Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data

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Abstract
In analyzing panel data, the issue of heterogeneity across households is an important consideration. If heterogeneity is present but is ignored in the analysis, it will result in biased and inconsistent estimates of the effects of marketing mix variables on brand choice. The authors propose the use of a random effects specification to account for heterogeneity in brand preferences across households in a logit framework. The model parameters are estimated by both parametric and semiparametric approaches. The authors also compare their results with those obtained from logit models in which observed past choice behavioir is used to capture such heterogeneity. The different models are estimated with the IRI saltine crackers dataset. A formal statistical test of the model specifications shows that the semiparametric specification is the most preferred in terms of the overall fit of the model to the data. In addition, that specification predicts best when the models are validated in a holdout sample of households.