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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/13039 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/13039 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/13039 archive 351 disk0/00/00/80/41 2016-01-15 08:38:58 2021-08-27 17:19:37 2016-01-15 08:38:58 monograph show 2 Alberth S. 8244 pub iis_met iis_mod prog_ime prog_ysp To 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 the results were considered in 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. 2006 published IR-06-058 IR-06-058 1114 public interim_report IIASA, Laxenburg, Austria 37 FALSE info:eu-repo/semantics/book