Political Science 509: The Linear Model
Emory University, Spring
2005, Class No. 3305
Tarbutton
Hall 111
TuTh 1:00p-2:15p
January 20, 2005
Professor: Eric
Reinhardt
Office: 329
Tarbutton
Hall
Office hours: Th 10:00a-11:30a, & by appointment
Phone: 404-727-4977
Email: erein@emory.edu
My home page: http://userwww.service.emory.edu/~erein/
Course home page: http://userwww.service.emory.edu/~erein/courses/pols509/
Course directory: \\soc-sci.ss.emory.edu\polisci\polsclass-lab\pols_509\
Course Description &
Objectives
This
course covers basic techniques in quantitative political analysis. It introduces students to widely-used
procedures for regression analysis, and provides intuitive, applied, and formal
foundations for regression and more advanced methods treated later in the
course sequence. Unlike POLS 508, this
course will use rudimentary calculus and matrix algebra rather intensively, but
prior knowledge of such methods (beyond basic high school algebra) is not
obligatory. Rather, the course includes
brief primers on those mathematical techniques.
In terms of foundations, the course also covers some probability theory
and the properties of estimators. As its
core, the course uses these foundations to examine multivariate ordinary
least-squares regression. The course
covers model assumptions and techniques for addressing violations of those
assumptions (e.g., heteroskedasticity,
autocorrelation, multicollinearity), as well as
issues of model specification, functional forms, measurement error, and endogeneity.
Requirements
Grades
in the course will be based on the following items:
·
50% — Homework. Twelve equally-weighted assignments,
typically with a week from distribution to due date. Turn these in by the start of class. Unlike the 508 assignments, these will be
graded; they may not be pass/fail. You
may talk about the problems with your fellow students, but do not
copy another person’s homework; your submission must contain your own work. Please hand in homeworks
in hard copy, rather than emailing them, unless otherwise approved.
·
50% — Methods paper, 14-18
pages. This paper will demonstrate your
technical mastery of the practical aspects of OLS regression in the context of
a specific research problem of your own formulation. Choose a topic, develop a hypothesis, and
test it quantitatively. The only twist
here is that your dependent variable should be continuous rather than discrete,
so that it is suitable for the kinds of techniques we will be learning in
class. The format should be similar to a
“research note” in APSR or JOP, but with greater emphasis on the technical details. A one-page synopsis describing the hypothesis
to be tested and the dataset to be used is due on Tu,
Feb 22, at the start of class. Final version due Wed, May 4. Paper guidelines
may be found on the course
web page.
Course Policies
Late assignments will be penalized. Each day the assignment is late will result
in a drop of a letter grade, e.g., A to B, etc.
Reading Materials
Statistics
texts are expensive, but they are the kind of book that you’ll find yourself
referring to frequently throughout your graduate years and beyond. The main texts, available in the Druid Hills
bookstore in
·
Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach,
2nd ed. (Thomson South-Western, 2003). This is my favorite text not relying
on matrix algebra. It is particularly
good at spelling out the little steps needed to execute each test. In terms of topical coverage, it is
especially useful in dealing with simple time series methods and with the usual
violations/corrections in the panel data context. Very accessible.
·
William Greene, Econometric Analysis, 5th ed.
(Prentice Hall, 2003). A favorite among
political scientists; has many practical applications and variations of the
main models and tests (with somewhat more emphasis on cross-sectional sorts of
problems), but uses matrix algebra as the primary exposition device, and does
not spell out every single step in some cases.
A difficult first-read.
·
Early on in the course we will also use the monograph, Timothy M. Hagle, Basic Math for Social Scientists: Concepts
(Sage, 1995).
We may
also occasionally use selections from Peter Kennedy, A Guide to Econometrics,
4th ed. (MIT, 1998); John Fox, Applied Regression Analysis,
Linear Models, and Related Methods (Sage, 1997); or other sources. Check NetLibrary for free online availability for some of
these. (You can also navigate to this by
searching for them through
Like
POLS 508, this course will rely on Stata as our chief
statistical software. Just as before,
you can buy your own personal Stata license if you
wish. Call the Stata Corporation at 800-782-8272, saying that you are part
of the “GradPlan III” for
Course Outline
Jan 20 (Th)
&
Jan 25 (Tu): (1) Introduction. Course
administration. Math primer I: notation, sets, functions, limits,
differential calculus.
§
Hagle, 1-54.
§
Wooldridge, 675-695.
§
Optional: Fox, 521-523.
Jan 27 (Th)
&
Feb 1 (Tu): (2) Math primer II: more differential
calculus, optimization, integral calculus.
§
Hagle, 58-71.
§
Optional: Fox, 535-539.
ð Homework
# 1 distributed.
Feb 3 (Th)
&
Feb 8 (Tu): (3) Math primer III: probability theory
and distributions, estimators and their properties.
§
Wooldridge, 696-775.
§
Greene, 845-896, esp. 885-896.
§
Optional: Kennedy,
1-29.
§
Optional: Fox, 540-566.
ð Homework # 1 due Th
Feb 3.
ð Homework
# 2 distributed.
Feb 10 (Th)
&
Feb 15 (Tu): (4) The
regression model. Estimation and inference.
§
Wooldridge, 21-89, 95-187, 197-198.
§
Hagle, 54-56.
§
Optional: Greene, 7-71.
§
Optional: Fox, 85-94, 112-125.
ð Homework # 2 due Th
Feb 10.
ð Homework
# 3 distributed.
Feb 17 (Th): (5) Model fit and outliers. Dummy variables, interactions, regression graphs.
§
Wooldridge, 182-256.
§
Optional: Fox, 267-287, 135-154.
§
Optional: Kennedy,
221-232.
§
Michael S. Lewis-Beck and Andrew Skalaban,
“When to Use R-Squared,” The Political Methodologist 3:2 (1990), 9-11.
§
Gary King, “When Not to Use R-Squared,” The Political
Methodologist 3:2 (1990), 11-12.
§
Robert C. Luskin, “R-Squared Encore,” The
Political Methodologist 4:1 (1991), 21-23.
§
Robert J. Friedrich, “In
Defense of Multiplicative Terms in Multiple Regression Equations,” American
Journal of Political Science 26 (November 1982), 797-833.
ð Homework # 3 due Th
Feb 17.
ð Homework
# 4 distributed.
Feb 22 (Tu)
&
Feb 24 (Th): (6) Math primer IV: matrix algebra.
§
Hagle, 71-95.
§
Wooldridge, 776-786.
§
Greene, 803-845.
§
Optional: Fox, 524-534.
ð Homework # 4 due Th
Feb 24.
ð Homework
# 5 distributed.
ð Paper proposal due Tu Feb 22.
Mar 1 (Tu) &
Mar 3 (Th): No
class (International Studies Association Annual Meeting).
Mar
8 (Tu) &
Mar 10 (Th): (7) The regression
model in matrix form. Estimation and inference.
§
Wooldridge, 787-801.
§
Greene, 7-71.
§
Optional: Fox, 204-218, 221-235.
ð Homework # 5 due Tu
Mar 10.
ð Homework
# 6 distributed.
Mar 15 (Tu)
&
Mar 17 (Th): No
class. Spring Break.
Mar 22 (Tu): (8) Multicollinearity.
§
Wooldridge, 96-100.
§
Greene, 56-59.
§
Optional: Fox, 337-366.
§
Optional: Kennedy,
183-193.
ð Homework # 6 due Tu
Mar 22.
ð Homework
# 7 distributed.
Mar 24 (Th)
&
Mar 29 (Tu): (9) Heteroskedasticity.
The problem & diagnosis. GLS and
robust SEs.
§
Wooldridge, 257-288.
§
Greene, 215-249, 198-211.
§
Optional: Fox, 301-309, 320-321, 326-328.
§
Optional: George W. Downs and David M. Rocke,
“Interpreting
Heteroscedasticity,” American Journal of
Political Science 23:4 (November 1979), 816-828.
ð Homework # 7 due Tu
Mar 29.
ð Homework
# 8 distributed.
Mar 31 (Th)
&
Apr 5 (Tu): (10) Autocorrelation. The
problem, diagnosis, techniques, GLS and robust SEs.
§
Wooldridge, 391-424.
§
Greene, 250-282.
§
Optional: Fox, 369-385.
§
Optional: Kennedy,
121-126.
§
Optional: Frank R. Baumgartner, Bryan D. Jones, and Michael
C. MacLeod, “The
Evolution of Legislative Jurisdictions,” Journal of Politics 62:2
(May 2000), 321-349.
ð Homework # 8 due Tu
Apr 5.
ð Homework
# 9 distributed.
Apr 7 (Th)
&
Apr 12 (Tu): (11) Model specification. Linearity and data transformations.
§
Wooldridge, 89-95, 100-101, 142-148, 198-200, 289-322.
§
Greene, 116-161.
§
Optional: Fox, 235-239, 59-74, 309-317.
§
Optional: Kennedy,
88-99.
ð Homework # 9 due Tu
Apr 12.
ð Homework
# 10 distributed.
Apr 14 (Th)
&
Apr 19 (Tu): (12) Techniques for panel data.
§
Wooldridge, 426-483.
§
Greene, 283-338.
ð Homework # 10 due Tu
Apr 19.
ð Homework
# 11 distributed.
Apr 21 (Th)
&
Apr 26 (Tu): (13) Measurement error & endogeneity. Instrumental variables, concepts, adequacy,
testing. Two-stage least squares.
§
Wooldridge, 295-309, 484-524, optional 525-552.
§
Greene, 83-90, 378-400.
§
Optional: Fox, 130-133.
§
Optional: Steven D. Levitt, “Using
Electoral Cycles in Police Hiring to Estimate the Effect of Police on Crime,”
American Economic Review 87:3 (June 1997), 270-290.
ð Homework # 11 due Tu
Apr 26.
ð Homework
# 12 distributed.
Apr 28 (Th): (14) Introduction to maximum likelihood
estimation. ML estimation of the linear model, with derived
techniques.
§
Greene, 468-524, esp. 492-496.
§
Optional: Fox, 566-574, 219-224, 321-324, 438-456.
§
Optional: Gary King, Unifying Political Methodology: The Likelihood
Theory of Statistical Inference (
§
Optional: Charles H. Franklin, “Eschewing
Obfuscation? Campaigns and the Perception of US Senate Incumbents,” American
Political Science Review 85:4 (December 1991), 1193-1214.
May 2 (M): Homework # 12 due by 1:00p.
May 4 (W): Paper due by 1:00p.