How predictable is technological progress?

J.D. Farmer, F. Lafond*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Recently it has become clear that many technologies follow a generalized version of Moore's law, i.e. costs tend to drop exponentially, at different rates that depend on the technology. Here we formulate Moore's law as a correlated geometric random walk with drift, and apply it to historical data on 53 technologies. We derive a closed form expression approximating the distribution of forecast errors as a function of time. Based on hind-casting experiments we show that this works well, making it possible to collapse the forecast errors for many different technologies at different time horizons onto the same universal distribution. This is valuable because it allows us to make forecasts for any given technology with a clear understanding of the quality of the forecasts. As a practical demonstration we make distributional forecasts at different time horizons for solar photovoltaic modules, and show how our method can be used to estimate the probability that a given technology will outperform another technology at a given point in the future. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Original languageEnglish
Pages (from-to)647-665
Number of pages19
JournalResearch Policy
Volume45
Issue number3
DOIs
Publication statusPublished - 1 Apr 2016

Keywords

  • Forecasting
  • Technological progress
  • Moore's law
  • Solar energy
  • LEARNING-CURVE
  • FUNCTIONAL-APPROACH
  • EXPERIENCE CURVE
  • UNIT-ROOT
  • COSTS
  • ELECTRICITY
  • ECONOMICS
  • INTERVALS
  • TRENDS
  • LEVEL

Fingerprint

Dive into the research topics of 'How predictable is technological progress?'. Together they form a unique fingerprint.

Cite this