IDEAS home Printed from https://ideas.repec.org/a/eee/quaeco/v71y2019icp223-227.html
   My bibliography  Save this article

A focused information criterion for quantile regression: Evidence for the rebound effect

Author

Listed:
  • Behl, Peter
  • Dette, Holger
  • Frondel, Manuel
  • Vance, Colin

Abstract

In contrast to conventional model selection criteria, the Focused Information Criterion (FIC) allows for the purpose-specific choice of model specifications. This accommodates the idea that one kind of model might be highly appropriate for inferences on a particular focus parameter, but not for another. Using the FIC concept that is developed by Behl, Claeskens, and Dette (2014) for quantile regression analysis, and the estimation of the rebound effect in individual mobility behavior as an example, this paper provides for an empirical application of the FIC in the selection of quantile regression models.

Suggested Citation

  • Behl, Peter & Dette, Holger & Frondel, Manuel & Vance, Colin, 2019. "A focused information criterion for quantile regression: Evidence for the rebound effect," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 223-227.
    • Handle: RePEc:eee:quaeco:v:71:y:2019:i:c:p:223-227
    DOI: 10.1016/j.qref.2018.08.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062976917300261
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.qref.2018.08.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2012. "Choice is suffering: A Focused Information Criterion for model selection," Economic Modelling, Elsevier, vol. 29(3), pages 817-822.
    2. Manuel Frondel and Colin Vance, 2013. "Re-Identifying the Rebound: What About Asymmetry?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    3. Sorrell, Steve & Dimitropoulos, John, 2008. "The rebound effect: Microeconomic definitions, limitations and extensions," Ecological Economics, Elsevier, vol. 65(3), pages 636-649, April.
    4. Frondel, Manuel & Ritter, Nolan & Vance, Colin, 2012. "Heterogeneity in the rebound effect: Further evidence for Germany," Energy Economics, Elsevier, vol. 34(2), pages 461-467.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. Christian T. Brownlees & Giampiero M. Gallo, 2008. "On Variable Selection for Volatility Forecasting: The Role of Focused Selection Criteria," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 513-539, Fall.
    7. Manuel Frondel & Jorg Peters & Colin Vance, 2008. "Identifying the Rebound: Evidence from a German Household Panel," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 145-164.
    8. Claeskens, Gerda & Hjort, Nils Lid, 2008. "Minimizing Average Risk In Regression Models," Econometric Theory, Cambridge University Press, vol. 24(2), pages 493-527, April.
    9. Holger Dette & Mark Podolskij & Mathias Vetter, 2006. "Estimation of Integrated Volatility in Continuous‐Time Financial Models with Applications to Goodness‐of‐Fit Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 259-278, June.
    10. Sorrell, Steve & Dimitropoulos, John & Sommerville, Matt, 2009. "Empirical estimates of the direct rebound effect: A review," Energy Policy, Elsevier, vol. 37(4), pages 1356-1371, April.
    11. Dette, Holger & Podolskij, Mark, 2008. "Testing the parametric form of the volatility in continuous time diffusion models--a stochastic process approach," Journal of Econometrics, Elsevier, vol. 143(1), pages 56-73, March.
    12. Claeskens G. & Hjort N.L., 2003. "The Focused Information Criterion," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 900-916, January.
    13. Berkhout, Peter H. G. & Muskens, Jos C. & W. Velthuijsen, Jan, 2000. "Defining the rebound effect," Energy Policy, Elsevier, vol. 28(6-7), pages 425-432, June.
    14. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2013. "Energy substitution: When model selection depends on the focus," Energy Economics, Elsevier, vol. 39(C), pages 233-238.
    Full references (including those not matched with items on IDEAS)

    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.
    1. repec:hal:gemwpa:hal-00991732 is not listed on IDEAS
    2. Manuel Frondel & Colin Vance, 2018. "Drivers’ response to fuel taxes and efficiency standards: evidence from Germany," Transportation, Springer, vol. 45(3), pages 989-1001, May.
    3. Schleich, Joachim & Mills, Bradford & Dütschke, Elisabeth, 2014. "A brighter future? Quantifying the rebound effect in energy efficient lighting," Energy Policy, Elsevier, vol. 72(C), pages 35-42.
    4. Vance, Colin & Frondel, Manuel, 2015. "From fuel taxation to efficiency standards: A wrong turn in European climate protection?," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113171, Verein für Socialpolitik / German Economic Association.
    5. Frondel, Manuel & Vance, Colin, 2013. "Re-Identifying the Rebound: What About Asymmetry?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 34(4), pages 42-54.
    6. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2012. "Choice is suffering: A Focused Information Criterion for model selection," Economic Modelling, Elsevier, vol. 29(3), pages 817-822.
    7. Behl, Peter & Dette, Holger & Frondel, Manuel & Tauchmann, Harald, 2013. "Energy substitution: When model selection depends on the focus," Energy Economics, Elsevier, vol. 39(C), pages 233-238.
    8. Karen Turner, 2013. ""Rebound" Effects from Increased Energy Efficiency: A Time to Pause and Reflect," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    9. Frondel, Manuel & Ritter, Nolan & Vance, Colin, 2012. "Heterogeneity in the rebound effect: Further evidence for Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 34(2), pages 461-467.
    10. Hediger, Cécile, 2023. "The more kilometers, the merrier? The rebound effect and its welfare implications in private mobility," Energy Policy, Elsevier, vol. 180(C).
    11. Manuel Frondel & Colin Vance, 2013. "Fuel Taxes versus Efficiency Standards – An Instrumental Variable Approach," Ruhr Economic Papers 0445, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    12. Tilov, Ivan & Weber, Sylvain, 2023. "Heterogeneity in price elasticity of vehicle kilometers traveled: Evidence from micro-level panel data," Energy Economics, Elsevier, vol. 127(PA).
    13. Stela Rubínová, 2011. "Reakce poptávky domácností po energii na zvyšování energetické účinnosti: teorie a její důsledky pro konstrukci empiricky ověřitelných modelů [Reaction of Household Energy Demand to Improvements in," Politická ekonomie, Prague University of Economics and Business, vol. 2011(3), pages 359-378.
    14. Thomas, Brinda A. & Azevedo, Inês L., 2013. "Estimating direct and indirect rebound effects for U.S. households with input–output analysis Part 1: Theoretical framework," Ecological Economics, Elsevier, vol. 86(C), pages 199-210.
    15. Sylvain Weber & Mehdi Farsi, 2014. "Travel distance, fuel efficiency, and vehicle weight: An estimation of the rebound effect using individual data in Switzerland," IRENE Working Papers 14-03, IRENE Institute of Economic Research.
    16. repec:zbw:rwirep:0445 is not listed on IDEAS
    17. Dimitropoulos, Alexandros & Oueslati, Walid & Sintek, Christina, 2018. "The rebound effect in road transport: A meta-analysis of empirical studies," Energy Economics, Elsevier, vol. 75(C), pages 163-179.
    18. Belaïd, Fateh & Youssef, Adel Ben & Lazaric, Nathalie, 2020. "Scrutinizing the direct rebound effect for French households using quantile regression and data from an original survey," Ecological Economics, Elsevier, vol. 176(C).
    19. Yu, Xuewei & Moreno-Cruz, Juan & Crittenden, John C., 2015. "Regional energy rebound effect: The impact of economy-wide and sector level energy efficiency improvement in Georgia, USA," Energy Policy, Elsevier, vol. 87(C), pages 250-259.
    20. Ghoddusi, Hamed & Roy, Mandira, 2017. "Supply elasticity matters for the rebound effect and its impact on policy comparisons," Energy Economics, Elsevier, vol. 67(C), pages 111-120.
    21. Wang, Zhaohua & Lu, Milin & Wang, Jian-Cai, 2014. "Direct rebound effect on urban residential electricity use: An empirical study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 124-132.
    22. Rishan Adha & Cheng-Yih Hong, 2021. "How Large the Direct Rebound Effect for Residential Electricity Consumption When the Artificial Neural Network Takes on the Role? A Taiwan Case Study of Household Electricity Consumption," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 354-364.

    More about this item

    Keywords

    Information criteria; Fuel efficiency; Price elasticities;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • D2 - Microeconomics - - Production and Organizations

    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:quaeco:v:71:y:2019:i:c:p:223-227. See general information about how to correct material in RePEc.

    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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620167 .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.