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 kattapia@ucdavis.edu
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.
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 30 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 will be a mix of teaching necessary methods and student presentations.
You
can also purchase your own copy of Stata (recommended) - 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).
The
Stata code used in my ECN 102 text is at https://cameron.econ.ucdavis.edu/aed/aedSTATAprograms.html
And the datasets are at https://cameron.econ.ucdavis.edu/aed/aeddatasources.html
For
more advanced Stata my book https://cameron.econ.ucdavis.edu/mus2/
can be accessed online through the UCD library. All the data and
code for that book are available free at https://www.stata-press.com/data/mus2.html
R for
regression:
R can be downloaded to your own computer free.
I have some older notes at https://cameron.econ.ucdavis.edu/R/R.html and especially see https://cameron.econ.ucdavis.edu/R/Rintro.html
Project:
The key component of this class is a
research paper which will be a group project (2 students per
paper depending on class size).
COURSE OUTLINE
Class 1: Regression basics and Stata
Files tr_basics.pdf, basics.do 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 https://cameron.econ.ucdavis.edu/causal/
- 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
COURSE GRADING
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: 5%
Due 4 pm Friday November 17
Project Final Presentation: 10% Thursday November 30 and Tuesday December 3
Project final:
30% 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.
Course grade is determined by the total
score, with weights given above.