ECONOMETRIC FOUNDATIONS
ARE/ECN 239
  Department of Economics

University of California - Davis
Fall 2019
SYLLABUS

Instructor:
Professor Colin Cameron
SSH Building 1124  
accameron@ucdavis.edu

Meeting:
Tues-Thurs 12.10 - 1.30 pm  Creuss 107
Friday 12.10 - 1.00 pm  Olson 147  

Office Hours:
Monday      12.30 - 2.00 p.m.
Wednesday  3.30 - 5.00 p.m.

Teaching Assistant:
Yumeng Gu   ymgu
@ucdavis.edu

Office hours: Thursday 2:00 - 4:00 PM at SSH 0116.


Course Goals:
To provide the probability and statistical foundation for Ph.D. level coursework in econometrics and in economics.

Pre-requisites: Upper division undergraduate sequence in probability and statistics, econometrics and linear algebra.

Course Outline:

1. Probability theory: sets, basics, conditional probability  CB 1.1-1.3 and Supplemental Notes
2. Random variables, distributions  CB 1.4-1.6
3. Transformations and expectations  CB 2.1-2.2
4. Moments, moment generating functions CB 2.3-2.4
5.
Commonly-used distributions, inequalities  CB 3.1-3.6
6. Bivariate random variables: joint and marginal distributions, transformations, conditional distributions  CB 4.1, 4.3, 4.2 
7. Bivariate random variables: conditional distributions, inequalities CB 4.4-4.5, 4.7
8. Multivariate random variables: CB 4.6  and Supplemental Notes
9. Linear Regression   Supplemental notes
10. Midterm exam 
11. Random samples and properties of the sample mean CB 5.1-5.4
12. Convergence in probability, law of large numbers  CB 5.5.1-5.5.2
13. Convergence in distribution, central limit theorem  CB 5.5.3-5.5.5
14. Maximum likelihood estimation  CB 6.2.1, 6.3.1, 10.1
15. Maximum likelihood tests   CB 10.3
16. Point estimation: Methods, Efficiency and Sufficiency  CB 7.1-7.3  (selected parts)
17. Point estimation (continued)
18. Hypothesis Testing  CB 8.1-8.3  (selected parts)
19. Hypothesis Tests and confidence intervals  CB 9.1-9.3 (selected parts)
20. Monte Carlo procedures  CB 4.8-4.9 

Required Material:

Casella and Berger (2002), Statistical Inference, Second Edition, Duxbury.
This is a different text from past years. It is the most commonly-used text in Ph.D. economics courses. I follow it closely, especially for probability.
We will cover chapters 1-5 in considerable detail and sequentially, and then selected parts of chapters 6-10, not sequentially.
I assume that you have access to this book. One copy should be on two hour reserve at Shields Library and one copy is at ARE library reserve.

You need a hardcopy text. This will be your reference book to go to when needed in subsequent classes and in research. there is no perfect text for everyone.
(1) Student's background varies enormously for this class, and so will the level of book best suited to the student.
(2) We have one quarter whereas the books are intended for a full-year course.
For less prepared students, in particular for those who have not taken an undergraduate upper-division sequence in probability and statistics such as UCD's STA 130A-B or 131A-C you need to  have an undergraduate probability and statistics text that presents material more simply and with more examples. Old editions are fine and are cheap. Two such books are
Robert V. Hogg and Elliot Tannis, Probability and Statistics, Pearson.
Richard Larson and Morris Marx, Introduction to Mathematical Statistics and its Applications.

 

Some supplemental notes on selected topics will be posted on Canvas under Files / Supplemental Notes.
These will mostly be on statistical inference. For probability I follow the textbook very closely.

Computer Materials:

Assignments will include both theory and data examples using STATA.
STATA is available on both Econ and ARE computers, as well as some campus computers. it is used in subsequent econometrics classes.
For Economics students see https://www.ssds.ucdavis.edu/form/account-request for account on the L&S Social Sciences Computational Research Service server Painter. See also https://www.ssds.ucdavis.edu/computing-0
For ARE students use ARE computer server.
For campus computers see virtual lab  http://ats.ucdavis.edu/services/iet-virtual-lab/ and 93 Hutchison and 2016 SciLab if available - see  http://computerrooms.ucdavis.edu/available/
If you choose to purchase Stata go to http://www.stata.com/order/new/edu/gradplans/student-pricing/  The basic Stata/IC version is adequate.


To get started with Stata see http://cameron.econ.ucdavis.edu/stata/stata.html

Course material will be posted at http://canvas.ucdavis.edu with slides, assignments, and past exams filed under Files. I will also post, after each class, a pdf of what I write during the class.

Course Grading:

Assignments  21%
Best 7 out of 9. Last assignment is compulsory. Each worth 3%.
Due Tuesdays October 1, 8, 15, 22, 29, November 12, 21 (Thurs), 26 (Tues), and Friday December 6 (4pm).

Midterm exam (closed book)  29%
Tuesday October 29 in class

Final exam (closed book)  50%
Tuesday December 10  1.00 - 3.00 pm  Comprehensive.

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 and asks that these be included in course syllabi.
(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 239 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.

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. 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.