**ECONOMETRIC FOUNDATIONS
ARE/ECN 239
DRAFT SYLLABUS**

Department of Economics

**Instructor:**

Professor Colin Cameron

SSH Building 1124

accameron@ucdavis.edu

**Meeting:
**

Tues-Thurs 1.40 - 3.00 pm Wellman 001

Discussion To be arranged. Most likely Friday.

**Office Hours: **

Tuesday 9.00 - 10.20 am

Wednesday 2.00 - 3.30 pm

Johannes Matschke jcmatschke@ucdavis.edu SSH ??

Office Hours: ??

Course Goals:

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

NOTE: IF YOU HAVE NOT DONE AN UPPER DIVISION UNDERGRADUATE
SEQUENCE IN PROBABILITY AND STATISTICS THEN YOU WILL FIND MUCH
OF THE MATERIAL NEW AND SHOULD GET A LOWER LEVEL STATISTICS
TEXT (LISTED BELOW) IN ADDITION TO THE COURSE TEXT.

**Course Outline****:** **Draft.**

1. Introduction to Probability and Statistics
(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
2.6-2.7 (Slides 3)

9. Multivariate distributions HMC
2.8, 3.5 (Slides 3)

10. Statistical Inference
Introduction: Sampling, Statistics, Estimation HMC
4.1 (Slides 4)

11. Midterm
exam

12. Statistical Inference Introduction: Confidence
Interval HMC 4.2-4.3 (Slides 4)

13. Statistical Inference Introduction: Hypothesis Tests HMC
4.5-4.6 (Slides 4)

14. Monte Carlo procedures HMC
4.8-4.9 (Slides 5)

15. Convergence in Probability,
law of large numbers HMC 5.1 (Slides 6)

16. Convergence in Distribution, central limit
theorem HMC 5.2-5.4 (Slides 6)

17. Maximum Likelihood: Point
estimation HMC 6.1 (Slides
7)

18. Maximum Likelihood: Efficiency HMC 6.2 (Slides 7)

19. Maximum Likelihood: Hypothesis Testing HMC
6.3 (Slides 7)

20. Brief discussion: Point estimation: Sufficiency HMC
7.1-7.5 (Slides 8)

20. Brief discussion: Hypothesis Tests: Most powerful tests HMC
8.1-8.3 (Slides
9)

**Required Material:
**

Course slides available at Canvas, filed under Files.**
**

Text: Hogg, McKean and Cragg (2013), Introduction to Mathematical Statistics, Seventh Edition, Pearson.

Much of the class will follow this book. The book is often more detailed than what will be covered in this class.

**Recommended Material:**

**The book is more advanced than an undergraduate text and has
relatively few real data examples. It is "mathematical
statistics" not "statistics".**

** I ****very strongly recommend ****also having an
undergraduate probability and statistics text that presents
material more simply and with more examples. **

** **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.

The older the edition the cheaper the book.

The most commonly-used Ph.D. level text is Casella and
Berger (2002), Statistical Inference, Second Edition, Duxbury.

This is more advanced than Hogg, McKean and Cragg.

**Computer
Materials:**

Assignments will include both theory and data examples using
STATA.

STATA is available on both Econ and ARE computers (http://www.ssds.ucdavis.edu/computing/computing).

If you choose to purchase Stata go to http://www.stata.com/order/new/edu/gradplans/student-pricing/
Get the Stata/IC version.

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

Course material will be posted at http://canvas.ucdavis.edu with
slides filed under Files

**Course Grading:**

Assignments 18%

Best 6 out of 7. Last assignment is compulsory. Each worth 3%.

Due Tuesdays October 10, 17, 24, Oct 31,
November 14, 21, and Thursday December 7.

Midterm 32%

Thursday November 2 in
class

Final 50%

Tuesday December 12 1.00 - 3.00 pm **Comprehensive.**

Assignments
must be handed in on time, so solutions can be discussed in
class and distributed in a timely manner.

No credit for late assignments. Lowest assignment score is
dropped.

Academic integrity is required. What is academic integrity?
See the UCD Student Judicial Affairs website http://sja.ucdavis.edu/cac.html

As an exception to their rules, I permit some collaboration
with other students in doing assignments, but the work handed
in must be your own. Each person must create their own Stata
output and write up their own
answers. And you are to write on your assignment the name of
the person(s) you worked with.

Exams will be closed book. The final exam is comprehensive.