Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/80251 
Autor:innen: 
Erscheinungsjahr: 
2005
Schriftenreihe/Nr.: 
CSERGE Working Paper EDM No. 05-07
Verlag: 
University of East Anglia, The Centre for Social and Economic Research on the Global Environment (CSERGE), Norwich
Zusammenfassung: 
Contingent valuation (CV) surveys frequently employ elicitation procedures that return interval-censored data on respondents' willingness to pay (WTP). Almost exclusively, CV practitioners have applied Turnbull's self-consistent algorithm to such data in order to obtain nonparametric maximum likelihood (NPML) estimates of the WTP distribution. This paper documents two failings of Turnbull's algorithm; (1) that it may not converge to NPML estimates and (2) that it may be very slow to converge. With regards to (1) we propose new starting and stopping criteria for the algorithm that guarantee convergence to the NPML estimates. With regards to (2) we present a smorgasbord of alternative NPML estimators and demonstrate, through Monte Carlo simulations, their performance advantages over Turnbull's algorithm.
Schlagwörter: 
Contingent valuation
Interval-censored data
nonparametric maximum likelihood
Turnbull's self-consistent algorithm
Dokumentart: 
Working Paper

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