**ECONOMETRIC FOUNDATIONS
ARE/ECN 239
**

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.

Natalia Orlova norlova@ucdavis.edu

Office Hours: Monday noon - 1 pm in SSH 0116

Course Goals:

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

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.

**
**