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

Victor Champonnois () and Olivier Chanel

No 2016.12, Working Papers from FAERE - French Association of Environmental and Resource Economists

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.

Keywords: contingent valuation; quantile regression; censored quantile regression; Monte Carlo simulations; flood (search for similar items in EconPapers)
JEL-codes: C15 C9 C21 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2016-04
New Economics Papers: this item is included in nep-ecm and nep-pr~
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http://faere.fr/pub/WorkingPapers/Champonnois_Chanel_FAERE_WP2016.12.pdf First version, 2016 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:fae:wpaper:2016.12

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