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Embracing Model Uncertainty: Strategies for Response Pooling and Model Averaging

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  • David Layton

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  • S. Lee
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    Abstract

    Econometricians modeling Stated Preference (SP) data, and most other types of data, are confronted with the uncomfortable reality that our knowledge of the “true” model is limited, with only certain variables suggested by the application at hand and general classes of functional forms and error structures suggested by the literature. Accepting our limited knowledge, we pursue strategies for analyzing SP data that are more robust to uncertainties in our knowledge of the true model. These include non-parametric and parametric likelihood-based tests of pooling responses from different elicitation formats, and a frequentist-based model averaging approach for estimating willingness to pay functions. We argue that these strategies lead to increased econometric integrity and empower the ultimate users of models, such as policy decision-makers and even juries, to better assess the robustness of the results. We apply these approaches to an SP survey of saltwater angling in Alaska which utilized split-sample rankings and ratings elicitation methods. While an important goal of our paper is to develop practicable modeling strategies that will ultimately lead to more robust conclusions and more confidence by the users of SP results, an equally important goal is to engender a critical discussion of how we can make the analysis of SP data more robust. Copyright Springer 2006

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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): 34 (2006)
    Issue (Month): 1 (05)
    Pages: 51-85

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    Handle: RePEc:kap:enreec:v:34:y:2006:i:1:p:51-85

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

    Related research

    Keywords: model averaging; rankings; ratings; stated preference; C35; Q26; Q51;

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    Cited by:
    1. Mehdi Farsi, 2008. "Risk-Aversion and Willingness to Pay for Energy Efficient Systems in Rental Apartments," CEPE Working paper series 08-55, CEPE Center for Energy Policy and Economics, ETH Zurich.
    2. Vanina Forget, 2012. "Doing well and doing good: a multi-dimensional puzzle," Working Papers hal-00672037, HAL.
    3. Babatunde Abidoye & Joseph Herriges, 2012. "Model Uncertainty in Characterizing Recreation Demand," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 53(2), pages 251-277, October.
    4. C. Haynie, Alan & F. Layton, David, 2010. "An expected profit model for monetizing fishing location choices," Journal of Environmental Economics and Management, Elsevier, vol. 59(2), pages 165-176, March.
    5. Hoyos, David, 2010. "The state of the art of environmental valuation with discrete choice experiments," Ecological Economics, Elsevier, vol. 69(8), pages 1595-1603, June.
    6. Godwin Kofi Vondolia & Håkan Eggert & Ståle Navrud & Jesper Stage, 2014. "What do respondents bring to contingent valuation? A comparison of monetary and labour payment vehicles," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 3(3), pages 253-267, November.
    7. Jordan Louviere, 2006. "What You Don’t Know Might Hurt You: Some Unresolved Issues in the Design and Analysis of Discrete Choice Experiments," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 34(1), pages 173-188, 05.
    8. Jorge Araña & Carmelo León, 2007. "Repeated Dichotomous Choice Formats for Elicitation of Willingness to Pay: Simultaneous Estimation and Anchoring Effect," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 36(4), pages 475-497, April.
    9. Balcombe, Kelvin & Chalak, Ali & Fraser, Iain, 2009. "Model selection for the mixed logit with Bayesian estimation," Journal of Environmental Economics and Management, Elsevier, vol. 57(2), pages 226-237, March.

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