(Econ/ARE 240F) Syllabus

**Instructor:**

Professor Colin Cameron Bainer 1132
accameron@ucdavis.edu

**Meeting:**

Tuesday Thursday 8.00 - 9.50 am SSH 1113 (Blue
Conference Room)

Wednesday 2.00 - 4.00 pm and Thursday 10.00 am - 11.00 noon

Jongkwan Lee jknlee@ucdavis.edu

Office hours: Tuesday 2-3 pm and Wedenesday 2-3 pm

**Pre-requisites:**** The listed pre-requisite is Econ / ARE
240D. ****The essential pre-requisite is Econ / ARE
240D.**

**Course Goals:** The Spring 2016 course includes a survey of
Statistical Learning Methods. This will cover many of the methods
very briefly. The most important for econometrics include
cross-validation, Lasso and regression trees. Following the
survey the course will consider their use in causal econometrics
research. The remainder of the course covers various topics.

**Brief Course Outline:
**Classes 1-10 Statistical learning

Classes 11-12 Statistical learning for causal econometrics

Classes 13-16 Bayesian analysis and multiple imputation

Classes 17-20 Further topics (most likely including clustering)

For statistical learning the main text is an undergraduate 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 http://www-bcf.usc.edu/~gareth/ISL/
and a $25 hardcopy can be obtained via
http://www.springer.com/gp/products/books/mycopy

Supplementary material on statistical learning will come from the
graduate 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
and a $25 hardcopy can e obtained via
http://www.springer.com/gp/products/books/mycopy

**Detailed Course Outline: **

**Classes 1-2** Introduction to Statistical Learning

Statistical learning overview (ISL chapters 1, 2.1-2-2)

Getting started in R (ISL chapter 2.3 and http://cameron.econ.ucdavis.edu/R/R.html)

Linear Regression (ISL chapter 3)

Cross-validation (ISL chapter 5)

**Classes 3-5** Linear Model Selection and
Regularization

Subset selection (ISL chapter 6.1)

Ridge Regression, Lasso, and LARS (ISL chapter 6.2 and
ESL pp.73-79, 86-93)

Principal Components and Partial Least Squares (ISL chapter 6.3)

High-dimensional Data (ISL chapter 6.4)

**Classes 6-7** Flexible Regression Models

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)

**Classes 8-9 ** 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 10.1-10.3)

**Class 10** Midterm exam

**Classes 11-12** Statistical Learning in Econometrics

Double Selection Lasso Belloni, Chernozhukov and Hansen (JEP
Spring 2014 pp.29-50)

Big Data for Econometrics Varian (JEP Spring 2014 pp.3-28)

Recursive tree partitioning Athey and Imbens (2015)

**Classes 13-17 **Bayesian Methods and Multiple
Imputation

Bayesian Methods Cameron and Trivedi (2005), Microeconometrics:
Methods and Applications, Chapter 13.1-13.6 plus notes
provided

Multiple Imputation Cameron and Trivedi (2005), Microeconometrics:
Methods and Applications, Chapter 13.7, 21.7-27.9

**Other Material:**

Assignments, data, etc will be posted at the course website at
Smartsite under Resources.

R

Assignments will use STATA.

**Course Grading:**

Assignments 50% Due Thursdays April 7, 21; May 3, 17; June
2.

Midterm 25% Thursday
April 28

Final 25% **Wednesday
June 8 10.30am-12.30pm 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 closed book. The final exam is comprehensive.