Tinghan Zhang
arXiv 13 Jan 2025 · Econometrics
arXiv:2501.07514 · PDF · Extracted main text
The rapid growth of online shopping has made consumer search data increasingly available, opening up new possibilities for empirical research. Sequential search models offer a structured approach for analyzing such data, but their estimation remains difficult. This is because consumers make optimal decisions based on private information revealed in search, which is not observed in typical data. As a result, the model's likelihood function involves high-dimensional integrals that require intensive simulation. This paper introduces a new representation that shows a consumer's optimal search decision-making can be recast as a partial ranking over all actions available throughout the consumer's search process. This reformulation yields the same choice probabilities as the original model but leads to a simpler likelihood function that relies less on simulation. Based on this insight, we provide identification arguments and propose a modified GHK-style simulator that improves both estimation performances and ease of implementation. The proposed approach also generalizes to a wide range of model variants, including those with incomplete search data and structural extensions such as search with product discovery. It enables a tractable and unified estimation strategy across different settings in sequential search models, offering both a new perspective on understanding sequential search and a practical tool for its application.
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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 | Weitzman, Martin (1979) Optimal search for the best alternative | 1.000 | 11 | 4 | 100% |
| 2 | Ursu, Raluca and Seiler, Stephan and Honka, Elisabeth (2025) The sequential search model: A framework for empirical research | 1.000 | 9 | 3 | 100% |
| 3 | Compiani, Giovanni and Lewis, Gregory and Peng, Sida and Wang, Peichun (2024) Online Search and Optimal Product Rankings: An Empirical Framework | 0.961 | 9 | 5 | 89% |
| 4 | Moraga-González, José Luis and Sándor, Zsolt and Wildenbeest, Matthi… (2023) Consumer search and prices in the automobile market | 0.928 | 5 | 4 | 80% |
| 5 | Jiang, Zhenling and Chan, Tat and Che, Hai and Wang, Youwei (2021) Consumer search and purchase: An empirical investigation of retargeting based on consumer online behaviors | 0.928 | 5 | 3 | 80% |
| 6 | Morozov, Ilya (2023) Measuring benefits from new products in markets with information frictions | 0.928 | 5 | 3 | 80% |
| 7 | Onzo, Kohei and Ansari, Asim (2025) Bayesian nonparametric sequential search | 0.928 | 5 | 3 | 80% |
| 8 | Morozov, Ilya and Seiler, Stephan and Dong, Xiaojing and Hou, Liwen (2021) Estimation of preference heterogeneity in markets with costly search | 0.874 | 6 | 4 | 67% |
| 9 | Greminger, Rafael P (2022) Optimal search and discovery | 0.874 | 6 | 2 | 100% |
| 10 | Chen, Yuxin and Yao, Song (2017) Sequential search with refinement: Model and application with click-stream data | 0.843 | 4 | 3 | 75% |
Showing the top 10 of 59 scored citations.