2016Unpublished venueRequires access

Demand estimation with unobserved choice set heterogeneity

Rachel Griffith, Alessandro Iaria, Greg Crawford

Open publisher page 9 citations

Abstract

We present a method to estimate preferences in the presence of unobserved choice set heterogeneity. We build on the insights of Chamberlain's Fixed-Effect Logit and exploit information in observed purchase decisions in either panel or cross-section environments to construct sufficient sets of choices that lie within consumers' true but unobserved choice sets. This allows us to recover preference parameters without having to specify the process of choice set formation. We illustrate our ideas by estimating demand for chocolate bars on-the-go using individual-level data from the UK. Our results show that failing to account for unobserved choice set heterogeneity can lead to statistically and economically significant biases in the estimation of preference parameters.

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What this paper is about

We present a method to estimate preferences in the presence of unobserved choice set heterogeneity. We build on the insights of Chamberlain's Fixed-Effect Logit and exploit information in observed purchase decisions in either panel or cross-section environments to construct sufficient sets of choices that lie within consumers' true but unobserved choice sets. This allows us to recover preference parameters without having to specify the process of choice set formation. We illustrate our ideas by estimating demand for chocolate bars on-the-go using individual-level data from the UK. Our results show that failing to account for unobserved choice set heterogeneity can lead to statistically and economically significant biases in the estimation of preference parameters.

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Available abstract

We present a method to estimate preferences in the presence of unobserved choice set heterogeneity. We build on the insights of Chamberlain's Fixed-Effect Logit and exploit information in observed purchase decisions in either panel or cross-section environments to construct sufficient sets of choices that lie within consumers' true but unobserved choice sets. This allows us to recover preference parameters without having to specify the process of choice set formation. We illustrate our ideas by estimating demand for chocolate bars on-the-go using individual-level data from the UK. Our results show that failing to account for unobserved choice set heterogeneity can lead to statistically and economically significant biases in the estimation of preference parameters.

Key concepts: Choice set, Mixed logit, Revealed preference, Econometrics, Preference, Estimation, Set (abstract data type), Logit

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