
Pols 603600: Spring
2010 

B. Dan Wood 
Time: 7:009:50 p.m. Tuesday 

Office: 2098 Allen Building 
Room: 2064 Allen Building 

Phone: 8451610 
Office Hours: 4:004:30 p.m Tuesday 
Purpose This course provides a more advanced treatment of
statistical methods for evaluating social science phenomena. The major topics
to be discussed include probability and distribution theory, statistical
inference and hypothesis testing, the General Linear Regression Model, the
Restricted General Linear Regression Model, analysis of covariance,
heteroskedasticity and autocorrelation, stochastic regressors, simultaneous
equations and other disturbance related regressions, multicollinearity, limited
dependent variables, and selected time series topics. The emphasis will be on
both statistical theory and application.
Course Grade The final grade will be based on four components. Weekly
homework assignments will count one fourth of the grade. Homework must be handed
in on time or no credit will be given. A midsemester and end of semester
examination will each count one fourth of the grade. The examinations will test
your skill in doing and understanding advanced statistical methods. The
remaining one fourth will be based on an empirical paper which utilizes one or
more of the methods taught in this course. You should discuss with me the paper
topic sometime before the midsemester examination. The paper is due on the last
class day before the final examination.
Prerequisites Prior to
entering the course you should have reviewed the basic principles of
probability, and also gained some background in basic linear algebra and
calculus. Good sources for these materials are the following.
Recommended preparatory texts
Dowling, Edward T. 2001. Introduction
to Mathematical Economics, Third Edition.
Spiegel, Murray, John
Schiller, and R. Alu Srinivasan. 2000. Probability and Statistics.
Second Edition.
Required texts
Greene, William H. 2008. Econometric Analysis, Sixth Edition. New York:
PrenticeHall.
Kennedy, Peter. 2008. A Guide to Econometrics. Sixth Edition. Cambridge,
Ma: MIT Press.
Note that the Solutions
Manual and Data for the Greene text are available at http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
. You might find it useful to download the Solutions Manual, since all of the
written assignments are solved there. Do NOT copy directly from the solutions
manual in homework assignments. Try doing the work first, and use them only as
a guide when you are stumped.
Topics, Readings, and Materials
Following is the order of the subjects taught in this course. Note that
there are only 12 headings, which implies that some may be given multiple week
treatments, while others may receive less than a week. I do not attach dates to
allow flexibility in timing.
1. Introduction to Statistical Models Greene, chapter 1; Kennedy, chapter 1;
DO: Fundamentals of R, R Assignment 1. Click to download Example.dat. Click here for STATA Assignment 1.
Click here for example.dta
.
2. Mathematics for Statistical Analysis Greene, Appendix A. DO: Handout problems
and computer assignments. R Assignment 2. STATA
Assignment 2.
3. Probability and Distribution Theory Greene, Appendix B; Kennedy, Appendix
A, B, and C. Do: Handout
problems. Explore the probability
distribution spreadsheets that comprise computer Assignment
Probability.
4. Statistical Theory of Estimation and Inference  Greene, Appendix C and D;
Kennedy, chapter 2. Do: Handout problems.
5. The General Linear Statistical Model Greene, chapters 2, 3, and 4; Kennedy,
chapter 3. Do: Greene, Chapter 3, Questions 4, 5, 10, 11, 12, 13 and Chapter 4,
Questions 3 and 7. Do applications 1 in chapters 3 and 4 in EITHER R or STATA.
R Program to be provided the following week.
6. Hypothesis Tests and Prediction with the General Linear Statistical Model
Greene, chapter 5; Kennedy, chapter 4. Do: Greene, Chapter 5, Questions 1, 2,
5, 6, and 9. No homework due. However, you should go over applications 1 and 3
in Either R or STATA. R Program provided.
7. Violating the Assumptions of the General Linear Statistical Model,
misspecification and nonlinear models Greene, chapters 6 and 7; Kennedy,
chapters 5 and 6. Spring break week. Light homework assignment. Do Greene,
Chapter 6, applications 1 and 2. R Program to be provided the following week.
8. Violating the Assumptions, multicollinearity, missing observations,
influential observations, and measurement error Greene, chapters 4.8.1, 4.8.2,
12.5; Kennedy, chapters 10, 12, 21. Do: Greene, Chapter 4, Question 17.
Replicate the results in Greene, example 4.6. Calculate various multicollinearity
statistics for the data matrix using either R or STATA. Calculate influence
statistics on the Longley data in this example using either R or STATA. R
Program to be provided the following week.
9. Violating the Assumptions, heteroskedasticity and autocorrelation Greene,
chapters 8, 19.119.9, 22.2; Kennedy, chapter 8. Do: Greene, Chapter 8,
Questions 6 and 12. Do Greene, Chapter 19, Question 3. Do Chapter 8 Application
1 using Either R or STATA. Do Chapter 19 Application 1. R Program to be
provided the following week.
10. Models with Discrete Dependent Variables Greene, chapter 23.323.4;
Kennedy, chapter 16. No written
assignments after this date to work on papers. Do Greene, Chapter 23
Application 1. R Program to be provided next week.
11. Violating the Assumptions, stochastic regressors and simultaneity Greene,
chapter 1213; Kennedy, chapters 9, 10, and 11. Do Greene, Chapter 13
Application 1. R Program to be provided next week.
12. Time Series Topics Greene, chapters 19, 20, 21, 22; Kennedy, chapter 19.
Do Greene, Chapter 22 Application 2. R Program to be provided next week.