arXiv 6 Oct 2026 · Econometrics
arXiv:2610.08409 · PDF · Extracted main text
This work studies how Bayesian machine learning methods can be used for large-scale demand estimation with many product categories. I compare two model classes, a latent factorization model and a mixed logit model and two Bayesian estimation approaches, Markov Chain Monte Carlo (MCMC) and Variational Inference (VI). The analysis combines a simulation study with an application to supermarket scanner data. The results show that the latent factorization model benefits from information across categories and improves its predictive performance as the dimensionality of the choice environment increases, whereas the mixed logit model does not exhibit the same pattern. MCMC delivers the highest predictive accuracy but is computationally intensive. VI achieves slightly lower predictive performance while substantially reducing runtime. In the empirical application, VI also outperforms the mixed logit benchmark. These findings highlight a trade-off between accuracy and computational feasibility in multi-category demand estimation.
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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 | Donnelly, R. and Ruiz, F. J. R. and Blei, D. and Athey, S (2021) Counterfactual inference for consumer choice across many product categories | 1.000 | 8 | 4 | 100% |
| 2 | Wan, M. and Wang, D. and Goldman, M. and Taddy, M. and Rao, J. and L… (2017) Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs | 0.928 | 4 | 3 | 100% |
| 3 | Gelman, A. and Calin, J. and Stern, H. and Dunson, D. and Vehtari, A… (2014) Bayesian Data Analysis | 0.811 | 4 | 2 | 100% |
| 4 | Athey, S. and Blei, D. and Donnelly, R. and Ruiz, F. and Schmidt, T (2018) Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data | 0.737 | 3 | 2 | 100% |
| 5 | Athey, S. and Imbens, G. W (2019) Machine Learning Methods That Economists Should Know About | 0.737 | 3 | 2 | 100% |
| 6 | Ruiz, F. J. R. and Athey, S. and Blei, D. M (2020) SHOPPER: a probabilistic model of consumer choice with substitutes and complements | 0.737 | 3 | 2 | 100% |
| 7 | Blei, D. M. and Kucukelbir, A. and McAuliffe, J. D (2017) Variational Inference: A Review for Statisticians | 0.693 | 5 | 1 | 100% |
| 8 | Kucukelbir, A. and Tran, D. and Ranganath, R. and Gelman, A. and Ble… (2017) Automatic differentiation variational inference | 0.693 | 5 | 1 | 100% |
| 9 | Betancourt, M (2018) A Conceptual Introduction to Hamiltonian Monte Carlo | 0.644 | 4 | 1 | 100% |
| 10 | Neal, R. M (2011) MCMC using Hmiltonian dynamics | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 65 scored citations.