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Distributional Assumptions and the Estimation of Contingent Valuation Models

James McDonald (), Daniel B. Walton () and Bryan Chia ()
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Daniel B. Walton: Stanford University
Bryan Chia: Brigham Young University

Computational Economics, 2020, vol. 56, issue 2, No 7, 460 pages

Abstract: Abstract Contingent valuation methods are well-established techniques for measuring the value of goods and services not transacted in markets and have been applied in many different settings. Some of these applications include estimating the value of outdoor recreation, reducing risk, decreasing pollution, or reducing transportation time. The parameter estimates depend upon the survey design, the model specification, and the method of estimation. Distributional misspecification or heteroskedasticity can lead to inconsistent estimators. This paper introduces a partially adaptive estimation procedure, based on two families of flexible probability density functions [the generalized beta of the second kind (GB2) and the skewed generalized t (SGT)], to adjust for distributional misspecification and accommodate possible heteroskedasticity. Using a linear link function, these methods are applied to the problem of estimating the willingness to pay to protect Australia’s Kakadu Conservation Zone from mining. In this application, the assumption of homoskedasticity is not rejected for the GB2 family, but is rejected for the SGT. A Monte Carlo simulation confirms the importance of the homoskedasticity assumption as well as the impact of the bid design. For this example, many of the more flexible distributions are in fairly close agreement with some of their special cases. However, this application illustrates how flexible nested distributions can be used to accommodate diverse distributional characteristics, including possible heteroskedasticity.

Keywords: Willingness to pay; Partially adaptive estimation; Skewed generalized t; Generalized beta of the second kind; Heteroskedasticity (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10614-019-09930-x

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