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Distribution-free estimation with interval-censored contingent valuation data: troubles with Turnbull?

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  • Brett Day

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    Abstract

    Contingent valuation (CV) surveys frequently employ elicitation procedures that return interval-censored data on respondents’ willingness to pay (WTP). Almost without exception, 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 starting and stopping criteria for the algorithm that guarantee convergence to the NPML estimates. With regards to (2) we present a variety of alternative estimators and demonstrate, through Monte Carlo simulations, their performance advantages over Turnbull’s algorithm. Copyright Springer Science+Business Media, Inc. 2007

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    File URL: http://hdl.handle.net/10.1007/s10640-006-9061-8
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    Bibliographic Info

    Article provided by European Association of Environmental and Resource Economists in its journal Environmental and Resource Economics.

    Volume (Year): 37 (2007)
    Issue (Month): 4 (August)
    Pages: 777-795

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    Handle: RePEc:kap:enreec:v:37:y:2007:i:4:p:777-795

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    Web page: http://www.springerlink.com/link.asp?id=100263

    Related research

    Keywords: Interval-censored data; Turnbull’s self-consistent algorithm; Nonparametric maximum likelihood; Contingent valuation;

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