E47 - Forecasting and Simulation: Models and Applications - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-28T12:32:04+00:00'Lean' versus 'Rich' Data Sets: Forecasting during the Great Moderation and the Great Recession
https://www.bankofcanada.ca/2010/12/working-paper-2010-37/
We evaluate forecasts for the euro area in data-rich and ‘data-lean' environments by comparing three different approaches: a simple PMI model based on Purchasing Managers' Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus data from national economies (pseudo-real time data).2010-12-23T12:55:41+00:00en'Lean' versus 'Rich' Data Sets: Forecasting during the Great Moderation and the Great Recession2010-12-23Econometric and statistical methodsInternational topicsWorking Paper 2010-37https://www.bankofcanada.ca/wp-content/uploads/2010/12/wp10-37.pdf‘Lean' versus ‘Rich' Data Sets: Forecasting during the Great Moderation and the Great RecessionMarco J. LombardiPhilipp MaierDecember 2010CC5C50C53EE3E37E4E47Losses from Simulated Defaults in Canada's Large Value Transfer System
https://www.bankofcanada.ca/2010/10/discussion-paper-2010-14/
The Large Value Transfer System (LVTS) loss-sharing mechanism was designed to ensure that, in the event of a one-participant default, the collateral pledged by direct members of the system would be sufficient to cover the largest possible net debit position of a defaulting participant. However, the situation may not hold if the indirect effects of the defaults are taken into consideration, or if two participants default during the same payment cycle.2010-10-21T14:11:53+00:00enLosses from Simulated Defaults in Canada's Large Value Transfer System2010-10-21Financial institutionsFinancial stabilityPayment clearing and settlement systemsDiscussion Paper 2010-14 https://www.bankofcanada.ca/wp-content/uploads/2010/10/dp10-14.pdfLosses from Simulated Defaults in Canada's Large Value Transfer SystemNellie ZhangTom HossfeldOctober 2010EE4E47GG2G21On the Advantages of Disaggregated Data: Insights from Forecasting the U.S. Economy in a Data-Rich Environment
https://www.bankofcanada.ca/2010/03/working-paper-2010-10/
The good forecasting performance of factor models has been well documented in the literature. While many studies focus on a very limited set of variables (typically GDP and inflation), this study evaluates forecasting performance at disaggregated levels to examine the source of the improved forecasting accuracy, relative to a simple autoregressive model. We use the latest revision of over 100 U.S. time series over the period 1974-2009 (monthly and quarterly data).2010-03-22T14:49:24+00:00enOn the Advantages of Disaggregated Data: Insights from Forecasting the U.S. Economy in a Data-Rich Environment2010-03-22Econometric and statistical methodsInternational topicsWorking Paper 2010-10https://www.bankofcanada.ca/wp-content/uploads/2010/05/wp10-10.pdfOn the Advantages of Disaggregated Data: Insights from Forecasting the U.S. Economy in a Data-Rich EnvironmentNikita PerevalovPhilipp MaierMarch 2010CC5C50C53EE3E37E4E47