RESEARCH WITH ECONOMICS DATA (Econ 190) 
[http://cameron.econ.ucdavis.edu/e190/e190syl.html]
Department of Economics, University of California - Davis
FALL 2024

SYLLABUS

Instructor:
Professor Colin Cameron,  1124 Social Sciences and Humanities
Email: accameron@ucdavis.edu  Website: https://cameron.econ.ucdavis.edu/

Meeting: Tuesday and Thursday 9.00-10.20 pm  Storer 1344  (In person)

Office Hours:  Tuesday      3.30 - 5.00 pm  in SSH 1124
                         Wednesday 3.30 - 5.00 pm  in SSH 1124

Discussion Section:
A01: Wednesday 5.10 - 6.00 pm  Wellman 3

Course Goals:
The course goals are to provide students with the skills to write a basic research paper that applies regression methods to data.
The course will cover
(1) Basic regression methods.
(2) Downloading and cleaning data from a major public data source (IPUMs). 
(3) Replication of a published journal paper.
(4) Creating publishable tables from statistical analysis and regression analysis
(5) A project that is (a) presented using overheads, and (b) written in the format of a published research paper.

Pre-requisites:
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 25 students.
Should there still be room after first pass I will consider students 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.

COURSE OUTLINE: 

Classes 1-3: Regression methods
Multiple regression including nonlinearity (e.g. quadratic), interactions, indicator variables and logs.
Most of tr_basics.pdf
(Assignment 1 due day after class 3)

Class 4: Various robust standard errors of regression coefficients
tr_basic.pdf sections 6 and 7
AED_Appendix_C.pdf sections C1 and C2

Class 5: Midterm exam
This covers classes 1-4 and assignment 1

Classes 6-7
Obtaining data from IPUMS
Fixed and random effects tr_extras.pdf
(Assignment 2 due day after class 7)

Classes 8-9
Differences in differences - https://cameron.econ.ucdavis.edu/causal/tr_dind.pdf
Data replication: David Card and Alan Krueger (1994), "“Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania”,"
American Economic Review 84, pages 772-793.
The article and data are available from https://davidcard.berkeley.edu/data_sets.html
(Assignment 3 due day after class 9)

Class 10
Panel data - fixed and random effects
(Assignment 4 due day after class 11)

Class 11
Tables of Descriptive Statistics in Stata
Introduce the project - Discussion of Affordable Care Act (Obamacare)
Work in groups of size 2

Class 12
Tables of Regression Results in Stata
Logit and Probit for Binary Outcomes

Class 13
Logit and Probit for Binary Outcomes
Possible career paths

Classes 14-15
Various Causal methods
Parallel trends assumption for differences in differences
 
Class 16
Example of research
trdyadic_cameron_miller_January_2024.pdf under Canvas / Files

Classes 17-18
Project and how to produce a nicely structured and formatted research paper.
(Includes Assignment 5 due day of class 14 (a Tuesday)

Classes 19 - 20
Project presentations
10-15 minute in-class presentation using well developed overhead slides

Final Project due

COURSE MATERIALS

Stata:
Teaching, assignments and project will use Stata.
I recommend strongly that you purchase your own copy of Stata - see below.

Econometrics Textbook:
- 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.
For more on the book and associated material see https://cameron.econ.ucdavis.edu/aed/
- Undergraduate level treatments that are 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

Canvas Website
Key material will be posted at the course Canvas site (http://canvas.ucdavis.edu) under Files
This includes Lecture Slides posted at the course Canvas site under Files / Lecture Slides.

Stata resources:
We use the package Stata that is used primarily in economics, other social sciences and biostatistics.
To get started in Stata see http://cameron.econ.ucdavis.edu/stata/stata.html and especially http://cameron.econ.ucdavis.edu/stata/stataintro.html
The Stata website has introductory material at https://www.stata.com/links/stata-basics/ and videos at https://www.stata.com/links/video-tutorials/
Also see https://www.stata.com/bookstore/stata-cheat-sheets/

You may find it helpful to use especially the first few chapters of my book "Microeconometrics using Stata: Volume 1: Cross-sectional and Panel Regression Methods".
Chapters 1-3 of the final draft are posted at the Course Canvas Website.
The book can be accessed online through the UCD library.
For purchase see https://cameron.econ.ucdavis.edu/mus2/
All the data and code for the book are available free at https://www.stata-press.com/data/mus2.html  

I strongly recommend that you purchase your own copy of Stata - go to https://www.stata.com/order/new/edu/gradplans/student-pricing/
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).

If you do not purchase Stata, Stata is installed in computer labs 93 Hutchison, 91 Shields.
To see whether these labs are available see https://computerrooms.ucdavis.edu/available/.
Stata is also available after hours in the Virtual Lab (evenings and weekends when labs are closed - see https://virtuallab.ucdavis.edu)
This video provides directions if you use the Virtual lab: Connect_virtual_lab_and_start_Stata.mp4 

Project:
A key component of this class is a group project (1-2 students per group).

Assignments:     30%   Due 10 a.m. on Fridays October 4, 18, and 25 and Nov 1, and Wednesday Nov 13. Best 4 of 5.
Midterm Exam:  15%   In-class exam   Thursday October 10
Project Interim Report: 15% 10 am Friday November 22
Project Presentation in class: 10% Tuesday December 3 and Thursday December 5
Project Report:     30%    Due 4 pm Tuesday December 10.
Final exam: There is no final exam.

Assignments are posted on Canvas under Files / Homeworks.
Assignment answers are to be turned in on Canvas under Assignments by 10 am on Fridays.
Assignment answers are to be turned in as a single pdf file that includes key Stata output.
Additionally hand in the Stata program as a single do file.
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. 

ChatGPT: ChatGPT and similar LLM's can be used for coding. They cannot be used for written work.

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 https://ossja.ucdavis.edu/code-academic-conduct. 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.