ECONOMETRIC FOUNDATIONS
ARE/ECN 239
 
Department of Economics
University of California - Davis
Fall 2018
SYLLABUS REVISED OCTOBER 24

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      1.30 - 3.00 p.m.
Wednesday  3.30 - 5.00 p.m.

Teaching Assistant:
Natalia Orlova    norlova
@ucdavis.edu
Office Hours: Monday noon - 1 pm in SSH 0116
                        Friday 1 -2 pm  in 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. Course introduction: Overview and ML estimation  (Slides 0)
2. Probability Theory  HMC 1.1-1.2   (Slides 1)
3. Probability Theory continued HMC 1.3-1.4   (Slides 1)
4. Random Variables, distributions, transformations, expectations HMC 1.5-1.10   (Slides 2)
5. Commonly-used distributions HMC 3.1-3.4,3.6   
  (Slides 2)
6. Bivariate distributions and transformations HMC 2.1-2.2   (Slides 3)
7.
Bivariate distributions, conditional distributions and conditional expectations HMC 2.3-2.5  (Slides 3)
8. Multivariate distributions HMC 2.6-2.8, 3.5  (Slides 3)
9. Multivariate distributions HMC 2.6-2.8, 3.5  (Slides 3)
10. Linear Regression  (Slides 4)
11.
Midterm exam 
12. Convergence in Probability, law of large numbers HMC 5.1   (Slides 5)
13. Convergence in Distribution, central limit theorem HMC 5.2-5.4   (Slides 5)
14. Maximum Likelihood Estimation HMC 4.1, 6.1   (Slides 6)
15. Maximum Likelihood Estimation (continued)  HMC 6.2     (Slides 6)
16. Point estimation: Efficiency and Sufficiency HMC 6.2, 7.1-7.5    (Slides 7)
17. Hypothesis Testing: ML tests and optimality HMC 4.5-4.6, 6.3    (Slides 8)
18. Hypothesis Tests and confidence intervals HMC 8.1-8.3, 4.2     (Slides 8)
19. Monte Carlo procedures HMC 4.8-4.9   (Slides 9)  

Required Material:

YOU NEED TO HAVE A MATHEMATICAL STATISTICS TEXTBOOK !! AND THERE IS NO PERFECT BOOK.
(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.

I have recommended Hogg, McKean and Cragg, Introduction to Mathematical Statistics, Pearson.
Either the Seventh (2013) or the Eighth (2018) edition will do. They have exactly the same chapter and section numbering.
(My lecture notes are based on the seventh edition of this book).
A few days before classes start an electronic version (with try-before-you-buy access for 10 days) will be available through Red Shelf - click on the Modules tab in Canvas for access.
The electronic version for 180 days is $41.99 for the 7th edition and $86.99 for the eighth edition.

As this is available only for a limited time I would only do this if you also have another book.
The seventh edition will also be available for short-term loan at Shield Library Reserves.

For more advanced students a standard text is Casella and Berger (2002), Statistical Inference, Second Edition, Duxbury.
This is the most commonly-used text in Ph.D. economics courses but I think it is too advanced for most students (whereas Hogg et al. is viewed as the easiest of the graduate level texts).

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.

Because there is no perfect book and we have only one quarter to go through the essentials of both probability and statistics, I have made a complete set of slides for the course that will be posted on Canvas under Files / Typed lecture notes.

You still need a hardcopy text. This will be your reference book to go to when needed in subsequent classes and in research. And my lecture slides will have typos (still not completely cleaned up despite three years of teaching the material with three years of students having the opportunity to bring typos to my attention). The text books should be clean.

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 http://www.ssds.ucdavis.edu/secure/computing/waccount.html for account on Painter.
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  18%
Best 6 out of 7.
Last assignment is compulsory. Each worth 3%.
Due Tuesdays October 9, 16, 23, 30, November 13, 27, and Friday December 7.

Midterm exam (closed book)  32%
Thursday November 1
in class

Final exam (closed book)  50%
Thursday December 13  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.