IDEAS home Printed from https://ideas.repec.org/a/eee/respol/v45y2016i3p647-665.html
   My bibliography  Save this article

How predictable is technological progress?

Author

Listed:
  • Farmer, J. Doyne
  • Lafond, François

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.

Suggested Citation

  • Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
  • Handle: RePEc:eee:respol:v:45:y:2016:i:3:p:647-665
    DOI: 10.1016/j.respol.2015.11.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0048733315001699
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. repec:ucp:bknber:9780226304557 is not listed on IDEAS
    2. Colpier, Ulrika Claeson & Cornland, Deborah, 2002. "The economics of the combined cycle gas turbine--an experience curve analysis," Energy Policy, Elsevier, vol. 30(4), pages 309-316, March.
    3. Bosetti, Valentina & Catenacci, Michela & Fiorese, Giulia & Verdolini, Elena, 2012. "The future prospect of PV and CSP solar technologies: An expert elicitation survey," Energy Policy, Elsevier, vol. 49(C), pages 308-317.
    4. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    5. 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.
    6. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-144, April.
    7. Michael P. Clements & David F.Hendry, 2001. "Forecasting with difference-stationary and trend-stationary models," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-19.
    8. Erin Baker & Meredith Fowlie & Derek Lemoine & Stanley S. Reynolds, 2013. "The Economics of Solar Electricity," Annual Review of Resource Economics, Annual Reviews, vol. 5(1), pages 387-426, June.
    9. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    10. Shlyakhter, Alexander I. & Kammen, Daniel M. & Broido, Claire L. & Wilson, Richard, 1994. "Quantifying the credibility of energy projections from trends in past data : The US energy sector," Energy Policy, Elsevier, vol. 22(2), pages 119-130, February.
    11. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-135, April.
    12. Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
    13. Gavin Sinclair & Steven Klepper & Wesley Cohen, 2000. "What's Experience Got to Do With It? Sources of Cost Reduction in a Large Specialty Chemicals Producer," Management Science, INFORMS, vol. 46(1), pages 28-45, January.
    14. Schilling, Melissa A. & Esmundo, Melissa, 2009. "Technology S-curves in renewable energy alternatives: Analysis and implications for industry and government," Energy Policy, Elsevier, vol. 37(5), pages 1767-1781, May.
    15. Blough, Stephen R, 1992. "The Relationship between Power and Level for Generic Unit Root Tests in Finite Samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(3), pages 295-308, July-Sept.
    16. James W. Taylor & Derek W. Bunn, 1999. "A Quantile Regression Approach to Generating Prediction Intervals," Management Science, INFORMS, vol. 45(2), pages 225-237, February.
    17. Valentina Bosetti & Michela Catenacci & Giulia Fiorese & Elena Verdolini, 2012. "The Future Prospects of PV and CSP Solar Technologies," Review of Environment, Energy and Economics - Re3, Fondazione Eni Enrico Mattei, January.
    18. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 51-72.
    19. Peter Thompson, 2012. "The Relationship between Unit Cost and Cumulative Quantity and the Evidence for Organizational Learning-by-Doing," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 203-224, Summer.
    20. 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.
    21. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    22. William D. Nordhaus, 2014. "The Perils of the Learning Model for Modeling Endogenous Technological Change," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    23. Robert J. Gordon, 1990. "The Measurement of Durable Goods Prices," NBER Books, National Bureau of Economic Research, Inc, number gord90-1, December.
    24. Koomey, Jonathan & Hultman, Nathan E., 2007. "A reactor-level analysis of busbar costs for US nuclear plants, 1970-2005," Energy Policy, Elsevier, vol. 35(11), pages 5630-5642, November.
    25. Kenneth J. Arrow, 1962. "The Economic Implications of Learning by Doing," Review of Economic Studies, Oxford University Press, vol. 29(3), pages 155-173.
    26. Sampson, Michael, 1991. "The Effect of Parameter Uncertainty on Forecast Variances and Confidence Intervals for Unit Root and Trend Stationary Time-Series Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(1), pages 67-76, Jan.-Marc.
    27. A.G. Wilson, 1969. "Forecasting 'Planning'," Urban Studies, Urban Studies Journal Limited, vol. 6(3), pages 347-367, November.
    28. Benson, Christopher L. & Magee, Christopher L., 2014. "On improvement rates for renewable energy technologies: Solar PV, wind turbines, capacitors, and batteries," Renewable Energy, Elsevier, vol. 68(C), pages 745-751.
    29. Marvin B. Lieberman, 1984. "The Learning Curve and Pricing in the Chemical Processing Industries," RAND Journal of Economics, The RAND Corporation, vol. 15(2), pages 213-228, Summer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Magee, C.L. & Basnet, S. & Funk, J.L. & Benson, C.L., 2016. "Quantitative empirical trends in technical performance," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 237-246.
    2. repec:eee:enepol:v:116:y:2018:i:c:p:95-111 is not listed on IDEAS
    3. Newbery, David & Pollitt, Michael G. & Ritz, Robert A. & Strielkowski, Wadim, 2018. "Market design for a high-renewables European electricity system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 695-707.
    4. repec:eee:tefoso:v:128:y:2018:i:c:p:104-117 is not listed on IDEAS
    5. repec:eee:enepol:v:109:y:2017:i:c:p:270-278 is not listed on IDEAS
    6. Dosi, Giovanni & Grazzi, Marco & Mathew, Nanditha, 2017. "The cost-quantity relations and the diverse patterns of “learning by doing”: Evidence from India," Research Policy, Elsevier, vol. 46(10), pages 1873-1886.
    7. repec:eee:rensus:v:77:y:2017:i:c:p:590-595 is not listed on IDEAS
    8. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    9. repec:spr:fininn:v:4:y:2018:i:1:d:10.1186_s40854-018-0088-y is not listed on IDEAS
    10. Christopher L. Benson & Christopher L. Magee, 2018. "Data-Driven Investment Decision-Making: Applying Moore's Law and S-Curves to Business Strategies," Papers 1805.06339, arXiv.org.
    11. Hansen, J.P. & Narbel, P.A. & Aksnes, D.L., 2017. "Limits to growth in the renewable energy sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 769-774.
    12. repec:oup:renvpo:v:12:y:2018:i:1:p:133-153. is not listed on IDEAS
    13. repec:eee:energy:v:135:y:2017:i:c:p:913-929 is not listed on IDEAS
    14. repec:eee:phsmap:v:495:y:2018:i:c:p:59-66 is not listed on IDEAS
    15. Heinrich, Torsten, 2016. "The Narrow and the Broad Approach to Evolutionary Modeling in Economics," MPRA Paper 75797, University Library of Munich, Germany.
    16. Heinrich, Torsten, 2015. "Growth Cycles, Network Effects, and Intersectoral Dependence: An Agent-Based Model and Simulation Analysis," MPRA Paper 79575, University Library of Munich, Germany, revised 08 Jun 2017.
    17. repec:eee:tefoso:v:125:y:2017:i:c:p:116-124 is not listed on IDEAS
    18. Francois Lafond & Daniel Kim, 2017. "Long-run dynamics of the U.S. patent classification system," Papers 1703.02104, arXiv.org.
    19. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    20. repec:eee:rensus:v:81:y:2018:i:p2:p:1636-1642 is not listed on IDEAS
    21. Martin Kalthaus, 2017. "Identifying technological sub-trajectories in photovoltaic patents," Jena Economic Research Papers 2017-010, Friedrich-Schiller-University Jena.

    More about this item

    Keywords

    Forecasting; Technological progress; Moore's law; Solar energy;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:respol:v:45:y:2016:i:3:p:647-665. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/respol .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.