Quantile Regression Analysis of Censored Data with Selection An Application to Willingness-to-Pay Data
Fiche du document
11 juillet 2022
- handle: 10670/1.1vfkqp
- hal: hal-03739861
info:eu-repo/semantics/OpenAccess
Mots-clés
Censored Quantile Regression Contingent Valuation Flood Monte Carlo Experiment Quantile Regression Selection Model Willingness to PaySujets proches
Methods of analysis Analysis and chemistry Analytical methods Chemical analysis Analysis methods Analysis and examinationCiter ce document
Victor Champonnois et al., « Quantile Regression Analysis of Censored Data with Selection An Application to Willingness-to-Pay Data », HAL-SHS : économie et finance, ID : 10670/1.1vfkqp
Métriques
Partage / Export
Résumé
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.