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Achieving Individual-Level Predictions from CBC Data: Comparing ICE and Hierarchical Bayes (1998)

This article is adapted from a presentation given at the 1998 A/R/T Forum by Joel Huber, Duke University. Huber compares three different methods for accounting for heterogeneity in CBC modeling: Hierarchical Bayes (HB), Latent Class, and ICE (Individual Choice Estimation). Three data sets are used to compare the merits of these approaches. HB and ICE are shown to outperform Latent Class in all aspects.

He concludes: "The important result is that although HB is more theoretically elegant than ICE, our experience suggests that both methods work equally well in practice." Latent Class is shown to provide estimates of aggregate shares nearly as accurate as HB and ICE, but "Latent Class, for its part, does a poor job of predicting individual choices unless its weights are allowed to be negative, as they are with ICE."

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