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dc.contributor.authorAlberth, Stephan
dc.date.accessioned2007-08-22T09:47:27Z
dc.date.available2007-08-22T09:47:27Z
dc.date.issued2007-02
dc.identifier.otherCWPE0710
dc.identifier.otherEPRG0702
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/194690
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/194690
dc.description.abstractTo further our understanding of the effectiveness of learning or experience curves to forecast technology costs, a statistical analysis using historical data has been carried out. Three hypotheses have been tested using available data sets that together shed light on the ability of experience curves to forecast future technology costs. The results indicate that the Single Factor Learning Curve is a highly effective estimator of future costs with little bias when errors were viewed in their log format. However, it was also found that due to the convexity of the log curve an overestimation of potential cost reductions arises when returned to their monetary units. Furthermore the effectiveness of increasing weights for more recent data was tested using Weighted Least Squares with exponentially increasing weights. This resulted in forecasts that were typically less biased than when using Ordinary Least Square and highlighted the potential benefits of this method.en
dc.language.isoenen
dc.publisherFaculty of Economics
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.relation.ispartofseriesEPRG Working Paper
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectforecastingen
dc.subjectlearning curvesen
dc.subjectrenewable energyen
dc.titleForecasting technology costs via the Learning Curve - Myth or Magic?en
dc.typeWorking Paperen
dc.identifier.doi10.17863/CAM.5166


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