(Econ/ARE 240F)

SYLLABUS

**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)**

Friday 10.00 am - noon

Office hours: Friday 3.00 - 5.00 pm

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
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.

**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.