# Applied microeconometrics, Spring 2024

**Applied microeconometrics**

PhD Course, NYU Economics

Spring 2024

Alfred Galichon

This course will revisit some classical topics in microeconometrics (such as random utility models, dynamic discrete choice, demand estimation, matching models, and bundle choice problems) though the lenses of machine learning and state-of-the-art optimization methods. An important part of the course is dedicated to gaining familiarity with computational libraries such as scikit-learn, pytorch, openAI gym, chatGPT, gurobi, and others.

Lectures are delivered under a mix of in-person and online format. The language used is Python. Students not familiar with Python should contact the instructor to be provided a crash course before the start of classes.

**Part 1. Random utility models meet Machine learning**

*Content:*

Poisson regression and logistic regression as generalized Linear Models, Lasso and Elastic Net, Min-Max Regret. Computation using Scikit-learn and TensorFlow.

*Lectures:*

- L1: Tue 1/30, 10am-noon (in person)
- L2: Thu 2/1, 10am-noon (in person)
- L3: Tue 2/6, 10am-noon (online)
- L4: Tue 2/13, 10am-noon (online)

*References:*

*An Introduction to Statistical Learning with applications in Python*with by James, Witten, Hastie, Tibshirani and Taylor*The Elements of Statistical Learning*by Hastie, Tibshirani, and Friedman.*Generalized Linear Models*by McCullagh and Nelder.

*Applications:*

- choice of transportation mode https://www.kaggle.com/datasets/thedevastator/california-commuting-mode-choice-from-2000-2010

**Part 2. Dynamic discrete choice models meet Reinforcement Learning****Content:**

Rust, Markov Decision Processes, Multi-armed bandits, Q-Learning. Computation using OpenAI Gym and Stable Baselines.

*Lectures:*

- L5: Tue 2/27, 10am-noon (online)
- L6: Tue 3/5, 10am-noon (online)
- L7: Wed 3/13, 10am-noon (in person)
- L8: Thu Mar 3/14, 10am-noon (in person)

**References:**

- Rust, J. (1987). Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher.
*Econometrica*. *Dynamic Programming and Optimal Control*by Dimitri P. Bertsekas.*Reinforcement Learning: An Introduction*by Richard S. Sutton and Andrew G. Barto.

**Applications:**

- optimal maintenance decisions: http://qed.econ.queensu.ca/jae/datasets/blevins001/
- career decisions https://respy.readthedocs.io/en/latest/projects/estimating-keane-and-wolpin-1997-msm.html
- tbd, taken from http://individual.utoronto.ca/vaguirre/wpapers/program_code_survey_joe_2008.html

**Part 3. Characteristics models meet Deep Learning and Optimal Transport****Content:**

Pure characteristics model, random coefficient logit model, Power diagrams, matching models.

Simulation (Probit, GHK), stochastic GD. Computation using pyopt package, pyBLP, pyTorch.

**Lectures:**

- L9: Fri, Mar 3/15, 10am-noon (in person)
- L10: Tue Mar 3/26, 10am-noon (online)
- L11: Tue 4/2, 10am-noon (online)
- L12: Tue 4/16, 10am-noon (in person)

**References:**

*Deep Learning*by Aaron Courville, Ian Goodfellow, and Yoshua Bengio.- Train, K. (2009).
*Discrete Choice Methods with Simulation*. - Galichon, A. (2016).
*Optimal Transport Methods in Economics*.

**Applications:**

* automotive pricing https://www.kaggle.com/code/rkamath1/exploratory-analysis-tests-regression/input

https://pyblp.readthedocs.io/en/stable/_notebooks/tutorial/blp.html

* marriage market: https://github.com/TraME-Project/TraME-Datasets/

**Part 4. Recent advances on Bundle choice***Content:*

Bundle choice, assortment problem, one-to-many matching, gross substitutes, greedy algorithm

*Lectures:*

- L13: Thu 4/18, 10am-noon (in person)
- L14: Fri 4/19, 10am-noon (in person)
- L15: Tue 4/23, 10am-noon (online)

*Application:*

- bundling in multichannel television markets, https://www.jstor.org/stable/23245430, Dataset: https://warwick.ac.uk/fac/soc/economics/staff/academic/crawford/research/bundling_welfare/