Pols 603-600: Spring
B. Dan Wood
Time: 7:00-9:50 p.m. Tuesday
Office: 2098 Allen Building
Room: 2064 Allen Building
Office Hours: 4:00-4:30 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.
Greene, William H. 2008. Econometric Analysis, Sixth Edition. New York: Prentice-Hall.
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 non-linear 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, 19.1-19.9, 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 23.3-23.4; 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 12-13; 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.