Can pecuniary and environmental incentives via SMS messaging make households adjust their electricity demand to a fluctuating production?
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DOI: 10.1016/j.eneco.2019.01.023
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- Kevin D. Hoover & Stephen J. Perez, 1999.
"Data mining reconsidered: encompassing and the general-to-specific approach to specification search,"
Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
- Kevin D. Hoover & Stephen J. Perez, "undated". "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Department of Economics 97-27, California Davis - Department of Economics.
- Kevin Hoover & Stephen J. Perez, 2003. "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Working Papers 200, University of California, Davis, Department of Economics.
- Maria Gleerup & Anders Larsen & Soren Leth-Petersen & Mikael Togeby, 2010. "The Effect of Feedback by Text Message (SMS) and Email on Household Electricity Consumption: Experimental Evidence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 113-132.
- Caves, Douglas W. & Christensen, Laurits R. & Herriges, Joseph A., 1984.
"Consistency of residential customer response in time-of-use electricity pricing experiments,"
Journal of Econometrics, Elsevier, vol. 26(1-2), pages 179-203.
- Caves, Douglas W. & Christensen, L. R. & Herriges, Joseph A., 1984. "The Consistency of the Residential Customer Response in Time-Of-Use Electricity Pricing Experiments," Staff General Research Papers Archive 10798, Iowa State University, Department of Economics.
- Newsham, Guy R. & Bowker, Brent G., 2010. "The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review," Energy Policy, Elsevier, vol. 38(7), pages 3289-3296, July.
- Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, vol. 3(2), pages 1-25, April.
- David F. Hendry & Hans-Martin Krolzig, 2005.
"The Properties of Automatic "GETS" Modelling,"
Economic Journal, Royal Economic Society, vol. 115(502), pages 32-61, March.
- David Hendry & Hans-Martin Krolzig, 2003. "The Properties of Automatic Gets Modelling," Economics Papers 2003-W14, Economics Group, Nuffield College, University of Oxford.
- Hendry, David F & Hans-Martin Krolzig, 2003. "The Properties of Automatic Gets Modelling," Royal Economic Society Annual Conference 2003 105, Royal Economic Society.
- Faruqui, Ahmad & Sergici, Sanem & Sharif, Ahmed, 2010. "The impact of informational feedback on energy consumption—A survey of the experimental evidence," Energy, Elsevier, vol. 35(4), pages 1598-1608.
- James Reade & Genaro Sucarrat, 2016. "General-to-Specific (GETS) Modelling And Indicator Saturation With The R Package Gets," Economics Series Working Papers 794, University of Oxford, Department of Economics.
- Alexander Martin Tureczek & Per Sieverts Nielsen, 2017. "Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data," Energies, MDPI, vol. 10(5), pages 1-19, April.
- Almas Heshmati, 2014. "Demand, Customer Base-Line And Demand Response In The Electricity Market: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 28(5), pages 862-888, December.
- Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9-10), pages 1082-1095, October.
- Faruqui, Ahmad & George, Stephen, 2005. "Quantifying Customer Response to Dynamic Pricing," The Electricity Journal, Elsevier, vol. 18(4), pages 53-63, May.
- Faruqui, Ahmad & George, Stephen S., 2002. "The Value of Dynamic Pricing in Mass Markets," The Electricity Journal, Elsevier, vol. 15(6), pages 45-55, July.
- Carlos Santos & David Hendry & Soren Johansen, 2008.
"Automatic selection of indicators in a fully saturated regression,"
Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
- David Hendry & Søren Johansen & Carlos Santos, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 337-339, April.
- Faruqui, Ahmad & Malko, J.Robert, 1983. "The residential demand for electricity by time-of-use: A survey of twelve experiments with peak load pricing," Energy, Elsevier, vol. 8(10), pages 781-795.
- Delmas, Magali A. & Fischlein, Miriam & Asensio, Omar I., 2013. "Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012," Energy Policy, Elsevier, vol. 61(C), pages 729-739.
- David F. Hendry, 2009. "The Methodology of Empirical Econometric Modeling: Applied Econometrics Through the Looking-Glass," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 1, pages 3-67, Palgrave Macmillan.
- Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9), pages 1082-1095.
- Ahmad Faruqui & Sanem Sergici, 2010. "Household response to dynamic pricing of electricity: a survey of 15 experiments," Journal of Regulatory Economics, Springer, vol. 38(2), pages 193-225, October.
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Cited by:
- Kapeller, Rudolf & Cohen, Jed J. & Kollmann, Andrea & Reichl, Johannes, 2023. "Incentivizing residential electricity consumers to increase demand during periods of high local solar generation," Energy Economics, Elsevier, vol. 127(PA).
- Yang, So Young & Woo, JongRoul & Lee, Wonjong, 2024. "Assessing optimized time-of-use pricing for electric vehicle charging in deep vehicle-grid integration system," Energy Economics, Elsevier, vol. 138(C).
- Kacperski, Celina & Ulloa, Roberto & Klingert, Sonja & Kirpes, Benedikt & Kutzner, Florian, 2022. "Impact of incentives for greener battery electric vehicle charging – A field experiment," Energy Policy, Elsevier, vol. 161(C).
- Albertsen, Lau H. & Andersen, Mads & Boscán, Luis R. & Santos, Athila Q., 2020. "Implementing dynamic electricity taxation in Denmark," Energy Policy, Elsevier, vol. 143(C).
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More about this item
Keywords
Household-level electricity demand; General-to-Specific automatic model selection; SMS messaging; Field experimental data;All these keywords.
JEL classification:
- C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
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