Incorporating Micro Data into Differentiated Products Demand Estimation with PyBLP
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Cited by:
- Tianyu Du & Ayush Kanodia & Susan Athey, 2023.
"Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python,"
Papers
2304.01906, arXiv.org, revised Jul 2023.
- Du, Tianyu & Kanodia, Ayush & Athey, Susan, 2023. "Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python," Research Papers 4106, Stanford University, Graduate School of Business.
- Matsumoto, Tomoki & Kamai, Tomohito & Kanazawa, Yuichiro, 2024. "Examining bargaining power in the distribution channel under possible price pass-through behaviors of retailers," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
- repec:ags:aaea22:343858 is not listed on IDEAS
More about this item
JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
- D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
- L0 - Industrial Organization - - General
- L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco
NEP fields
This paper has been announced in the following NEP Reports:- NEP-COM-2023-10-02 (Industrial Competition)
- NEP-ECM-2023-10-02 (Econometrics)
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