Instructor:
Professor Colin Cameron
SSH Building 1124 752-8396
accameron@ucdavis.edu
Meeting:
Tues Thurs 10.00 – 11.50 a.m. Wellman 115
3 hours lecture and one hour discussion
Office Hours:
Tues 3.30 p.m. - 5.00 p.m.
Wed 3.30 p.m. - 5.00 p.m.
Teaching Assistant:
Yingxue Li SSH 0141 yxli@ucdavis.edu
Office hours: Wednesday 9.30 - 11.30 a.m.
Course Goals: (1) To be able to perform estimation and testing in basic linear cross-section regression models, (2) to understand the theory underlying ordinary least squares (OLS) using matrix algebra. This course is the foundation for any subsequent regression / econometrics class.
Pre-requisites: A solid foundation in statistics is required (the listed pre-requisite is ECN/ARE 239). Additionally I assume previous exposure to linear regression and to matrix algebra. If your preparation in these areas is thin then work hard in the first two weeks to come up to speed.
Course Outline:
| Class 0 |
Basic two variable linear regression model
is assumed. |
|
| Classes 1-3 | 3 classes | Least Squares Regression with
Matrix Algebra Greene 7th Ed 2.1-2.4, 3.1-3.7, Appx A.1-A.4 |
| Class 4-6 | 3 classes | Finite Sample Properties of
Least Squares Greene 7th Ed 4.1-4.3 |
| Classes 7-9 |
3 classes | Large Sample Properties of
Least Squares Greene 7th Ed 4.4, Appx D.1-D.4 |
| Class 10 |
1 class | Midterm Exam |
| Class 11-12 |
2 classes |
Tests of Linear Restrictions Greene 7th Ed 5.1-5.6, Appx B.10-B.11 |
| Class 13-14 | 2 classes | Practical Issues Greene 7th Ed 4.7 (data problems), 4.6 (prediction), 6.2-6.3 (data transformation and indicator variables) |
| Class 15 |
1 class |
Model Specification Error Greene 7th Ed 4.3, 8.2 |
| Class 16 |
1 class |
Instrumental Variables (IV) Estimation Greene 7th Ed 8.1-8.3 |
| Class 17 | 1 class | Maximum Likelihood (ML)
Estimation Greene 7th Ed 14.1-14.3, 14.9.1 |
| Class18 |
1 class |
Generalized Least Squares (GLS) Greene 9.3 |
| Class19 |
1 class |
Heteroskedasticity Greene 9.3-9.6 |
| Class20 |
1 class |
Autocorrelation Greene 20.1-20.5, 20.7-20.9 |
Required Material:
Greene, W.H. (2011), Econometric Analysis, 7th edition,
Prentice-Hall.
Older editions are fine, even much older editions are fine. My
assignment questions do not come from Greene.
Over time, however, Greene is better organized and brings in newer
material.
If you have money, buy the 7th edition. If you don't then use an
earlier edition.
Greene is the standard text for this course at any economics
Ph.D. program. Greene is as much a reference source as it is a
textbook, and the chapter sections given above include in places
more material than we will cover. What you really need to know is
the material I cover in lectures.
Especially for those with a thin background in econometrics a
more introductory book will be helpful.
Undergraduate texts such as Stock and Watson or Wooldridge are
good, but they do little matrix algebra which is a big part of
this course.
A book with matrix algebra I like is Johnson, J. and J. Dinardo
(1997), Econometric Methods, 4th edition, McGraw-Hill, but
this is out of print.
Additional Material:
Assignments and datasets and exam scores will be posted at
Smartsite.
Also at
http://www.econ.ucdavis.edu/faculty/cameron/e240a/e240a.html I
have provided a review of bivariate regression; some other lecture
notes (especially on asymptotic theory); and past exams and
solutions.
Computer Materials:
This course will use STATA (www.stata.com ), the leading all-purpose econometrics package for analysis of cross-section data and short panels. Often complete programs will be provided and the assignments will concentrate on interpretation of the output. Stata is available on both PC and Unix platforms for ECN and ARE students.
Course Grading:
Assignments 18%
Best 6 out of 7. Last assignment is compulsory. Each worth 3%.
Due Thursdays Jan 17, 24, Feb 5 (Tues), 21, 28, March 7, 14.
Midterm 32%
Thursday Feb 7 10.00 – 11.50 p.m. Closed book exam.
Final 50%
Thursday March 21 3.30 – 5.30 p.m.
Comprehensive. Closed book exam
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.
Academic integrity is required. What is academic integrity? See
the UCD Student Judicial Affairs website http://sja.ucdavis.edu/integ.htm.
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.