Demand Estimation  - Starting October 20th,

Scott Cunningham is the author of Causal Inference: the Mixtape.

Scott Cunningham, Mixtape Sessions

Demand Estimation. October 20th, 22nd, and 24th
Details: https://www.mixtapesessions.io/session/demand_oct20

Workshop description:This three-day workshop covers the Berry-Levinsohn-Pakes (BLP) approach to estimating the statistical relationship between product sales and product characteristics such as prices. As the foundational approach for differentiated products demand estimation in the industrial organization literature, BLP is used by academics, antitrust regulators, and industry professionals to shed light on difficult questions.<ul><li>What is the value of a new good?</li><li>Will a merger hurt consumers?</li><li>Should we change prices?</li></ul>Through a running empirical example, the workshop will use a series of coding exercises to build up practical knowledge for studying these types of questions and more. Daily Structure:This is a 3-day workshop. The goal of the workshop is for students to gain enough knowledge from the lectures and experience from the programming activities that they become confident and capable enough to implement and interpret these methods in their own work, as well as continue to learn this new material on their own after the workshop concludes. Each day lasts 3 hours with lectures. At the end of each day, coding exercises will be started and solutions will be provided! Schedule:October 20thDay 1 • 6pm-9pm EST<ul><li>History and motivation for BLP (by Ariél Pakes)</li><li>The BLP model</li><li>Pure logit estimation</li><li>Dealing with price endogeneity</li><li>Exercise 1: Getting set up with PyBLP, estimating the pure logit model, and running a price cut counterfactual.</li></ul>October 22ndDay 2 • 6pm-9pm EST<ul><li>Mixed logit estimation</li><li>Differentiation instruments</li><li>Numerical best practices</li><li>Exercise 2: Adding random coefficients, incorporating consumer demographics, and evaluating improvements to the counterfactual</li></ul>October 24thDay 3 • 6pm-9pm EST<ul><li>Micro BLP estimation</li><li>Incorporating consumer survey data</li><li>An overview of other extensions to the BLP approach</li><li>Exercise 3: Adding micro moments, adding second choice moments, and evaluating improvements to the counterfactual</li></ul> About the instructor:<a href="https://jeffgortmaker.com/" title="https://jeffgortmaker.com/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Jeff Gortmaker</a> is a fifth-year PhD candidate at Harvard University studying Business Economics. He has published academic research on best practices for differentiated products demand estimation and maintains a popular open-source Python package, PyBLP, that makes such techniques accessible to a wider range of researchers. In addition to teaching demand estimation, he has taught workshops at Harvard Business School that introduce Python to researchers. Prior to Harvard, Jeff worked at the Federal Reserve Bank of New York.<a href="https://scholar.harvard.edu/pakes/home" title="https://scholar.harvard.edu/pakes/home" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Ariél Pakes</a> is the Thomas Professor of Economics in the Department of Economics at Harvard University, where he teaches courses in Industrial Organization and Econometrics. He received the Frisch Medal of the Econometric Society in 1986. He was elected as a fellow of that society in 1988, of the American Academy of Arts and Sciences in 2002, and of the National Academy of Sciences in 2017. Ariel was the Distinguished Fellow of the Industrial Organization in 2007. In 2017 he received the Jean-Jacques Laffont prize for research which combines empirical work with theory, in in 2018 the BBVA Frontiers of Knowledge Award in Economics and Finance, and in 2022 the Nemmers for research of lasting significance in Economics.International and Student Pricing:Email causalinf@mixtape.consulting for student and international pricing.

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Demand Estimation - Starting October 20th,

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Demand Estimation - Starting October 20th,

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