Victor H. Aguiar, Nail Kashaev
arXiv 9 Jul 2019 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 4 citations (OpenAlex)
arXiv:1907.04853 · PDF · DOI · OpenAlex · Extracted main text
We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved choice sets. We recover the joint distribution of choice sets and preferences from a panel dataset on choices. We assume that either the latent choice sets are sparse or that the panel is sufficiently long. Sparsity requires the number of possible choice sets to be relatively small. It is satisfied, for instance, when the choice sets are nested, or when they form a partition. Our estimation procedure is computationally fast and uses mixed-integer optimization to recover the sparse support of choice sets. Analyzing the ready-to-eat cereal industry using a household scanner dataset, we find that ignoring the unobservability of choice sets can lead to biased estimates of preferences due to significant latent heterogeneity in choice sets.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Nevo (2001) Measuring market power in the ready-to-eat cereal industry | 0.974 | 13 | 4 | 92% |
| 2 | Hu (2008) Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution | 0.950 | 7 | 3 | 86% |
| 3 | Berry, Levinsohn and Pakes (1995) Automobile prices in market equilibrium | 0.928 | 4 | 3 | 100% |
| 4 | Lu (2014) A Moment Inequality Approach to Estimating Multinomial Choice Models with Unobserved Consideration Sets | 0.928 | 4 | 3 | 100% |
| 5 | Hu, McAdams and Shum (2013) Identification of first-price auctions with non-separable unobserved heterogeneity | 0.909 | 8 | 4 | 75% |
| 6 | Crawford, Griffith and Iaria (2021) A survey of preference estimation with unobserved choice set heterogeneity | 0.811 | 4 | 2 | 100% |
| 7 | Nevo (2000) A practitioner's guide to estimation of random-coefficients logit models of demand | 0.811 | 4 | 2 | 100% |
| 8 | Bertsimas, King, Mazumder et al (2016) Best subset selection via a modern optimization lens | 0.644 | 2 | 2 | 100% |
| 9 | Goeree (2008) Limited information and advertising in the US personal computer industry | 0.644 | 2 | 2 | 100% |
| 10 | Kasahara and Shimotsu (2009) Nonparametric identification of finite mixture models of dynamic discrete choices | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Scalable Estimation of Multinomial Response Models with Random Consideration Sets | 0.928 | 4 | 3 |
| 2 | Demand Analysis under Price Rigidity and Endogenous Assortment: An Application to China's Tobacco Industry | 0.644 | 2 | 2 |
| 3 | Peer Effects in Random Consideration Sets | 0.405 | 1 | 1 |