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

François Lafond, Aimee Gotway Bailey, Jan David Bakker, Dylan Rebois, Rubina Zadourian, Patrick McSharry and J. Farmer

Technological Forecasting and Social Change, 2018, vol. 128, issue C, 104-117

Abstract: 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.

Keywords: Forecasting; Technological progress; Experience curves (search for similar items in EconPapers)
JEL-codes: C53 O30 Q47 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Working Paper: How well do experience curves predict technological progress? A method for making distributional forecasts (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:128:y:2018:i:c:p:104-117

DOI: 10.1016/j.techfore.2017.11.001

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