(Econ/ARE 240F)

SYLLABUS

**Instructor:**

Professor Colin Cameron
accameron@ucdavis.edu https://cameron.econ.ucdavis.edu/

**Meeting:**

Tuesday Thursday 10.30 am - 11.50 pm Hoagland 113

Tuesday 3.30-5.00 pm (in office) and Wednesday 3.30-5.00pm (zoom and in office)

Teaching Assistant:

Yumeng Gu ymgu@ucdavis.edu

Office hours: 2.00 - 4.00 pm in SSH 0116

Pre-requisites:

**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

Class 20 TBA: Brief overview of bootstrap or spatial regression

**Software
**Course will mostly use

Additionally we wilI use

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:**

MUS2:

__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,"

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,"

**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",

Hal Varian, "Big Data: New Tricks for Econometrics",

ML Part 5:

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)

**Class 11
**Midterm Exam

**Classes 12-13
**Simulation and Monte Carlo Experiments

Course slides, MMA chapter 12, MUS2 chapter 5

Bayes Part 1:

Course slides, MMA chapter 13, MUS2 chapter 29

**Class 15-16
Bayes Part 2: **Data Augmentation, Imputation

Course slides, MMA chapter 13, 27, MUS2 chapter 30.

**Class 17
Maximum Simulated Likelihood; Brief Bootstrap
**Course slides, MMA Chapter 12.

**Classes 18-19
Cluster-Robust Inference Part 1: **Many 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.

https://www.jstor.org/stable/pdf/24735989.pdf

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

TBA: Possibly brief coverage of spatial regression

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

Computer Materials:

**Course Grading:**

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.