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Predicting versus testing: a conditional cross‐forecasting accuracy measure for hypothetical bias

Author

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  • Dmitriy Volinskiy
  • Wiktor Adamowicz
  • Michele Veeman

Abstract

A measure of hypothetical bias, or the divergence between stated and revealed preferences, based on conditional cross-forecasting accuracy is suggested, based on out-ofsample prediction accuracy when estimates from stated preference data are used in place of those from actual choices, and vice versa. We describe an application of this measure to assess hypothetical bias in the context of an inquiry into people’s willingness to pay to avoid canola oil produced from genetically modified plants. The analysis suggests the presence of groupwise hypothetical bias in these choice data.
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Suggested Citation

  • Dmitriy Volinskiy & Wiktor Adamowicz & Michele Veeman, 2011. "Predicting versus testing: a conditional cross‐forecasting accuracy measure for hypothetical bias," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(3), pages 429-450, July.
    • Handle: RePEc:bla:ajarec:v:55:y:2011:i:3:p:429-450
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    Cited by:

    1. Craig D. Broadbent, 2012. "Hypothetical Bias, Consequentiality and Choice Experiments," Economics Bulletin, AccessEcon, vol. 32(3), pages 2490-2499.
    2. Craig D. Broadbent, 2014. "Evaluating mitigation and calibration techniques for hypothetical bias in choice experiments," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 57(12), pages 1831-1848, December.

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