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 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.