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

Victor Champonnois (1) , Olivier Chanel (1) , Costin Protopopescu (1)

1 AMSE - Aix-Marseille Sciences Economiques

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.

Keywords: Censored Quantile Regression; Contingent Valuation; Flood; Monte Carlo Experiment; Quantile Regression; Selection Model; Willingness to Pay

Domains: Humanities and Social Sciences; Economics and Finance

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