Objectives:
Our aim is to develop
familiarity with a wide variety of linear statistical techniques which are
routinely used in the analysis of economic data. Where appropriate, these
techniques will be placed on a strong theoretical basis. Primary emphasis,
however, will be placed upon applications using computer assignments and a
critical analysis of the literature. As a byproduct, we hope to develop certain
theoretical and computing skills which will facilitate the mastery of
additional techniques in the future.
Organization:
Class meets twice a week
for lectures. Grades will be based 1/3 on (5 or 6) problem sets, 1/3 on a midterm
examination and 1/3 on the final.
Prerequisites:
Familiarity with calculus, linear algebra, and mathematical statistics is expected. Basic computer literacy will be needed to complete the problem sets.
Textbooks:
The lectures will come
primarily from my notes which will be
made available in a timely fashion. The basic textbook for the course will be:
Greene,
W., Econometric Analysis, 6th ed., PrenticeHall, 2008.
This book should be available at the bookstore and the following alternative references should be on reserve at the library.
Ruud, P., An Introduction to Classical Econometric Theory,
Schmidt, P., Econometrics, Marcel Deckker, 1976.
Wooldridge, J., Econometric Analysis of Cross Section and Panel Data, MIT
Press, 2002.
Computer
Programs:
We will primarily use MATLAB for examples and problem sets. MATLAB is a matrix programming language widely used in econometrics and all areas of science and engineering. It should be available on all the lab PC's on campus and a student version for installation on your personal machine can be purchased for a nominal amount. GAUSS is very similar to MATLAB, and is also widely used by economists.
Disability
Statement:
Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me during the first two weeks of class. All discussions will remain confidential. Students with disabilities should also contact Disabled Student Services in the Ley Student Center.
Outline
and Reading List:
(*Required) A. BASIC CONCEPTS 1. Nature of Econometrics 2. Useful Distributions
3. Asymptotic Theory
4. Maximum Likelihood Methods
5. Matrix Algebra
*Greene, 1,
Appendices AD
Schmidt,
Appendix
Wooldridge,
13
Ruud,
Appedices BF
Bishop, Y.M.M., S.E. Fienberg,
and P.W. Holland, Discrete Multivariate Analysis,
(MIT Press, 1975), 14
White, H., Asymptotic
Theory for Econometricians, Academic Press, 1984, 14
B.
CLASSICAL LINEAR MODEL (IDEAL CONDITIONS)
1. Bivariate Regression
2. Multivariate Regression
3. Statistical Results
4. Hypothesis Testing
5. Prediction
*Greene, 24,6,17
Wooldridge, 4
Ruud, 112
Schmidt, 1
C.
VIOLATIONS OF IDEAL CONDITIONS
1. Multicollinearity
2. Stochastic Regressors
3. Nonnormality
4. Nonscalar Covariances
5. Heteroskedasticity
6. Serial Correlation
7. Misspecification
*Notes, 914
*Greene, 5,
712
Ruud, 1320
Wooldridge
58
Schmidt,
2.12.5, 3.13.3
White, 6, 7
D.
EXTENSIONS OF CLASSICAL LINEAR MODEL
1. Combining NonSample Information
2. Nonlinear Regression
3. Binary Variables
4. Distributed Lags
5. Pooled TimeSeries CrossSection
6. Errors in Variables
*Greene, 13,
16, 1922
Ruud, 2125
Schmidt, 3.4
E. LINEAR
SIMULTANEOUS EQUATION MODEL (SEM)
1. Identification
2. Limited Information Estimation
3. Full Information Estimation
4. Inference in SEM
5. Prediction in SEM
*Greene, 14,15
Ruud, 26
Wooldridge, 9
Schmidt, 4, 5
