Econometrics

Summer 1999

Instructor:

Lee C. Adkins
Professor of Economics
Oklahoma State University

Address:

Department of Economics
College of Business Administration
Oklahoma State University
Stillwater, OK 74078
USA

Email:

lee.adkins@okstate.edu

Web Page

http://adkins.bus.okstate.edu

1  Objective

2  Recommended Textbooks

Judge, Hill, Griffiths, Luktepohl, Lee, Introduction to the Theory and Practice of Econometrics, John Wiley & Sons, 1988.

Kennedy, Peter, A Guide to Econometrics 4th edition, MIT Press.

Greene, William H., Econometric Analysis, Prentice Hall, 1997.

• Errata can be found at:
  http://www.stern.nyu.edu/ ~ wgreene/errata.htm.
  This information is quite important due to the relatively large number of errors found in the text.
• Data Sets found in the test are located at:
  http://www.stern.nyu.edu/ ~ wgreene/datasets.htm.
  This site is also very useful since it is the source of all the data used for homeworks in this course.

Other Resources

SAS/IML User's Guide, Version 6

SAS/IML Software: Changes and Enhancements through Release 6.11

SAS/ETS User's Guide, Version 6, Second Edition

Fomby, Hill, and Johnson, Advanced Econometric Methods, Springer Verlag, 1984. Second Printing, 1988.

Jan Kmenta, The Elements of Econometrics.

Judge et al., The Theory and Practice of Econometrics, 2nd Edition, Wiley, 1985.

Schmidt, Peter, Econometrics, Marcel Dekker, 1976.

3  Prerequisites

4  Course Outline

1. Introduction
2. Statistical Inference: Estimation and Hypothesis Testing (Chapter 3, ITPE II)
3. Classical Linear Regression Model (Chapters 5 & 6, ITPE II; Chapters 3 & 4, Kennedy)

   1. Assumptions
   2. Estimators

      1. OLS estimator
      2. Maximum likelihood Estimator
      3. Using OLS when Gauss-Markov assumptions are violated
   3. Hypothesis Testing and Confidence Intervals (Section 6.4, ITPE II and Section 7.2, Greene)

4. Using Prior Information in Regression (Chapter 12, Kennedy)
   1. Restricted Least Squares (Section 6.2, ITPE II and Section 7.3, Greene)
   2. Specification Analysis (Section 8.4, Greene; Section 20.4, ITPE II; and Chapter 5, Kennedy)
   3. Biased Estimators and Pretests (Section 8.5, Greene; Section 20.3, ITPE II)

5. Wrong Regressors, Nonlinearities, and Parameter Inconstancy (Chapter 6, Kennedy)

   1. RESET
   2. Tests of Structural Change (Sections 7.6-7.8, Greene)
   3. Testing Nonlinear Restrictions (Section 7.9, Greene)
   4. Nonnested Hypothesis Tests (Section 7.10, Greene)

6. Dummy Variables (Section 8.2, Greene)
7. General Linear Regression Model (Sections 11.1-11.2, Greene)

5  Computer Assignments

GAUSS is a mathematical programming language that is similar to SAS IML. In fact GAUSS is superior to IML in many respects. SHAZAM is another software package that contains preprogrammed regression routines. Although I will not require you to learn either of these software packages, I want you to feel free to experiment with them. Both are very powerful in their respective specialties.

Although I will assign a number of homeworks during the course, I want you to mail them in to me after the course is complete. This means that you will have plenty of time to complete the assignments.

Here is the link that will take you to the homework assignments:
Homework Problems.
This is a pdf file so you will have to have the Adobe Acrobat reader installed on your computer to use it. Here is a link to the Adobe site: Get Acrobat Reader. Near the bottom of the page there is a "Get Acrobat Reader" button. Click on it and follow the instructions to begin the download.

6  Grades

Grades will be assigned based on the following scale.