Department of Economics, University of California - Davis
FALL 2023

Professor Colin Cameron,  1124 Social Sciences and Humanities
Email:  Website:

Meeting: Tuesday and Thursday 1.40-3.00 pm  Bainer 1060  (In person)

Office Hours:  Tuesday      3.30 - 5.00 pm
                         Wednesday 3.30 - 5.00 pm

Teaching Assistant: 

Kathya Tapia              
Office hours:  Tuesday 11.00am - 1.00 pm   SSH 0118

Discussion Sections:
A01: Wednesday 5.10 - 6.00 pm  Bainer 1060

Course Goals:
The course goals are to introduce students to research applying basic regression methods to data.
(1) Students write a research paper based on analysis of economics data.
(2) Cover some basic regression methods, especially for causal analysis. 

(3) Assignments will including downloading and cleaning data (from IPUMs), some basic regression analysis, and replication of a published journal paper.
(4) Teaching will use Stata. Projects can use Stata, R or Python.

The course is intended for Economics majors, and first Pass is restricted to Economics majors.
The key requirement is Economics 102 with a grade of B- or better or (better still) Economics 140 with a grade of B- or better.
Enrollment is capped at 30 students.
Should there still be
room after first pass I will consider s
tudents who have taken STA 108 with a grade of B- or better or ARE 106 with a grade of B- or better, provided they have also taken basic economics courses in ECN or ARE.


Classes will be a mix of teaching necessary methods and student presentations.


Assignments will use Stata. I recommend strongly that you purchase your own copy of Stata.
For your project you can use Stata, R or Python.
Key material will be posted at the course Canvas site ( under Files

Lecture slides: 

Lecture Slides will be posted at the course Canvas site ( under Files / Lecture Slides.


- You should have access to an undergraduate econometrics textbook.
- Below I give references to AED:
A. Colin Cameron Analysis of Economics Data: An Introduction to Econometrics
This is a relatively inexpensive book (available as pdf or print) that provides an introduction.
- Undergraduate level treatments that are a bit more more advanced are available in the standard texts
Jeffrey Wooldridge Introductory Econometrics: A Modern Approach
Stock and Watson Introduction to Econometrics.
A text focused on causal inference with individual-level data is Scott Cunningham Causal Inference: The Mixtape

Stata for regression:
We use the package Stata that is used primarily in economics, other social sciences and biostatistics.
To get started in Stata see
and especially
Stata is installed in computer labs 93 Hutchison, 2101 SCC. To see whether these labs are available see
It is also available after hours in the Virtual Lab (evenings and weekends when labs are closed - see
This video provides directions if you use the Virtual lab: Connect_virtual_lab_and_start_Stata.mp4

You can also purchase your own copy of Stata (recommended) - go to
For this course and other economics classes the cheapest version Stata/BE is more than adequate and costs $48 (6 months), $94 (1 year); $225 (permanent copy).
To install Stata after it is purchased:
(1) Choose the correct operating system (e.g. Windows or Mac);
(2) Choose the correct version of Stata - the student price version is Stata/IC;
(3) When you first run Stata after installation it will ask for an "authorization code". These codes are given in a pdf attachment you will received in the email from Stata following purchase (some codes are lengthy and it is easiest to cut and paste them in).

The Stata code used in my ECN 102 text is at
And the datasets are at

For more advanced Stata my book can be accessed online through the UCD library. All the data and code for that book are available free at

R for regression:
R can be downloaded to your own computer free.
I have some older notes at
and especially see

The key component of this class is a research paper which will be a group project (2 students per paper depending on class size).


Class 1: Regression basics and Stata
Files tr_basics.pdf, and AED_EARNINGS_COMPLETE.DTA at Canvas

Classes 2 to 8: Introduction to Causal Methods
In order go through the following topics with slides posted at
- A brief overview
- Randomized control trials (RCTs)  AED 14.2 and 13.5
- Instrumental variables (IV)  AED 17.4, Appx C.3 and data example 13.8
- Directed acyclic graphs (DAGS)
- Panel data  and fixed effects (FE)  AED 17.1, 17.2 and 17.3
- Differences-in-differences (DID)  AED 17.5.4 and data example 13.6
- Regression discontinuity design (RD)  AED 17.5.7 and data example13.7
- Synthetic control
- Treatment evaluation (TE)  AED 17.5

Also Brief Introduction to Machine Learning

Classes 9 to 10: Project development and discussion

Classes 11: Midterm exam

Classes 12 to 13: Projects discussion and additional methods

Classes 14 to 15: Project interim presentations

Classes 16 to 18: Projects discussion and additional methods

Classes 19 and 20: Final project presentations


Attendance:        5%   Attendance is checked at each class. Lose 1% for each class missed beyond two missed classes in the quarter.
Assignments:     30%   Due 10 a.m. on (1) Friday October 6, (2) Wednesday October 18, (3) To be determined.
Midterm Exam:   15%   In-class exam   Thursday November 2
Project interim Presentation:  5
Tuesday November 14 and Thursday November 16
Project interim Report:  5Due 4 pm Friday November 17
Project Final Presentation: 10% Thursday November 30 and Tuesday December 3   
Project final:
Due4 pm Friday December 15.

Assignments are posted on Canvas under Files / Homeworks.
They are to be turned in as a single pdf file on Canvas under Assignments by 10 am on Fridays.
For how to include Stata results see USING STATA AND SAVING RESULTS.pdf posted
on Canvas under Files / Homeworks.

Scores are posted at Canvas. You have one week from when work is first returned in class (or in discussion section in the case of assignments), to raise any questions about grading.

Course grade is determined by the total score, with weights given above. 

Academic Honesty: Academic dishonesty is unfair to the majority of students who are honest. To that end the Davis Division of the U.C. Faculty Senate has the following policies.
(1) All undergraduate and graduate course outlines (syllabi) should list or provide a link to the U.C. Davis Code of Academic Conduct which is at This provides many leading examples of academic misconduct. You should read this.
(2) One specific example of academic honesty is copying from solutions to assignments given in previous 132 courses.

(3) If an instructor has a reasonable suspicion of academic misconduct, whether admitted by the student or not, the instructor shall report the matter to the Office of Student Support and Judicial Affairs.
(4) The instructor has authority to determine a grade penalty when academic misconduct is admitted or is determined by adjudication to have occurred; with a
maximum grade penalty of F for the course.
Note that Student Support and Judicial Affairs may separately impose sanctions for academic misconduct, including community service, suspension and dismissal.

Out of class collaboration: You are allowed to work together in groups for the assignments, but each student must turn in an individual solution. Please indicate on the solution the names of the other students you worked with, if any that you worked with you on the problem set. And for Stata, each person must create their own Stata output and write up their own answers. It is not a violation of this policy to submit essentially the same answer on an assignment as another student, but it is a violation of this policy to submit a close to exact or exact copy.