Susan Athey, David Blei, Robert Donnelly, Francisco Ruiz, Tobias Schmidt
arXiv 22 Jan 2018 · Econometrics · publishedAEA Papers and Proceedings (2018) · 17 citations (OpenAlex)
arXiv:1801.07826 · PDF · DOI · OpenAlex · Extracted main text
This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each item has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants with similar characteristics, and we compare our predictions to actual outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location.
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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 | Francisco J. R. Ruiz, Susan Athey \ David M. Blei (2017) SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements self | 0.659 | 7 | 3 | 29% |
| 2 | Mengting Wan, Di Wang, Matt Goldman, Matt Taddy, Justin Rao, Jie Liu… (2017) Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs | 0.405 | 1 | 1 | 100% |
| 3 | Susan Athey, David M. Blei, Robert Donnelly \ Francisco J. R. Ruiz (2017) Counterfactual Inference for Consumer Choice Across Many Product Categories self | 0.405 | 1 | 1 | 100% |
| 4 | Terry Elrod (1988) Choice map: Inferring a product-market map from panel data | 0.405 | 1 | 1 | 100% |
| 5 | Michael P. Keane (2015) Panel Data Discrete Choice Models of Consumer Demand | 0.405 | 1 | 1 | 100% |
| 6 | C Neilson (2013) Targeted vouchers, competition among schools, and the academic achievement of poor students | 0.405 | 1 | 1 | 100% |
| 7 | David M. Blei, Alp Kucukelbir \ Jon D. McAuliffe (2017) Variational Inference: A Review for Statisticians self | 0.000 | 1 | 1 | 0% |
| 8 | Julius R. Blum (1954) Approximation methods which converge with probability one | 0.000 | 1 | 1 | 0% |
| 9 | L. Bottou, F. E. Curtis \ J. Nocedal (2016) Optimization Methods for Large-Scale Machine Learning | 0.000 | 1 | 1 | 0% |
| 10 | M. D. Hoffman, David M. Blei, C. Wang \ J. Paisley (2013) Stochastic Variational Inference self | 0.000 | 1 | 1 | 0% |
Showing the top 10 of 17 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Why Do We Need Travel Behavior Theory in the Age of AI? Multiple Goal Pursuit as an Illustrative Theory | 0.405 | 2 | 1 |
| 2 | Causal Inference for Spatial Treatments | 0.405 | 1 | 1 |
| 3 | Dynamic Consumer Demand at Large Scale | 0.405 | 1 | 1 |