Corporate Loan Losses Under Stress: A New Corporate Default Model
https://www.bankofcanada.ca/2022/10/technical-report-122/
We present a new corporate default model, one of the building blocks of the Bank of Canada’s bank stress-testing infrastructure. The model is used to forecast corporate loan losses of the Canadian banking sector under stress.2022-10-03T16:29:10+00:00enForecasting Banks’ Corporate Loan Losses Under Stress: A New Corporate Default Model2022-10-03Economic modelsFinancial institutionsFinancial stabilityFinancial system regulation and policiesTechnical Report 2022-122https://www.bankofcanada.ca/wp-content/uploads/2022/10/tr122.pdfTechnical Report 2022-122Gabriel BruneauThibaut DupreyRuben HippOctober 2022CC2C22C5C52C53GG1G17G2G21G28Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models
https://www.bankofcanada.ca/2022/09/staff-discussion-paper-2022-19/
This paper derives a calculation for the effective degrees of freedom of a forecast combination under a set of general conditions for linear models. Computing effective degrees of freedom shows that the complexity cost of a forecast combination is driven by the parameters in the weighting scheme and the weighted average of parameters in the auxiliary models.2022-09-20T11:00:03+00:00enCalculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models2022-09-20Econometric and statistical methodsStaff Discussion Paper 2022-19https://www.bankofcanada.ca/wp-content/uploads/2022/09/sdp2022-19.pdfCalculating Effective Degrees of Freedom for Forecast Combinations and Ensemble ModelsJames YounkerSeptember 2022CC0C01C02C1C13C5C50C51C52C53Nowcasting Canadian GDP with Density Combinations
https://www.bankofcanada.ca/2022/05/staff-discussion-paper-2022-12/
We present a tool for creating density nowcasts for Canadian real GDP growth. We demonstrate that the combined densities are a reliable and accurate tool for assessing the state of the economy and risks to the outlook.2022-05-09T15:44:55+00:00enNowcasting Canadian GDP with Density Combinations2022-05-09Econometric and statistical methodsStaff Discussion Paper 2022-12https://www.bankofcanada.ca/wp-content/uploads/2022/05/sdp2022-12.pdfTony ChernisTaylor WebleyMay 2022CC5C52C53EE3E7Macroeconomic Predictions Using Payments Data and Machine Learning
https://www.bankofcanada.ca/2022/03/staff-working-paper-2022-10/
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.2022-03-04T11:15:37+00:00enMacroeconomic Predictions Using Payments Data and Machine Learning2022-03-04Business fluctuations and cyclesEconometric and statistical methodsPayment clearing and settlement systemsStaff Working Paper 2022-10https://www.bankofcanada.ca/wp-content/uploads/2022/03/swp2022-10.pdfMacroeconomic Predictions Using Payments Data and Machine LearningJames ChapmanAjit DesaiMarch 2022CC5C53C55EE3E37E4E42E5E52Assessing Climate-Related Financial Risk: Guide to Implementation of Methods
https://www.bankofcanada.ca/2022/01/technical-report-120/
A pilot project on climate transition scenarios by the Bank of Canada and the Office of the Superintendent of Financial Institutions assessed climate-related credit and market risks. This report describes the project’s methodologies and provides guidance on implementing them.2022-01-14T12:00:37+00:00enAssessing Climate-Related Financial Risk: Guide to Implementation of Methods2022-01-14Climate changeCredit and credit aggregatesEconometric and statistical methodsFinancial stabilityTechnical Report 2022-120https://www.bankofcanada.ca/wp-content/uploads/2021/11/tr120.pdfHossein HosseiniCraig JohnstonCraig LoganMiguel MolicoXiangjin ShenMarie-Christine TremblayJanuary 2022CC5C53C8C83GG1G3G32