ECONOMETRIC FOUNDATIONS
ARE/ECN 239
  DRAFT SYLLABUS

Department of Economics

University of California - Davis
Fall 2017

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

Teaching Assistant:
Johannes Matschke 
jcmatschke@ucdavis.edu    SSH ??
Office Hours??

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.

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.