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Quantile Regression Analysis of Censored Data with Selection An Application to Willingness-to-Pay Data

Author

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  • Victor Champonnois

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Olivier Chanel

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Costin Protopopescu

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

Recurring statistical issues such as censoring, selection and heteroskedasticity often impact the analysis of observational data. We investigate the potential advantages of models based on quantile regression (QR) for addressing these issues, with a particular focus on willingness to pay-type data. We gather analytical arguments showing how QR can tackle these issues. We show by means of a Monte Carlo experiment how censored QR (CQR)-based methods perform compared to standard models. We empirically contrast four models on flood risk data. Our findings confirm that selection-censored models based on QR are useful for simultaneously tackling issues often present in observational data.

Suggested Citation

  • Victor Champonnois & Olivier Chanel & Costin Protopopescu, 2022. "Quantile Regression Analysis of Censored Data with Selection An Application to Willingness-to-Pay Data," Working Papers hal-03739861, HAL.
    • Handle: RePEc:hal:wpaper:hal-03739861
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-03739861
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    References listed on IDEAS

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    1. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    2. Cho, Seong-Hoon & Yen, Steven T. & Bowker, J.M. & Newman, David H., 2008. "Modeling Willingness to Pay for Land Conservation Easements: Treatment of Zero and Protest Bids and Application and Policy Implications," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(1), pages 267-285, April.
    3. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
    4. Broberg, Thomas & Brännlund, Runar, 2008. "An alternative interpretation of multiple bounded WTP data--Certainty dependent payment card intervals," Resource and Energy Economics, Elsevier, vol. 30(4), pages 555-567, December.
    5. Champonnois, Victor & Chanel, Olivier & Makhloufi, Khaled, 2018. "Reducing the anchoring bias in multiple question CV surveys," Journal of choice modelling, Elsevier, vol. 28(C), pages 1-9.
    6. Ian J. Bateman & Richard T. Carson & Brett Day & Michael Hanemann & Nick Hanley & Tannis Hett & Michael Jones-Lee & Graham Loomes, 2002. "Economic Valuation with Stated Preference Techniques," Books, Edward Elgar Publishing, number 2639.
    7. Begona Alvarez-Farizo, 1999. "Estimating the Benefits of Agri-environmental Policy: Econometric Issues in Open-ended Contingent Valuation Studies," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 42(1), pages 23-43.
    8. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    9. Ferrini, Silvia & Scarpa, Riccardo, 2007. "Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study," Journal of Environmental Economics and Management, Elsevier, vol. 53(3), pages 342-363, May.
    10. A. Colin Cameron & Pravin K. Trivedi, 2010. "Microeconometrics Using Stata, Revised Edition," Stata Press books, StataCorp LP, number musr, March.
    11. Amemiya, Takeshi, 1984. "Tobit models: A survey," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 3-61.
    12. Peter Boxall & Wiktor Adamowicz, 2002. "Understanding Heterogeneous Preferences in Random Utility Models: A Latent Class Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 23(4), pages 421-446, December.
    13. Ben-Akiva, Moshe & McFadden, Daniel & Train, Kenneth & Börsch-Supan, Axel, 2002. "Hybrid Choice Models: Progress and Challenges," Sonderforschungsbereich 504 Publications 02-29, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    14. Hong H. & Chernozhukov V., 2002. "Three-Step Censored Quantile Regression and Extramarital Affairs," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 872-882, September.
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    More about this item

    Keywords

    Censored Quantile Regression; Contingent Valuation; Flood; Monte Carlo Experiment; Quantile Regression; Selection Model; Willingness to Pay;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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