Do Random Coefficients and Alternative Specific Constants Improve Policy Analysis? An Empirical Investigation of Model Fit and Prediction

Concerns about unobserved heterogeneity—either in preference or attribute space—have led environmental economists to turn increasingly to discrete choice models that incorporate random parameters and alternative specific constants. We use four recreation data sets and several empirical specification...

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Bibliographic Details
Published in:Environmental & resource economics 2019-05, Vol.73 (1), p.75-91
Main Authors: Klaiber, H. Allen, von Haefen, Roger H.
Format: Article
Language:English
Subjects:
Datasets
Decision making models
Discrete choice
Econometrics
Economic Policy
Economics
Economics and Finance
Economists
Empirical analysis
Environmental Economics
Environmental Law/Policy/Ecojustice
Environmental Management
Heterogeneity
Innovations
Policy analysis
Recreation
Recreational use
Travel
Wildlife management
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Summary:Concerns about unobserved heterogeneity—either in preference or attribute space—have led environmental economists to turn increasingly to discrete choice models that incorporate random parameters and alternative specific constants. We use four recreation data sets and several empirical specifications to show that although these modeling innovations often lead to substantial improvements in overall model fit, they also generate poor in-sample predictions relative to observed choices. Given the apparent tradeoff between fit and prediction, we then propose and empirically investigate a series of ‘second-best’ strategies that attempt to correct for the poor prediction we observe.
ISSN:0924-6460
1573-1502
DOI:10.1007/s10640-018-0250-z