C53 - Forecasting and Prediction Methods; Simulation Methods - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-28T15:21:44+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 2010CC5C50C53EE3E37E4E47Semi-Structural Models for Inflation Forecasting
https://www.bankofcanada.ca/2010/12/working-paper-2010-34/
We propose alternative single-equation semi-structural models for forecasting inflation in Canada, whereby structural New Keynesian models are combined with time-series features in the data. Several marginal cost measures are used, including one that in addition to unit labour cost also integrates relative price shocks known to play an important role in open-economies.2010-12-20T11:43:12+00:00enSemi-Structural Models for Inflation Forecasting2010-12-20Econometric and statistical methodsInflation and pricesWorking Paper 2010-34https://www.bankofcanada.ca/wp-content/uploads/2010/12/wp10-34.pdfSemi-Structural Models for Inflation ForecastingMaral KichianRumler FabioPaul CorriganDecember 2010CC1C13C5C53EE3E31On 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