Instructor:
Professor Colin Cameron
SSH Building 1124   752-8396
accameron@ucdavis.edu

Meeting:
Mon Wed 10.00 – 11.50 a.m. PhyGeo 130
3 hours lecture and one hour discussion

Office Hours:
Tues 1.30 p.m. - 3.00 p.m.
Thurs 9.00 a.m. - 10.30 a.m.

Teaching Assistant:
Juan Du: SSH 0115 jdu@ucdavis.edu 754-8074
Office hours: Tuesday 10.00 a.m. - noon

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: Solid foundation in statistics (the listed pre-requisite is Econ / Ag Econ 239 and also admission to the Ph.D. requires undergraduate statistics). Previous exposure to linear regression is highly desirable. If you have not seen linear regression before then work hard especially in the first two weeks to come up to speed.

Course Outline:
 

Classes 1-2	2 classes	Basic Two Variable Linear Regression Model
		Greene Not covered     J&DN 1.1-1.5
Class 3	1 class	Matrix Algebra
		G App.A.1-A.4   J&DN App.A.1.1-A.2.10, A.2.14
Classes 4-6	3 classes	Linear Regression with Matrix Algebra
		G Ch.2.1-2.3, 3.1-3.2, 3.5    J&DN Ch.3.1, 3.4.1-3.4.4
Class 7	1 classes	Prediction
		G Ch.6.6     J&DN Ch.3.5
Class 8	1 class	Wald F-Test of Linear Restrictions
		Gr Ch.4.1-4.8     J&DN Ch.3.4.5, 4.5
Class 9	1 class	Distribution of Quadratic Forms and F-Test Derivation
		Gr App.B.10-B.11     J&DN App.B.6-B.10
Class 10	1 class	Midterm Exam

 

Class 11	1 class	Estimation Subject to Linear Restrictions
		G Ch.6.3     J&DN Ch.3.4.7
Class 12	1 class	Data Transformation and Indicator Variables
		G Ch.7.2     J&DN Ch.2.2, 4.6
Class 13	1 class	Model Specification Error
		G Ch.8.1-8.2       J&DN Ch.4.1-4.3
Classes 14-15	2 classes	Statistical Inference in Large Samples (asymptotic)
		G Ch.5.1-5.2, 6.4     J&DN Ch.2.4, 5.1, 5.2
Class 16	1 class	IV Estimator
		G Ch.5.4     J&DN Ch.5.5
Class17-18	2 classes	GLS and Heteroskedasticity and autocorrelation
		G Ch.10.5, 11.2, 11.6      J&DN Ch.5.4, 6.1-6.5

Required Material:

Greene, W.G. (2003), Econometric Analysis, 5th edition, Prentice-Hall.
OR
Johnson, J. and J. Dinardo (1997), Econometric Methods, 4th edition, McGraw-Hill.

Greene is the standard text for this course at any economics Ph.D. program. The weakness of Greene is that it is as much a reference source as it is a textbook, making it challenging especially for the introductory material. Johnson and Dinardo is a good text for this course (my notes are drawn more from Johnson and Dinardo than from Greene) but has less on other methods down the line.

Bottom line: Stronger students get Greene and other studetns get Johnson and Dinardo.

Additional Material:

I may also make available some lecture notes.  In particular, my book Microeconometrics is more advanced than this course but I may give you copies of selected portions.

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. Most of the time 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 Wednesdays Jan 11, 25, Feb 1, 22, March 1, 8, 15.

Midterm 32%
Wednesday Feb 8   10.00 – 11.50 p.m.  Closed book exam.

Final 50%
Tuesday  March 21  8.00 – 10.00 a.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.