Yanhao 'Max' Wei, Zhenling Jiang
arXiv 1 May 2025 · Econometrics
arXiv:2505.00526 · PDF · Extracted main text
We explore pretraining estimators for structural econometric models. The estimator is "pretrained" in the sense that the bulk of the computational cost and researcher effort occur during the construction of the estimator. Subsequent applications of the estimator to different datasets require little computational cost or researcher effort. The estimation leverages a neural net to recognize the structural model's parameter from data patterns. As an initial trial, this paper builds a pretrained estimator for a sequential search model that is known to be difficult to estimate. We evaluate the pretrained estimator on 12 real datasets. The estimation takes seconds to run and shows high accuracy. We provide the estimator at pnnehome.github.io. More generally, pretrained, off-the-shelf estimators can make structural models more accessible to researchers and practitioners.
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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 | Wei and Jiang (2024) Estimating Parameters of Structural Models using Neural Networks | 0.909 | 12 | 3 | 75% |
| 2 | Ursu, Seiler and Honka (2023) The Sequential Search Model: A Framework for Empirical Research | 0.737 | 3 | 2 | 100% |
| 3 | Jiang, Chan, Che and Wang (2021) Consumer Search and Purchase: An Empirical Investigation of Retargeting based on Consumer Online Behaviors | 0.644 | 2 | 2 | 100% |
| 4 | Kaji, Manresa and Pouliot (2023) An adversarial approach to structural estimation | 0.511 | 2 | 1 | 100% |
| 5 | Ursu (2018) The power of rankings: Quantifying the effect of rankings on online consumer search and purchase decisions | 0.511 | 2 | 1 | 100% |
| 6 | Abowd, Gittings, McKinney, Stephens, Vilhuber and Woodcock (2012) Dynamically consistent noise infusion and partially synthetic data as confidentiality protection measures for related time series | 0.405 | 1 | 1 | 100% |
| 7 | Agrawal, Avadhanula, Goyal and Zeevi (2019) MNL-bandit: A dynamic learning approach to assortment selection | 0.405 | 1 | 1 | 100% |
| 8 | Anand and Lee (2023) Using deep learning to overcome privacy and scalability issues in customer data transfer | 0.405 | 1 | 1 | 100% |
| 9 | Athey, Tibshirani and Wager (2019) Generalized random forests | 0.405 | 1 | 1 | 100% |
| 10 | Avella-Medina (2021) Privacy-preserving parametric inference: a case for robust statistics | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Ranking Representation of Optimal Sequential Search | 0.405 | 1 | 1 |