Statistical Basis for Predicting Technological Progress
Forecasting technological progress is of great interest to engineers, policy makers, and private investors. Several models have been proposed for predicting technological improvement, but how well do these models perform? An early hypothesis made by Theodore Wright in 1936 is that cost decreases as a power law of cumulative production. An alternative hypothesis is Moore's law, which can be generalized to say that technologies improve exponentially with time. Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus. These hypotheses have not previously been rigorously tested. Using a new database on the cost and production of 62 different technologies, which is the most expansive of its kind, we test the ability of six different postulated laws to predict future costs. Our approach involves hindcasting and developing a statistical model to rank the performance of the postulated laws. Wright's law produces the best forecasts, but Moore's law is not far behind. We discover a previously unobserved regularity that production tends to increase exponentially. A combination of an exponential decrease in cost and an exponential increase in production would make Moore's law and Wright's law indistinguishable, as originally pointed out by Sahal. We show for the first time that these regularities are observed in data to such a degree that the performance of these two laws is nearly tied. Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year. These results have implications for theories of technological change, and assessments of candidate technologies and policies for climate change mitigation.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
- Nordhaus, William D., 2007. "Two Centuries of Productivity Growth in Computing," The Journal of Economic History, Cambridge University Press, vol. 67(01), pages 128-159, March.
- William D. Nordhaus, 2009.
"The Perils of the Learning Model For Modeling Endogenous Technological Change,"
Cowles Foundation Discussion Papers
1685, Cowles Foundation for Research in Economics, Yale University.
- William D. Nordhaus, 2009. "The Perils of the Learning Model For Modeling Endogenous Technological Change," NBER Working Papers 14638, National Bureau of Economic Research, Inc.
- Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
- McNerney, James & Doyne Farmer, J. & Trancik, Jessika E., 2011. "Historical costs of coal-fired electricity and implications for the future," Energy Policy, Elsevier, vol. 39(6), pages 3042-3054, June.
- Gillingham, Kenneth & Newell, Richard G. & Pizer, William A., 2008.
"Modeling endogenous technological change for climate policy analysis,"
Elsevier, vol. 30(6), pages 2734-2753, November.
- Gillingham, Kenneth T. & Newell, Richard G. & Pizer, William A., 2007. "Modeling Endogenous Technological Change for Climate Policy Analysis," Discussion Papers dp-07-14, Resources For the Future.
When requesting a correction, please mention this item's handle: RePEc:arx:papers:1207.1463. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators)
If references are entirely missing, you can add them using this form.