TOPICS IN ECONOMETRICS: CROSS-SECTION ANALYSIS
(Econ/ARE 240F)
 
 
SYLLABUS
Department of Economics , University of California - Davis
SPRING 2021

Instructor:
Professor Colin Cameron      accameron@ucdavis.edu

Meeting:
Tuesday Thursday 2.10 - 4.00 pm  Zoom class (recording of the class will be posted after class)

Office Hours:
Friday 10.00 am - noon

Teaching Assistant:
Minfei Xu   mfxu@ucdavis.edu
Office hours: Friday 3.00 - 5.00 pm

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
These topics will be covered and in this order but exact timing may change
Classes 1-10     Statistical Learning / Machine Learning and Causal Econometrics with Machine Learning
Classes 11-12   Bayesian regression
Classes 13-14   Cluster-robust inference for regression
Classes 15-16   Panel data (if there are gaps from 240B, 240D)
Classes 17-19   Network Analysis  (tentative)

For Stata Implementation and Some Theory
MUS2: Colin Cameron and Pravin Trivedi (2021), Microeconometrics using Stata, Second Edition, Stata Press, forthcoming.
Pdf's of drafts of the chapters covered in this course will be available at the course Canvas site. 

For statistical learning the main text is an undergraduate level / Masters level book
ISL: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibsharani (2013), An Introduction to Statistical Learning: with Applications in R, 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. A second edition is coming soon. 

Supplementary material on statistical learning will come from the Ph.D. level book
ESL: Trevor Hastie, Robert Tibsharani 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

More detailed Course Outline

Part 0  Overview
http://cameron.econ.ucdavis.edu/e240f/machlearn2019_Intro_brief.pdf (First 40 slides)
http://cameron.econ.ucdavis.edu/e240f/machlearn2020_Causal_Intro_brief.pdf

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

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

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.

Part 4: Other ML methods for prediction
Course slides
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)
MUS2 Chapter 28.5-28.7
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.

Part 5: More ML for causal inference, especially ATE with heterogeneous effects
Course slides
References to come

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 10.1-10.3)

Very Preliminary
Classes 11-12   Bayesian regression
Classes 13-14   Cluster-robust inference for regression
Classes 15-16   Panel data (if there are gaps from 240B, 240D)
Classes 17-19   Network Analysis (tentative)

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

Computer Materials:
Assignments will use STATA.  

Course Grading:
Assignments 50%  Assignment 1 due Friday 2 pm April 16.
(Submit as pdf under Canvas / assignments)
Midterm 20%     Tuesday May 4  2.10 pm
Final 30%           Friday June 4  3.30 - 5.30 pm  Mostly material after midterm.

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 unproctored two-hour open book.