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Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study

Abstract
Good practice in experimental design is essential for choice experiments used in nonmarket valuation. We review the practice of experimental design for choice experiments in environmental economics and we compare it with advances in experimental design. We then evaluate the statistical efficiency of four different designs by means of Monte Carlo experiments. Correct and incorrect specifications are investigated with gradually more precise information on the true parameter values. The data generating process (DGP) is based on estimates from data of a real study. Results indicate that D-efficient designs are promising, especially when based on Bayesian algorithms with informative prior. However, if good quality a priori information is lacking, and if there is strong uncertainty about the real DGP—conditions which are quite common in environmental valuation—then practitioners might be better off with shifted designs built from conventional fractional factorial designs for linear models.
Type
Journal Article
Type of thesis
Series
Citation
Ferrini, S. & Scarpa, R. (2007). Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study. Journal of Environmental Economics and Management, 53(3), 342-363.
Date
2007
Publisher
Academic Press
Degree
Supervisors
Rights