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How Closely Do Hypothetical Surveys and Laboratory Experiments Predict Field Behavior?
Author(s) -
Chang Jae Bong,
Lusk Jayson L.,
Norwood F. Bailey
Publication year - 2009
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1111/j.1467-8276.2008.01242.x
Subject(s) - multinomial logistic regression , mixed logit , econometrics , ranking (information retrieval) , logit , discrete choice , product (mathematics) , statistics , field (mathematics) , preference , logistic regression , computer science , economics , mathematics , machine learning , geometry , pure mathematics
We compare the ability of three preference elicitation methods (hypothetical choices, nonhypothetical choices, and nonhypothetical rankings) and three discrete‐choice econometric models (the multinomial logit [MNL], the independent availability logit [IAL], and the random parameter logit [RPL]) to predict actual retail shopping behavior in three different product categories (ground beef, wheat flour, and dishwashing liquid). Overall, we find a high level of external validity. Our specific results suggest that the nonhypothetical elicitation approaches, especially the nonhypothetical ranking method, outperformed the hypothetical choice experiment in predicting retail sales. We also find that the RPL can have superior predictive performance, but that the MNL predicts equally well in some circumstances.