How well do experience curves predict technological progress? A method for making distributional forecasts

Experience curves are widely used to predict the cost benefits of increasing the deployment of a technology. But how good are such forecasts? Can one predict their accuracy a priori? In this paper we answer these questions by developing a method to make distributional forecasts for experience curves...

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Bibliographic Details
Published in:Technological forecasting & social change 2018-03, Vol.128, p.104-117
Main Authors: Lafond, François, Bailey, Aimee Gotway, Bakker, Jan David, Rebois, Dylan, Zadourian, Rubina, McSharry, Patrick, Farmer, J. Doyne
Format: Article
Language:English
Subjects:
Experience curves
Forecasting
Information technology
Photovoltaic cells
Pricing
Technological change
Technological planning
Technological progress
Technology
Test procedures
Time series
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Summary:Experience curves are widely used to predict the cost benefits of increasing the deployment of a technology. But how good are such forecasts? Can one predict their accuracy a priori? In this paper we answer these questions by developing a method to make distributional forecasts for experience curves. We test our method using a dataset with proxies for cost and experience for 51 products and technologies and show that it works reasonably well. The framework that we develop helps clarify why the experience curve method often gives similar results to simply assuming that costs decrease exponentially. To illustrate our method we make a distributional forecast for prices of solar photovoltaic modules. •We develop a method to make distributional forecasts using experience curves.•We test the method using hindcasting.•We illustrate the method using prices of solar photovoltaic modules.
ISSN:0040-1625
1873-5509
DOI:10.1016/j.techfore.2017.11.001