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

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  • Franc{c}ois Lafond
  • Aimee Gotway Bailey
  • Jan David Bakker
  • Dylan Rebois
  • Rubina Zadourian
  • Patrick McSharry
  • J. Doyne Farmer

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.

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  • Franc{c}ois Lafond & Aimee Gotway Bailey & Jan David Bakker & Dylan Rebois & Rubina Zadourian & Patrick McSharry & J. Doyne Farmer, 2017. "How well do experience curves predict technological progress? A method for making distributional forecasts," Papers 1703.05979, arXiv.org, revised Sep 2017.
    • Handle: RePEc:arx:papers:1703.05979
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    2. Way, Rupert & Lafond, François & Lillo, Fabrizio & Panchenko, Valentyn & Farmer, J. Doyne, 2019. "Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves," Journal of Economic Dynamics and Control, Elsevier, vol. 101(C), pages 211-238.
    3. Edoardo Ruffino & Bruno Piga & Alessandro Casasso & Rajandrea Sethi, 2022. "Heat Pumps, Wood Biomass and Fossil Fuel Solutions in the Renovation of Buildings: A Techno-Economic Analysis Applied to Piedmont Region (NW Italy)," Energies, MDPI, vol. 15(7), pages 1-25, March.
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    5. Elizabeth Baldwin & Yongyang Cai & Karlygash Kuralbayeva, 2018. "To Build or Not to Build? Capital Stocks and Climate Policy," CESifo Working Paper Series 6884, CESifo.
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    7. Hann-Earl Kim & Yu-Sang Chang & Hee-Jin Kim, 2021. "Dynamic Electricity Intensity Trends in 91 Countries," Sustainability, MDPI, vol. 13(8), pages 1-26, April.
    8. Rubina Zadourian, 2024. "Model-based and empirical analyses of stochastic fluctuations in economy and finance," Papers 2408.16010, arXiv.org.
    9. Gary, Robert F. & Fink, Matthias & Belousova, Olga & Marinakis, Yorgos & Tierney, Robert & Walsh, Steven T., 2020. "An introduction to the field of abundant economic thought," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
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    12. Baldwin, Elizabeth & Cai, Yongyang & Kuralbayeva, Karlygash, 2020. "To build or not to build? Capital stocks and climate policy∗," Journal of Environmental Economics and Management, Elsevier, vol. 100(C).
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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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