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How useful are (Censored) Quantile Regressions for Contingent Valuation?

Author

Listed:
  • Victor Champonnois

    (AMSE-GREQAM)

  • Olivier Chanel

    (AMSE-GREQAM)

Abstract

We investigate the interest of quantile regression (QR) and censored quantile regression (CQR) to deal with issues from contingent valuation (CV) data. Indeed, although (C)QR estimators have many properties of interest for CV, the literature is scarce and restricted to six studies only. We proceed in three steps. First, we provide analytical arguments showing how (C)QR can tackle many econometric issues associated with CV data. Second, we show by means of Monte Carlo simulations, how (C)QR performs w.r.t. standard (linear and censored) models. Finally, we apply and compare these four models on a French CV survey dealing with flood risk. Although our findings show the usefulness of QR for analyzing CV data, findings are mixed on the improvements from CQR estimates with respect to QR estimates.

Suggested Citation

  • Victor Champonnois & Olivier Chanel, 2016. "How useful are (Censored) Quantile Regressions for Contingent Valuation?," Working Papers 2016.12, FAERE - French Association of Environmental and Resource Economists.
    • Handle: RePEc:fae:wpaper:2016.12
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    File URL: http://faere.fr/pub/WorkingPapers/Champonnois_Chanel_FAERE_WP2016.12.pdf
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    References listed on IDEAS

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    2. Victor Champonnois & Katrin Erdlenbruch, 2020. "Willingness of households to reduce flood risk in southern France," Working Papers hal-02586069, HAL.

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    More about this item

    Keywords

    contingent valuation; quantile regression; censored quantile regression; Monte Carlo simulations; flood;
    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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