ECN 102 A01-A04:  ANALYSIS OF ECONOMICS DATA
  SYLLABUS
Department of Economics, University of California - Davis
FALL 2022

NOTE: I am teaching both 102 A01-A04 (this class) and 102 B01-B04

THE DEPARTMENT'S GOAL IS TO RETURN TO A PRE-COVID IN-PERSON STUDENT EXPERIENCE.
ACCORDINGLY, TEACHING WILL BE IN PERSON AND EXAMS WILL BE IN CLASS. RECORDINGS WILL NOT BE AVAILABLE.

The text is
Analysis of Economics Data: An Introduction to Econometrics.
You can obtain a paperback version in one of two ways.
- free from the UCD bookstore if you chose to participate in UCD Equitable Access (cost $169 per quarter)
- for $25 plus shipping directly from Amazon
You can obtain a pdf version of the exact same text in the following way
- for $6.99 directly from Amazon for a "Kindle replica book" (this can be read on a computer after downloading Kindle Reader software from Amazon).

The statistical package used is Stata.
Stata is also available free in university computer labs.
More conveniently, a personal student license costs $48 (6 months), $94 (1 year); $225 (permanent copy).

Instructor:
Professor Colin Cameron
1124 Social Sciences and Humanities
email: accameron@ucdavis.edu
website:  http://www.econ.ucdavis.edu/faculty/cameron/

Meeting:
Tues Thurs 10.30 - 11.50 a.m. Wellman 126

Instructor Office Hours:
Tuesday afternoon       3.30 - 5.00 pm in office SSH 1124   In person only
Wednesday afternoon  3.30 - 5.00 pm  in office SSH 1124  In person plus zoom

Teaching Assistants: 

Kathya Tapia  kattapia@ucdavis.edu  
Office hours: Wednesday 11.00 am - 1.00 pm in SSH 0120   In person only

Rebecca Brough  rjbrough@ucdavis.edu  
Office hours: Monday 11.00 - 1.00 pm       in SSH 0120   In person only

Discussion Sections in Hutchison 93:
A01: Tuesday 1.10 - 2.00 pm  93 Hutchison   TA: Kathya Tapia
A
02: Tuesday 2.10 - 3.00 pm  93 Hutchison   TA: Kathya Tapia
A03: Tuesday 3.10 - 4.00 pm  93 Hutchison   TA: Rebecca Brough
A04:
Tuesday 4.10 - 5.00 pm  93 Hutchison  TA: Rebecca Brough

Course Goals:

(1) Review univariate statistics and then focus on regression analysis for the relationship between a single variable and one or several explanatory variables.
(2) Apply these methods to key economics data using the econometrics package Stata.
(3) Provide a bridge between introductory statistics and more advanced data analysis courses, e.g. between Statistics 13 and Economics 140. Pre-requisites:

Pre-requisites:
Economics 1A-B, Statistics 13, 13Y or 32 and Math 16A-B or 17A-B or 21A-B or consent of instructor.
The essential pre-requisites 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.
For more on possible data classes see http://cameron.econ.ucdavis.edu/e102/morestat.html

Topical Outline by Lecture Number
:

The text is A. Colin Cameron, Analysis of Economics Data: An Introduction to Econometrics.

A. UNIVARIATE: Analysis of a single economics variable
Lecture 1: Introduction and getting going on Stata  (chapter 1 + Stata http://cameron.econ.ucdavis.edu/stata/stata.html).
Lecture  2: Summarizing data using descriptive statistics and visualizing data using charts (chapter 2.1-2.6)
Lecture  3: The sample mean (chapter 3.1-3.6 and appendix B)
Lecture  4: Statistical inference based on the sample mean using estimation and confidence intervals (chapter 4.1-4.3)
Lecture  5: Statistical inference based on the sample mean using hypothesis tests (chapter 4.4-4.7)

B. BIVARIATE REGRESSION: The relationship between two economic variables

Lecture  6: Bivariate data summary: scatter plots, correlation and regression (chapter 5.1-5.5)

Lecture 7: ***** First midterm exam  *****

Lecture 8: Bivariate data summary: regression, goodness of fit (chapter 5.6-5.12)
Lecture 9: The least squares estimator (chapter 6.1-6.4)
Lecture 10: Statistical inference on regression coefficients (chapter 7.1-7.7)
Lecture 11: Bivariate case studies (chapter 8.1)
Lecture 12: Models with natural logarithms (chapter 9.1-9.6)

Lecture 13: ***** Second midterm exam *****

C. MULTIPLE REGRESSION: The relationship between more than two economic variables
Lecture 14: Multiple regression (chapter 10.1-10.8)
Lecture 15: Inference for multiple regression (chapter 11.1-11.7)
Lecture 16: Various: Robust errors (ch.12.1), prediction (ch 12.2), case study (ch. 13.1)
Lecture 17: Indicator Variables (chapter 14.1-14.4)
Lecture 18: Regression with transformed variables (chapter 15.1-15.7)
Lecture 19: Checking the Model and Data (chapter 16.1-16.8)
Lecture 20: Review.

Required Materials:

A. Colin Cameron, Analysis of Economics Data: An Introduction to Econometrics.
The text is available from Amazon for $25 paperback or $6.99 for a Kindle replica book.
A Kindle replica book is just a pdf but requires downloading the Kindle App to your PC, Mac, Android or iOS.
Copies of the text are also on Reserve at the Library.

Slides for the course text are available at http://cameron.econ.ucdavis.edu/aed/
The datasets and Stata programs used in the course text are at http://cameron.econ.ucdavis.edu/aed/

Additional Materials:
The course Canvas site has assignments under Files/ Homeworks. Assignments should be uploaded to Canvas under Assignments.
The website http://cameron.econ.ucdavis.edu/e102/e102.html has past exams and solutions and some links to Stata material.
There are usually free tutors for 102: see http://economics.ucdavis.edu/undergrad-program/tutoring
The Khan Academy has excellent video tutorials and exercises. See https://www.khanacademy.org/math/ap-statistics

Reading List:  
Topic Lecture Notes
A. Univariate Chapters 1-4, Appendix B

B. Bivariate Regression
Chapters 5-9
C. Multiple Regression Chapters 10-11, 12.1, 12.2, 13.1, 14-16

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 https://virtuallab.ucdavis.edu)
To see whether 93 Hutchison and 2101 SCC are available see https://computerrooms.ucdavis.edu/available/. 

I strongly recommend purchasing Stata. It will make it much easier to implement and learn Stata.
Stata can be purchased at https://www.stata.com/order/new/edu/gradplans/student-pricing/ 
Stata/BE is more than adequate and for a student license costs $48 (6 months), $94 (1 year); $225 (permanent copy).
Note that ECN140 and some other courses such as ECN 132 also use Stata.
Older versions of Stata are fine.

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 http://cameron.econ.ucdavis.edu/stata/stata.html
This video provides directions: Connect_virtual_lab_and_start_Stata.mp4

The best ways to succeed in this class are

  1. Read the chapters in the text before lecture
  2.  Come to lecture and take notes.
  3.  Try the homework assignments on your own
  4.  Go to discussion section where the TA will go over the homework and ask questions
  5.  Do end-of-chapter exercises and past exams, again without looking at your notes.
Assignments and Exams:

Assignments:      20%    Due 10.00 a.m. Fridays  September 30; October 7, 21, 28; November 18; December 2.
Midterm Exam1:  20%    Thursday October 13
Midterm Exam2:  20%    Thursday November 3
Final Exam:          40%    Thursday December 8   6.00 p.m.- 8.00 p.m.    Comprehensive (about half on material up to 2nd midterm and about half the remainder).

Bring SCANTRON for exams.

Assignments: are posted on Canvas under Files / Homeworks. My solutions will also be posted there.
Assignments solutions by you are to be posted (as a pdf) on Canvas under Files / Assignments.

Assignments will generally be graded satisfactory (4 points) or unsatisfactory (0 points); very occasionally partial credit will be given.
Satisfactory means a serious attempt to answer at least 80% of the questions.
Full solutions will be distributed.
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.

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.

FOR EXAMS YOU NEED TO BRING STUDENT PHOTO ID. I WILL DECIDE WHERE TO SEAT YOU. YOU CANNOT USE YOUR OWN CALCULATOR OR SMARTPHONE - CALCULATORS WILL BE PROVIDED.

Scores are posted at Canvas. You have one week from when work is first returned in class to raise any questions about grading.
AFTER THE FINAL EXAM IS TAKEN NO CHANGES WILL BE MADE FOR ANY REASON TO ANY SCORES RECORDED ON CANVAS.

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 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).

AFTER THE FINAL EXAM IS TAKEN NO CHANGES WILL BE MADE FOR ANY REASON TO ANY SCORES RECORDED ON CANVAS.

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 sja.ucdavis.edu/files/cac.pdf . 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.
You are to indicate on the solution the names of the other students you worked with, if any that you worked with you on the problem set.
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 (in addition to reporting to SSJA) 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-).