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

A measure of hypothetical bias, or the divergence between stated and revealed preferences, based on conditional cross‐forecasting accuracy is suggested, based on out‐of‐sample prediction accuracy when estimates from stated preference data are used in place of those from actual choices, and vice vers...

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Bibliographic Details
Published in:The Australian journal of agricultural and resource economics 2011-07, Vol.55 (3), p.429-450
Main Authors: Volinskiy, Dmitriy, Adamowicz, Wiktor, Veeman, Michele
Format: Article
Language:English
Subjects:
Accuracy
Agricultural economics
Bias
Conditional logit
Econometrics
Economic forecasts
Forecasting techniques
Genetically modified foods
hypothetical bias
Mixed logit
Out-of-sample prediction
Preferences
Revealed preferences
Studies
Willingness to pay
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Description
Summary:A measure of hypothetical bias, or the divergence between stated and revealed preferences, based on conditional cross‐forecasting accuracy is suggested, based on out‐of‐sample 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.
ISSN:1364-985X
1467-8489
DOI:10.1111/j.1467-8489.2011.00552.x