A Better Approach to Resolving Variable Selection Uncertainty in Meta Analysis for Benefits Transfer
Because original high-quality non-market valuation studies can be expensive, perhaps prohibitively so, benefits transfer (BT) approaches are often used for valuing, e.g., the outputs of multifunctional agriculture. Here we focus on the use of BT functions, a preferred method, and address an under-appreciated problem – variable selection uncertainty – and demonstrate a conceptually superior method of resolving it. We show that the standard method of value-function BT, using the full estimated model, may generate BT values that are too sensitive to insignificant variables, whereas models reduced by backward elimination of insignificant variables pay no attention to insignificant variables that may in fact have some influence on values. Rather than searching for the best single model for BT, Bayesian model averaging (BMA) is attentive to all of the variables that are a priori relevant, but uses posterior model probabilities to give systematically lower weight to less significant variables. We estimate a full value model for wetlands in the US, and then calculate BT values from the full model, a reduced model, and by BMA. Variable selection uncertainty is exemplified by regional variables for wetland location. Predicted values from the full model are quite sensitive to region; reduced models pay no attention to regional variables; and the BMA predictions are attentive to region but give it relatively low weight. However, the suite of insignificant RHS variables, taken together, have non-trivial influence on BT values. BMA predicted values, like values from reduced models, have much narrower confidence intervals than values calculated from the full model.
|Date of creation:||2011|
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- Klaus Moeltner & Richard Woodward, 2009.
"Meta-Functional Benefit Transfer for Wetland Valuation: Making the Most of Small Samples,"
Environmental & Resource Economics,
Springer;European Association of Environmental and Resource Economists, vol. 42(1), pages 89-108, January.
- Klaus Moeltner & Richard T. Woodward, 2007. "Meta-Functional Benefit Transfer for Wetland Valuation: Making the Most of Small Samples," Working Papers 07-012, University of Nevada, Reno, Department of Economics;University of Nevada, Reno , Department of Resource Economics.
- Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
- Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics 9903003, EconWPA, revised 06 Oct 2001.
- Carmen Fernandez & Eduardo Ley & Mark Steel, 2001. "Model uncertainty in cross-country growth regressions," Econometrics 0110002, EconWPA.
- Woodward, Richard T. & Wui, Yong-Suhk, 2001. "The economic value of wetland services: a meta-analysis," Ecological Economics, Elsevier, vol. 37(2), pages 257-270, May.
- Moeltner, Klaus & Boyle, Kevin J. & Paterson, Robert W., 2007. "Meta-analysis and benefit transfer for resource valuation-addressing classical challenges with Bayesian modeling," Journal of Environmental Economics and Management, Elsevier, vol. 53(2), pages 250-269, March.
- Luke Brander & Raymond Florax & Jan Vermaat, 2006. "The Empirics of Wetland Valuation: A Comprehensive Summary and a Meta-Analysis of the Literature," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 33(2), pages 223-250, 02.
- Gary Koop & Simon Potter, 2004. "Forecasting in dynamic factor models using Bayesian model averaging," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 550-565, December. Full references (including those not matched with items on IDEAS)
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