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A Simple Recursive Forecasting Model Author info | Abstract | Publisher info | Download info | Related research | Statistics Wiliam Branch (University of California - Irvine)
George W. Evans () (University of Oregon Economics Department)
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We compare the performance of alternative recursive forecasting models. A simple constant gain algorithm, used widely in the learning literature, both forecasts well out of sample and also provides the best fit to the Survey of Professional Forecasters.
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Paper provided by University of Oregon Economics Department in its series University of Oregon Economics Department Working Papers with number
2005-3.
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Length: 10
Date of creation: 01 Feb 2005Date of revision:
01 Feb 2005Handle: RePEc:ore:uoecwp:2005-3Contact details of provider: Postal: 1285 University of Oregon, 435 PLC, Eugene, OR 97403-1285 Phone: (541) 346-4661 Fax: (541) 346-1243 Email: Web page: http://economics.uoregon.edu/ More information through EDIRC
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Keywords: constant gain ; recursive learning ; expectations ; Other versions of this item:
Find related papers by JEL classification: E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
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