Elsevier

Research Policy

Volume 45, Issue 3, April 2016, Pages 647-665
Research Policy

How predictable is technological progress?

https://doi.org/10.1016/j.respol.2015.11.001Get rights and content
Under a Creative Commons license
open access

Highlights

  • A generalized version of Moore's law for unit costs is modelled as a correlated geometric random walk with drift.

  • We study 53 technologies from different sectors.

  • We model forecast errors and empirically test hypotheses about predictability.

  • We apply our method to make forecasts for the price of photovoltaic modules.

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.

JEL classification

C53
O30
Q47

Keywords

Forecasting
Technological progress
Moore's law
Solar energy

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