(Econ/ARE 240F)

SYLLABUS

**Instructor:**

Professor Colin Cameron
accameron@ucdavis.edu

**Meeting:**

Tuesday Thursday 12.10 - 1.30 pm Creuss Hall 107

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

Teaching Assistant:

Minryul Park mmpark@ucdavis.edu

Office hours: Wednesday 9-10 am (SSH 0118) and Thursday 9-10 am (SSH 0118)

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

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:**

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)

**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

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

Brief coverage of bootstrap, spatial regression and social networks

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

Computer Materials:

**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.