Download ECON 210: ECONOMETRICS A Course Description Course

ECON 210: ECONOMETRICS A
Department of Economics, The University of Chicago
Winter 2002
José María Liberti
E-mail: [email protected]
TA: Ricard Gil
E-mail: [email protected]
Course Description
Econometrics applies statistical methods and economic theory to the analysis of economic phenomena. Much of this introductionary course is necessarily devoted to the basic statistical techniques used by econometricians: estimation and hypothesis testing in the classical single equation
linear regression model, corrections of these methods for some typical violations of the classical
assumptions, and identification and estimation of simultaneous equations models. However, we will
pay ample attention to the application of these techniques to various economic problems, using real
data sets and the software package STATA.
Course Objectives
Students are to: (1) gain experience in working with economic data; (2) gain an understanding
of the econometric techniques for the analysis of economic data; (3) develop their ability to critically
evaluate empirical research; (4) gain experience in estimating different types of econometric models.
Textbook
Gujarati , D.N. (1995), Basic Econometrics, Third Edition, McGraw-Hill, New York.
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Organization
Up-to-date Information: All class material, problem sets and announcements will be posted on
the course web page at http://chalk.uchicago.edu. Please check regularly!
Time and Location: Lectures are held on Tuesday & Thursday 10:30-11:50 AM in Room Memorial Harper 130, Harper Building. TA Sessions will be held on Thursday 5:00-6:30 PM in Hinds
101.
Office Hours: I will hold Office Hours on Tuesdays 7:00-8:30PM on Regenstein Library 3rd.floor
and by appointment (send e-mail). Questions regarding any of the material can be sent to me by
e-mail. If you have any specific and quick question regarding the material seen in class you can
ask me directly when you see me anytime in the 3rd floor of the Regenstein Library. In addition,
Ricard Gil will hold Office Hours on Mondays at Cobb Room 206 from 5:00-6:30PM and answer
questions by e-mail as well. More details will be announced in class as we go.
Exams: Midterm exam on Thursday February 7 in class on all material covered up to Thursday
January 31. Comprehensive final exam will take place on the Final Exam Week for the College.
The Exam is scheduled for Tuesday March 12 between 10:30AM-12:30PM. The time of the Final
Exam is unchangeable. All exams will be closed books and closed notes.
Problem Sets:There will be 6 to 7 problem sets depending on the timing of classes and how far we
make it with the program. Problem Sets will be due every Thursday during the morining Lecture
Session. Late problem sets will not be accepted! Problem sets handed in after the deadline
will receive automatically the grade zero, although the TA will correct your assignment. There
will be no deviation from this rule. You can collaborate on problem sets, but you must hand in
your own answers. There will be some additional Problem Sets in order for you to practice for
the Mid-Term and Final Exam. These supplementary problem sets and exercises will not count for
your final grade of the course.
Grading: There will be weekly assignments, a midterm exam and a final. They will count toward
the grade as follows: problem sets (20%), midterm (30%) and the final (50%).
Regrading: Requests for regrading of an assignment or test must be in writing and revisions may
be up or down on the entire assignment or test.
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Course Outline
This is a tentative schedule per week which I intend to follow.
1. Overview of econometrics, quick review of probability and statistics.
2. Statistical Inference: point estimation and confidence interval estimation. The normal linear
model.
3. The classical simple linear regression model assumptions and OLS estimation.
4. The normality assumption and maximum likelihood estimation.
5. Hypothesis testing.
6. Extensions. Introduction of dummy explanatory variables, scaling and functional forms.
7. Classical multiple linear regression model. Introduction and estimation.
8. Violations of classical assumptions. Multicollinearity, heteroscedasticity and autocorrelation.
9. Specification errors.
10. Simultaneous equations. Identification. Estimation.
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