Instructor:
Professor Colin Cameron
accameron@ucdavis.edu
Meeting:
Tuesday Thursday 12.10 - 1.30 pm Creuss Hall 107
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
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.