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

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.
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Preprints, Working Papers, ...
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https://hal-amu.archives-ouvertes.fr/hal-03739861
Contributor : Elisabeth Lhuillier Connect in order to contact the contributor
Submitted on : Thursday, July 28, 2022 - 1:45:54 PM
Last modification on : Friday, August 26, 2022 - 3:50:00 AM
Long-term archiving on: : Saturday, October 29, 2022 - 6:48:19 PM

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WP 2022 - Nr 14.pdf
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  • HAL Id : hal-03739861, version 1

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Victor Champonnois, Olivier Chanel, Costin Protopopescu. Quantile Regression Analysis of Censored Data with Selection An Application to Willingness-to-Pay Data. 2022. ⟨hal-03739861⟩

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