TOPICS IN ECONOMETRICS: CROSS-SECTION ANALYSIS
(Econ/ARE 240F)
 
 
SYLLABUS
Department of Economics , University of California - Davis
SPRING 2022

Instructor:
Professor Colin Cameron      accameron@ucdavis.edu

Meeting:
Tuesday Thursday 12.10 - 1.30 pm   Creuss Hall 107 

Office Hours:
Tuesday 2.00-3.30 pm (in office) and Wednesday 2.00-3.00pm (zoom and in office)

Discussion Section Meeting:
Friday 11.00 - 11.50 am   Wellman 201

Teaching Assistant:

Minryul Park  mmpark@ucdavis.edu
Office hours: Wednesday 9-10 am (SSH 0118)  and  Thursday 9-10 am (SSH 0118)

Pre-requisites:
The listed pre-requisite is Econ / ARE 240D.  The essential pre-requisite is Econ / ARE 240D.

Course Goals: Cover several topics in cross-section econometrics not covered in previous classes.
Basic theory will be presented plus implementation using Stata.

Brief Course Outline
Classes 1-10     Statistical Learning / Machine Learning and Causal Econometrics with Machine Learning
Classes 11-12, 14   Bayesian regression
Class     15        Simulated maximum likelihood and imputation
Classes 16-17   Cluster-robust inference for regression
Class     18-20   Brief overview of bootstrap, spatial regression and networks

Texts for Machine Learning

For Stata Implementation and Some Theory
MUS2: Chapter 28 of Colin Cameron and Pravin Trivedi (2022), Microeconometrics using Stata: Volume 2: Nonlinear Models and Causal Inference, Second Edition, Stata Press, forthcoming.
Pdf of a draft of chapter 28 will be available at the course Canvas site. 

For statistical learning the main text is an undergraduate level / Masters level book
ISL2: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2013), An Introduction to Statistical Learning: with Applications in R, Second Edition, Springer.
A free legal pdf is at free legal pdf at https://www.statlearning.com/
A $25 hardcopy can be ordered via online UCD library.
 
Supplementary material on statistical learning is in the Ph.D. level book
ESL: Trevor Hastie, Robert Tibshirani and Jerome Friedman (2009), The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer.
A free legal pdf is at http://statweb.stanford.edu/~tibs/ElemStatLearn/index.html

Texts for Other Topics
MMA:
Colin Cameron and Pravin Trivedi (2005), Microeconometrics: Methods and Applications, Cambridge University Press.
MUS2:
Colin Cameron and Pravin Trivedi (2022), Microeconometrics using Stata, Second Edition, Stata Press, forthcoming.

More detailed Course Outline

Class 1
Overview
Course slides

ML Part 1  Model selection and cross validation
Course slides
ISL Chapters 5.1, 6.1; MUS2 Chapter 28.1-28.2 and 11.3.8.

Classes 2-3
ML Part 2:
Shrinkage methods (lasso, ridge, elastic net)
Course slides
ISL Chapters 6.2;  ESL pp.73-79, 86-9; MUS2 Chapter 28.3-28.4

Classes 4-5
ML Part 3:
ML for causal inference using lasso
Course slides
MUS2 Chapter 28.8
Alex Belloni, Victor Chernozhukov and Christian Hansen (2014), "High-dimensional methods and inference on structural and treatment effects," Journal of Economic Perspectives, Spring, 29-50.
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey and James Robins (2018), "Double/debiased machine learning for treatment and structural parameters," The Econometrics Journal, 21, C1-C68.

Classes 6-7
ML Part 4:
Other ML methods for prediction
Course slides
MUS2 Chapter 28.5, 28.6.1-28.6.6, 28.7
Principal Components and Partial Least Squares (ISL chapter 6.3)
High-dimensional Data (ISL chapter 6.4)
Polynomials, Step Functions and Basis Functions (ISL chapter 7.1-7.3)
Splines (ISL chapter 7.4-7.5)
Local Regression (ISL chapter 7.6)
Generalized Additive Models (ISL chapter 7.7)
Regression Trees (ISL chapter 8.1)
Bagging, Random Forests, Boosting (ISL chapter 8.2)
Neural Networks (ISL chapter 10.1-10.7)
Sendhil Mullainathan and J. Spiess: "Machine Learning: An Applied Econometric Approach", Journal of Economic Perspectives, Spring 2017, 87-106.
Hal Varian, "Big Data: New Tricks for Econometrics", Journal of Economic Perspectives, Spring, 3-28.

Classes 8-9
ML Part 5:
More ML for causal inference, especially ATE with heterogeneous effects
Course slides which include references
MUS2 Chapter 28.6.7, 28.6.8

Class 10
ML Part 6:
Classification and unsupervised learning
Course slides
Logistic Regression and Discriminant Analysis  (ISL chapter 4.1-4.5)
Support Vector Machines (ISL chapter 9.1-9.3)
Unsupervised Learning (ISL chapter 12.1-12.4)

Classes 11-12 Bayes Part 1: MCMC theory and application in Stata
Course slides, MMA chapter 13, MUS2 chapter 29

Class 13
Midterm Exam

Class 14
Bayes Part 2:
Data Augmentation, Imputation
Course slides, MMA chapter 13, 27, MUS2 chapter 30.

Class 15
Maximum Simulated Likelihood
Course slides, MMA Chapter 12.

Classes 16-17
Cluster-Robust Inference Part 1:
Many clusters
Cluster-Robust Inference Part 2: Few clusters
Course slides
A. Colin Cameron and Douglas L. Miller, "A Practitioner's Guide to Robust Inference with Clustered Data," Journal of Human Resources, Spring 2015, Vol.50, No.2, pp.317-373.
https://www.jstor.org/stable/pdf/24735989.pdf
James G. MacKinnon, Morten O. Nielsen and Matthew W. Webb (2021), "Cluster-Robust Inference: A Guide to Empirical Practice," Queens Economics Department WP 1456.
https://ideas.repec.org/p/qed/wpaper/1456.htm

Classes 18-20
Brief coverage of bootstrap, spatial regression and social networks
Course slides

Other Material:
Slides, assignments, programs, data, etc will be posted at the course Canvas website under Files.

Computer Materials:

Assignments will use STATA.  

Course Grading:
Assignments 40%  Ass 1 due Friday 2 pm April 16; Ass 2 due Tuesday 12.10 pm May 3, Ass 3 due 2 pm Friday May 20, Ass 4 due 2 pm Friday June 3.
(Submit as pdf under Canvas / assignments)
Midterm 30%     Tuesday May 10  12.10 pm  Machine learning  In-class exam
Final 30%           Tuesday June 7  6.00 - 8.00 pm  Mostly material after midterm. In-class exam

Assignments must be handed in on time, so solutions can be discussed in class and distributed in a timely manner.
No credit for late assignments. All must be done.
Academic integrity is required. What is academic integrity? See the UCD Student Judicial Affairs website http://sja.ucdavis.edu/
As an exception to their rules, I permit some collaboration with other students in doing assignments, but the work handed in must be your own. Each person must create their own Stata output and write up their own answers. And you are to write on your assignment the name of the person(s) you worked with.
Exams will be closed book.