Demand estimation in industrial organization
I wrote notes on demand estimation in industrial organization that survey a selected set of discrete choice techniques, from multinomial logit to Berry, Levinsohn, and Pakes (BLP)-type methods.
While originally intended as a study guide for a graduate industrial organization class, they can also serve as a high-level introduction for anyone who wants to use demand estimation.
The notes discuss conceptual and technical points, such as:
- Differences between product space and characteristics space models
- History and interpretation of the multinomial logit model
- Bridging traditional demand estimation and industrial organization methods, by accounting for the correlation between unobserved quality and price
- Understanding the random-coefficients logit demand model behind BLP, and the MPEC and nested fixed point versions of the BLP estimator
- Extensions to standard BLP, including adding micro data (Micro BLP) and incorporating a supply side model
- Econometrics of the BLP estimator
- Remaining difficulties, and some applications (including merger simulation)
(Notes last updated August 2021.)