Econometrics
Required readings
Pindyk, Robert S. & Daniel L. Rubinfeld, Econometric Models & Economic Forecasts, Fifth Edition, McGraw Hill, New York, 1997.
Wykoff, Frank C., Econometrics Handouts, 1 - 22.
Reference Works
Berndt, Ernst R., The Practice of Econometrics: Classic and Contemporary, Addison-Wesley, Reading, MA., 1991.
Chiang, Alpha, Fundamental Methods of Mathematical Economics, Third Ed., McGraw Hill, NY, 1984.
Deaton, Angus and JohnMuellbauer, Economics and Consumer Behavior, Cambridge University Press, London, 1983.
Gilman, Leonard and Allen J. Rose, APL An Interactive Approach, Second edition, John Wiley & Sons, NY, 1976.
Intriligator, Michael D., Econometric Models, Techniques, & Applications, Prentice Hall Inc., Englewood Cliffs, NJ, 1978.
Kelejian, Harry and Wallace Oats, Introduction to Econometrics and Econometric Practice, Third Edition, Harper & Rowe, NY, 1989.
Lucas, Robert and Thomas Sargeant, Rational Expectations and Econometric Practice, University of Minnesota Press, Minneapolis, MN, 1981.
Ramsey, James B. and Gerald Musgrave, APL-STAT, Wadsworth Inc., CA, 1981.
Smith, Gary, Statistical Reasoning, 1988.
Outline
Topics Time
1. Linear regression. . . . 3 weeks
2. Matrix algebra and calculus. . . . 2 weeks
3. Probability theory & statistical inference. . . . 1 week
4. The Gauss Markov Theorem. . . . 3 weeks
5. Extensions of the Gauss Markov Theorem. . . . 2 weeks
6. Multicollinearity, heteroscdasticity and autocorrelation. 2 weeks
7. Specification analysis and experimental design. . . . 1 week
8. Two-stage least squares and instrumental variables. . . . 1 week
9. Limited dependent variables. . . . time permitting
10. Time series analysis and forecasting. .
. . time permitting
Grades and other details
Approximate weights assigned to course material in determining course grade:
Midterm 1/6
Homework 1/6
Project 1/3
Final Exam 1/3
Course grade evaluation is based on my evaluation of your effort and performance throughout the course in addition to material handed in. This includes attendance, turning in assignments on time, effort on computer language work, attitude, participation, and progress. The material in this course is inherently difficult even for the best students and you will often feel lost in the detail of the material, so I attach much importance to effort and persistence.
Since course work includes homework, independent research, data acquisition,
computer analysis as well as reading, exams, and attendance, it is important
that you know that YOU ARE TO DO YOUR OWN WORK AND SHOULD ALWAYS TURN WORK
IN ON TIME. You may certainly collaborate with one another, but you are
not to go to other faculty, graduate students, friends from other universities
and so forth for help on your work. If you do need help, I am available
and you may get tutoring if you need it, but you are responsible for work
you turn in. In short, I use an honor code in this course.