Photovoltaic learning rate estimation: Issues and implications Ignacio Mauleón Department of Economics and Business Management, Universidad Rey Juan Carlos, Madrid, Spain article info Article history: Received 16 January 2015 Received in revised form 15 February 2016 Accepted 26 June 2016 Available online 21 July 2016 Keywords: Photovoltaic costs Estimation Dynamics Silicon prices Learning rates abstract This paper surveys the results of estimating learning rate (LR) equations for the photovoltaic (PV) in- dustry at the world level, and reports new results, placing emphasis on estimation issues, and other shortcomings surveyed recently. The results are reported in detail, one relevant finding being that the learning rate parameter might reach values substantially higher than those usually reported (18–20%). This result, however, does not necessarily translate to other energies. The relevance of selecting the estimation sample, dynamic specification, and omitted variables in simple standard specifications for the estimated learning rate is highlighted. A solution for the LR in dynamic non stationary models is pre- sented. The modeling of silicon prices is also discussed, and the concept of the total learning rate (TLR) is introduced. Probability confidence intervals for the main estimated learning rate parameters are ana- lyzed, and the time decomposition of PV module prices is discussed, highlighting the role of fossil energy prices. It is found that the total LR might reach values above 27% with a 95% probability. & 2016 Elsevier Ltd. All rights reserved. Contents 1. Introduction ........................................................................................................ 508 1.1. A review of significant allied research ............................................................................. 508 1.2. Technical progress, deployment, and costs reduction ................................................................. 508 1.3. Plan and objectives of the paper ................................................................................. 509 2. Methods ........................................................................................................... 509 2.1. Model and variables ........................................................................................... 509 2.2. Estimation methodology ........................................................................................ 510 2.3. The data ..................................................................................................... 510 3. Results and discussion................................................................................................ 511 3.1. Dynamics and sample choice .................................................................................... 512 3.2. Silicon and energy prices ....................................................................................... 514 3.3. LR confidence intervals and temporal break down of PV module prices .................................................. 518 3.4. Summary and discussion........................................................................................ 519 4. Summary .......................................................................................................... 520 5. Conclusions ........................................................................................................ 521 Acknowledgements ...................................................................................................... 521 Appendix .............................................................................................................. 521 A.1. Variables: definition and sources .................................................................................... 521 A.2. The learning rate as a ‘catch-all’ variable ............................................................................. 522 A.3. The learning rate in dynamic models ................................................................................ 522 A.4. The variance of the estimated learning rate ........................................................................... 522 References ............................................................................................................. 522 Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/rser Renewable and Sustainable Energy Reviews http://dx.doi.org/10.1016/j.rser.2016.06.070 1364-0321/& 2016 Elsevier Ltd. All rights reserved. E-mail address: ignacio.mauleon@urjc.es Renewable and Sustainable Energy Reviews 65 (2016) 507–524