Professor Colin Cameron firstname.lastname@example.org https://cameron.econ.ucdavis.edu/
Tuesday Thursday 10.30 am - 11.50 pm Hoagland 113
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
Class 12-13 Simulation and Monte Carlo experiments
Classes 14-16 Bayesian regression, multiple imputation
Class 17 Simulated maximum likelihood
Classes 18-19 Cluster-robust inference for regression and bootstrap
Class 20 Brief overview of quantile regrerssion
Course will mostly use Stata.
Additionally we wilI use Python for machine learning. For Python click here.
A good Python reference is Kevin Sheppard (2021), Introduction to Python for Econometrics, Statistics and Numerical Analysis: Fourth+ Edition"
pdf at https://www.kevinsheppard.com/teaching/python/notes/
Texts for Machine Learning
For Stata Implementation and Some Theory
MUS2: Chapter 28 of Colin Cameron and Pravin Trivedi (2023), Microeconometrics using Stata: Volume 2: Nonlinear Models and Causal Inference, Second Edition, Stata Press. https://cameron.econ.ucdavis.edu/mus2/
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 (2023), Microeconometrics using Stata, Second Edition, Stata Press.
More detailed Course Outline:
ML Part 1 Model selection and cross validation
ISL Chapters 5.1, 6.1; MUS2 Chapter 28.1-28.2 and 11.3.8.
ML Part 2: Shrinkage methods (lasso, ridge, elastic net)
ISL Chapters 6.2; ESL pp.73-79, 86-9; MUS2 Chapter 28.3-28.4
ML Part 3: ML for causal inference using lasso
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.
ML Part 4: Other ML methods for prediction
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.
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
ML Part 6: Classification and unsupervised learning
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)
Simulation and Monte Carlo Experiments
Course slides, MMA chapter 12, MUS2 chapter 5
Bayes Part 1: MCMC theory and application in Stata
Course slides, MMA chapter 13, MUS2 chapter 29
Bayes Part 2: Data Augmentation, Imputation
Course slides, MMA chapter 13, 27, MUS2 chapter 30.
Maximum Simulated Likelihood; Brief Bootstrap
Course slides, MMA Chapter 12.
Cluster-Robust Inference Part 1: Many clusters
Cluster-Robust Inference Part 2: Few clusters
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.
James G. MacKinnon, Morten O. Nielsen and Matthew W. Webb (2023), "Cluster-Robust Inference: A Guide to Empirical Practice," Queens Economics Department WP 1456. https://www.sciencedirect.com/science/article/pii/S0304407622000781
A. Colin Cameron and Douglas L. Miller, "Recent Developments in Cluster Robust Inference." Slides at https://cameron.econ.ucdavis.edu/research/papers.html
Brief coverage of quantile regression
Assignments 40% Tentative: Due Tuesdays 10.30 am Ass 1 due April 18; Ass 2 due May 2, Ass 3 due May 23, Ass 4 due June 7.
(Submit as pdf under Canvas / assignments)
Midterm 30% Tuesday May 9 10.40 am Machine learning In-class exam
Final 30% Friday June 9 1.00 - 3.00 pm Material after machine learning. 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.