Department of Economics, University of California - Davis

Professor Colin Cameron
1124 Social Sciences and Humanities

Tues Thurs 10.30 - 11.50 a.m. Wellman 106

Office Hours:
Monday afternoon       1.30 p.m. - 3.00 a.m.
Wednesday afternoon  3.30 p.m. - 5.00 p.m.

Teaching Assistants: 

Joowon Yoon    Office hours: Mon 9-10 am and Wed 5-6 pm in room  SSH 0118
Renee Liu       Office hours: Tues 12-2pm in room  SSH 0116

Discussion Sections:
Joowon Yoon     A01: Wednesday 12.10 - 1.50 pm  93 Hutchison
Joowon Yoon     A02: Wednesday 1.10 - 2.00 pm  93 Hutchison
Renee Liu            A03: Wednesday 2.10 - 3.00 pm  93 Hutchison
Renee Liu            A04: Wednesday 3.10 - 4.00 pm  93 Hutchison

Course Goals:

(1) Locate economics data and provide meaningful economic analysis of these data.
(2) Use a standard econometrics package (Stata) to produce charts, scatter plots, descriptive statistics and simple linear regression.
(3)  Provide a bridge between introductory statistics and more advanced data analysis courses (e.g. between Statistics 13 and Economics 140).

Economics 1A-B, Statistics 13 or 32 and Math 16A-B or consent of instructor.
The essential prerequisites are exposure to introductory lower-division courses in economics and statistics.

Relationship to Economics 140
Economics 140 (Econometrics) is a more advanced course that also covers the methods of Economics 102.
Economics 140 has Economics 102 or Statistics 108 as a prerequisite.

Note: Due to course overlap (all focus on regression) only 10 units of credit are available for taking all three of ECN 102, ECN 140 and STA 108.

We recommend stronger students serious about data analysis skip ECN 102 and take STA 108 followed by ECN 140.
[Note: The current listed prerequisites for ECN 140 are incorrect and are being changed to
 (ECN 100 or 100A or ARE 100A); (ECN 102 or STA 108); or Consent of instructor.].

For more on possible data classes see
These include classes on machine learning and on time series and financial data that are currently listed as ECN 190.

Topical Outline by Lecture Number

For all topics an essential ingredient is that economics data be analyzed throughout. This includes some case studies.
For some chapters we will not cover the entire chapter. The key sections will be announced as we go along.

A. Introduction:
Lecture 1: Introduction and getting going on Stata  (chapter 1 + Stata

B. Univariate: Analysis of a single economics variable
Lecture  2: Summarizing data using descriptive statistics and visualizing data using charts (chapter 2.1-2.2)
Lecture  3: Visualizing data using charts and univariate data transformation (chapter 2.3-2.4 and 4.2.4 and 4.3.1)
Lecture  4: The sample mean (chapter 5.1-5.4, 5.7-5.8)
Lecture  5: Statistical inference based on the sample mean using estimation and confidence intervals (chapter 5.5-5.6, 6.1-6.3)
Lecture  6: Statistical inference based on the sample mean using t tests (chapter 6.4, 6.6)
Lecture  7: Statistical inference generalizations (ch.7.1-7.2)

Lecture 8: ***** First midterm exam  *****

C. Bivariate: The relationship between two economic variables
Lecture  9-10: Bivariate data summary: scatter plots, correlation and regression (chapter 8.1-8.10)
Lecture 11: The least squares estimates (chapter 9.1-9.3, 9.5)
Lecture 12: Statistical inference on regression coefficients (chapter 9.4, 10.1-10.9)
Lecture 13: Bivariate case studies (chapter 11.3)

Lecture 14: ***** Second midterm exam *****

Lecture 15: Data Transformations for bivariate regression (chapter 12.3-12.4, 12.6, 4.1-4.3)

D. Multivariate:
The relationship between more than two economic variables
Lecture 16: Multiple regression (chapter 13.1-13.8)
Lecture 17: Inference for multiple regression (chapter 14.1-14.9)
Lecture 18: Case study (chapter 15.1)
Lecture 19: Data Transformations for multivariate regression (chapter 16.1-16.6)
Lecture 20: Model Checking (chapter 17.3-17.8)

Required Materials:

Lecture Notes for Analysis of Economics Data 102. Available at Davis Copy Maxx (Phone: (530)758-2311 at 232 Third Street - corner of 3rd and University Avenue). This material will not be posted on the web. These lecture notes cover the entire course. (Earlier versions of the notes may also be adequate if Fall 2012 or later, though there have been substantial recent changes.)
The datasets and Stata programs used in the course notes are at

Additional Materials:
The course Canvas site has assignments.
The website has datasets, Stata programs, Stata introduction and past exams and solutions.
There are usually free tutors for 102: see
The Khan Academy has excellent video tutorials and exercises. See

Reading List:  
Topic Lecture Notes
1. Univariate Chapters 1-7, Appendix A-B

2. Bivariate Chapters 8-12
3. Multivariate Chapters 13-16

4. Additional topics Chapter 17

Discussion Sections:
These are held in a university computer lab in Hutchison 93.

Computer Materials: Assignments use Stata.

Stata is installed in 93 Hutchison, 2101 SCC, and the Virtual Lab (after 2060 SciLab closes - see
To see whether 93 Hutchison and 2101 SCC are available see

If you choose to purchase Stata go to 
Stata/IC is more than adequate and costs $45 (6 months), $89 (1 year); $198 (permanent copy). Note that ECN140 and some other courses such as ECN 132 also use Stata.

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 are lengthy and it is easiest to cut and paste them in).

To get started in Stata see

Assignments and Exams:

The best way to learn the material is to attend class, do the assignments, and continually engage in the course material.
To this end I have added seven quizzes to the class (the best five are counted towards the final grade).

Assignments:           5%       Due 10.30 a.m. Thursdays  January 17, 24; February 7, 14; March 7, 14.
Quizzes:                   5%       10.30 a.m. Thursdays  January 17, 24; February 7, 14; February 28; March 7, 14.
Midterm Exam1:   22.5%    Thursday January 31    
Midterm Exam2:   22.5%    Thursday February 21  

Final Exam:           45%       Thursday March 21   3.30 p.m.-5.30 p.m.   
Comprehensive (about half on material up to 2nd midterm and about half the remainder).

Bring SCANTRON for quizzes and for the final exam.

are posted on Canvas under Files / Homeworks.
Assignments will be graded satisfactory (2 points) or unsatisfactory (0 points). Full solutions will be distributed. Satisfactory means a serious attempt to answer at least 80% of the questions. The lowest of the scores on the six assignments is dropped, i.e. no penalty for not handing in one assignment if the other five are graded satisfactory. No credit for late assignments.

Quizzes: these will generally be five multiple choice questions. Bring scantrons.
The lowest two of the scores on the seven quizzes is dropped. (So e.g. no penalty for not doing two of the quizzes if the other five received perfect scores.) 

Academic honesty is required - see below.

Exams are closed book with a mixture of short answer (about two-thirds) and multiple choice (about one-third) questions.
Some past exams and solutions are posted on Canvas.


Scores are posted at Canvas. You have one week from when work is first returned in class to raise any questions about grading.

Note that there is no automatic conversion formula such as an 85 is a B. Instead if 85 was the median (middle) score among all students who took the class then you would get the median grade which is most often B-. To let you know how you are going on each exam I give the distribution of the scores for the exam along with a "suggestive" grading curve. But the course grade is based on a course curve.

Course grade is determined by the total score, with weights given above.
The assignments are graded on a generous scale (satisfactory or unsatisfactory), so most students will get full credit on the assignment portion. Therefore for most students the course score is determined by scores on the assignments and exams.
To indicate your progress I give a grade on each midterm. But the final grade is determined by summing the exam and assignment scores (and not by averaging the grades).

Grading policy: To ensure fairness and consistency in grading, the Department expects that the GPA in all undergraduate economics courses will average 2.7. For example, a distribution with 20% A's, 50% B's, 15% C's, 10% D's, and 5% F's could be consistent with an overall GPA of 2.7.


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 102 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.

The most common form of academic misconduct in Economics 102 is copying from past assignment solutions or copying
(close to exact or exact) from other students. The most common penalty for doing so will be to receive zero for that assignment and additionally having your course grade reduced by one grade (examples: a B becomes a C, or a B- becomes a C-).