How predictable is technological progress?
Author
Abstract
Suggested Citation
Download full text from publisher
Other versions of this item:
- Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
References listed on IDEAS
- 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.
- Clements, M.P. & Hendry, D.P., 1998. "Forecasting with Difference-Stationary and Trend-Stationary Models," The Warwick Economics Research Paper Series (TWERPS) 516, University of Warwick, Department of Economics.
- David Hendry & Michael P. Clements, 2000. "Forecasting with Difference-Stationary and Trend-Stationary Models," Economics Series Working Papers 5, University of Oxford, Department of Economics.
- Clements, Michael P. & Hendry, David F., 1998. "Forecasting With Difference-Stationary And Trend-Stationary Models," Economic Research Papers 268798, University of Warwick - Department of Economics.
- 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.
- Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
- 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.
- Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-135, April.
- repec:ucp:bknber:9780226304557 is not listed on IDEAS
- 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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Bosetti, Valentina & Catenacci, Michela & Fiorese, Giulia & Verdolini, Elena, 2012. "The Future Prospect of PV and CSP Solar Technologies: An Expert Elicitation Survey," Climate Change and Sustainable Development 121699, Fondazione Eni Enrico Mattei (FEEM).
- Valentina Bosetti & Michela Catenacci & Giulia Fiorese & Elena Verdolini, 2012. "The Future Prospect of PV and CSP Solar Technologies: An Expert Elicitation Survey," Working Papers 2012.01, Fondazione Eni Enrico Mattei.
- Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
- McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
- 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.
- Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
- 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.
- A.G. Wilson, 1969. "Forecasting 'Planning'," Urban Studies, Urban Studies Journal Limited, vol. 6(3), pages 347-367, November.
- 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.
- K. J. Arrow, 1971.
"The Economic Implications of Learning by Doing,"
Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149,
Palgrave Macmillan.
- Kenneth J. Arrow, 1962. "The Economic Implications of Learning by Doing," Review of Economic Studies, Oxford University Press, vol. 29(3), pages 155-173.
- Nordhaus, William D., 2007. "Two Centuries of Productivity Growth in Computing," The Journal of Economic History, Cambridge University Press, vol. 67(1), pages 128-159, March.
- Robert J. Gordon, 1990. "The Measurement of Durable Goods Prices," NBER Books, National Bureau of Economic Research, Inc, number gord90-1.
- 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.
- Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-144, April.
- 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.
- 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.
- 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.
- 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.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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.
- 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.
- Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
- Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
- Funk, Jeffrey L. & Magee, Christopher L., 2015. "Rapid improvements with no commercial production: How do the improvements occur?," Research Policy, Elsevier, vol. 44(3), pages 777-788.
- 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.
- Giovanni Dosi & Marco Grazzi & Nanditha Mathew, 2016. "The cost-quantity relations and the diverse patterns of "learning by doing": Evidence from India," LEM Papers Series 2016/26, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- 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.
- Singh, Anuraag & Triulzi, Giorgio & Magee, Christopher L., 2021. "Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description," Research Policy, Elsevier, vol. 50(9).
- Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
- Grafström, Jonas & Poudineh, Rahmat, 2021. "A review of problems associated with learning curves for solar and wind power technologies," Ratio Working Papers 347, The Ratio Institute.
- Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
- Wilson, Charlie, 2012. "Up-scaling, formative phases, and learning in the historical diffusion of energy technologies," Energy Policy, Elsevier, vol. 50(C), pages 81-94.
- Verdolini, Elena & Anadon, Laura Diaz & Lu, Jiaqi & Nemet, Gregory F., 2015.
"The effects of expert selection, elicitation design, and R&D assumptions on experts' estimates of the future costs of photovoltaics,"
Energy Policy, Elsevier, vol. 80(C), pages 233-243.
- Verdolini, Elena & Diaz Anadon, Laura & Lu, Jiaqi & Nemet, Gregory F., 2015. "The Effects of Expert Selection, Elicitation Design, and R&D Assumptions on Experts’ Estimates of the Future Costs of Photovoltaics," Energy: Resources and Markets 196997, Fondazione Eni Enrico Mattei (FEEM).
- Elena Verdolini & Laura Diaz Anadon & Jiaqi Lu & Gregory F. Nemet, 2015. "The Effects of Expert Selection, Elicitation Design, and R&D Assumptions on Experts’ Estimates of the Future Costs of Photovoltaics," Working Papers 2015.01, Fondazione Eni Enrico Mattei.
- Harashima, Taiji, 2009. "A Theory of Total Factor Productivity and the Convergence Hypothesis: Workers’ Innovations as an Essential Element," MPRA Paper 15508, University Library of Munich, Germany.
- Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
- Lafond, Francois & Greenwald, Diana & Farmer, J. Doyne, 2020.
"Can stimulating demand drive costs down? World War II as a natural experiment,"
MPRA Paper
100823, University Library of Munich, Germany.
- Lafond, François & Farmer, J. Doyne & Greenwald, Diana, 2020. "Can stimulating demand drive costs down? World War II as a natural experiment," INET Oxford Working Papers 2020-02, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
- Béla Nagy & J Doyne Farmer & Quan M Bui & Jessika E Trancik, 2013.
"Statistical Basis for Predicting Technological Progress,"
PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
- Bela Nagy & J. Doyne Farmer & Quan M. Bui & Jessika E. Trancik, 2012. "Statistical Basis for Predicting Technological Progress," Papers 1207.1463, arXiv.org.
- Triulzi, Giorgio & Alstott, Jeff & Magee, Christopher L., 2020. "Estimating technology performance improvement rates by mining patent data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
- Harashima, Taiji, 2011. "A Model of Total Factor Productivity Built on Hayek’s View of Knowledge: What Really Went Wrong with Socialist Planned Economies?," MPRA Paper 29107, University Library of Munich, Germany.
- Schauf, Magnus & Schwenen, Sebastian, 2021. "Mills of progress grind slowly? Estimating learning rates for onshore wind energy," Energy Economics, Elsevier, vol. 104(C).
- Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
More about this item
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 statisticsCorrections
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:arx:papers:1502.05274. 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: . General contact details of provider: http://arxiv.org/ .
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 bibliographic 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.