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GV903 7(PGT) Advanced Research Methods
2014 2015
Lecturer and Module Supervisor Alejandro Quiroz Flores Tel:
01206 872506 E-mail: [email protected] Room: 5.025 Module
Administrator Office Hours: TBD Alex West, [email protected]
INSTANT DEADLINE CHECKER Assignment 1 Week 7 (Date at 09:45)
17.5% of coursework mark Feedback (Date) Assignment 2 Week 11 (Date
at 09:45) 17.5% of coursework mark Feedback (Date) Assignment 3
Week 19 (Date at 09:45) 17.5% of coursework mark Feedback (Date)
Assignment 4 Week 25 (Date at 09:45) 17.5% of coursework mark
Feedback (Date) MODULE DESCRIPTION This module presents
quantitative methods essential to test hypotheses. The first part
of
the course concentrates on hypotheses testing, hypotheses
testing using Least Squares, and
some classic violations of the Gauss-Markov conditions. The
second part of the module
concentrates on more advanced models ubiquitous in political
science.
The module places a strong emphasis on Least Squares, which is
approached substantively,
mathematically, and computationally. We will derive important
results for Least Squares
and replicate them using computer programs. The module makes
extensive use of STATA,
but we will also use R. Having covered Least Squares, the module
focuses on more
advanced models and particularly on recent political science
applications. Hence, the
number of readings increases in the second half of the course,
as we will cover the methods
and the actual applications to substantive questions. This is
particularly important because
students should familiarize themselves with the interpretation
and presentation of empirical
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evidence.
MODULE STRUCTURE AND TEACHING
Week Autumn Term
Week 2 Introduction to Advanced Research Methods:
Testing Theories
Week 3 Random Variables, Distributions, Expectations, and
(some) Large Sample Distribution Theory
Week 4 Relationships Between Variables, Estimation, and
Inference
Week 5 The Linear Regression Model and Least Squares
Week 6 Finite and Large Sample Properties of Least Squares:
The Gauss-Markov Theorem
Week 7 Inference and Prediction in Least Squares
Week 8 Functional Form, Structural Change, and Model
Selection
Week 9 Non-spherical Disturbances, Heteroscedasticity, and
Generalized Least Squares
Week 10 Auto-correlation and Basic Issues in Time Series
Week 11 Advanced Time Series
Week Spring Term
Week 16 Panel Data
Week 17 Data Problems: Endogeneity
Week 18 Instrumental Variables
Week 19 Simultaneous Equations and Seemingly Unrelated
Regressions
Week 20 Other Estimation Frameworks: Maximum Likelihood
and the Method of Moments
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Week 21 Non-Linear Models, Discrete Choice, and Maximum
Likelihood Estimation
Week 22 Advanced Discrete Choice Models
Week 23 Limited Dependent Variables
Week 24 Survival Models
Week 25 What Else is Out There? Causal Inference and List
Experiments
ASSESSMENT
Assessment Weight
Four (4) Assignments 17.5% each
Take-home examination 30%
Total 100%
STUDY ABROAD ASSESSMENT The lecturer will prepare two additional
assignments only for study abroad studentsdeadlines for these
assignments are weeks 5, 7, 9 and 11.
Assessment Weight
Four (4) Assignments 25% each
Total 100%
COURSEWORK SUBMISSION Submission deadlines Assignment 1 Week 7
(at 09:45) 25% of coursework mark Feedback Wk 9 Assignment 2 Week
11 (at 09:45) 25% of coursework mark Feedback Wk 13 Assignment 3
Week 19 (at 09:45) 25% of coursework mark Feedback Wk 21 Assignment
4 Week 25 (at 09:45) 25% of coursework mark Feedback Wk 27
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How to submit your essay using FASer (Online Coursework
Submission) You will be able to access the online submission via
your myEssex portal or via https://faser.essex.ac.uk. FASer allows
you to store your work-in-progress. This facility provides you with
an ideal place to keep partially completed copies of your work and
ensures that no work, even drafts, is lost. If you have problems
uploading your coursework, you should contact [email protected]. You
may find it helpful to look at the FASer guide
http://www.essex.ac.uk/elen/student/ocs.shtm. If you have any
questions about FASer, please contact your administrator or refer
to the handbook. Under NO circumstances is your coursework to be
emailed to the administrator or the lecturer. This will NOT be
counted as a submission. Coursework deadline policy for
PostGraduates There is a single policy at the University of Essex
for the late submission of coursework in Postgraduate courses.
Essays must be uploaded before 09.45 on the day of the deadline.
All coursework submitted after the deadline will receive a mark of
zero. The mark of zero shall stand unless the student submits
satisfactory evidence of extenuating circumstances that indicate
that the student was unable to submit the work prior to the
deadline. For further information on late submission of coursework
and extenuating circumstances procedures please refer to
http://www2.essex.ac.uk/academic/students/ug/extenug.html. Essay
feedback will be given via FASer. ALL submissions should be
provided with a coversheet (Available from Moodle). Plagiarism
Plagiarism is a very serious academic offence and whether done
wittingly or unwittingly it is your responsibility. Ignorance is no
excuse! The result of plagiarism could mean receiving a mark of
zero for the piece of coursework. In some cases, the rules of
assessment are such that a mark of zero for a single piece of
coursework could mean that you will fail your degree. If it is a
very serious case, you could be required to withdraw from the
University. It is important that you understand right from the
start of your studies what good academic practice is and adhere to
it throughout your studies. The Department will randomly select
coursework for plagiarism checks and lecturers are very good at
spotting work that is not your own. Plagiarism gets you nowhere;
DONT DO IT!
Following the guidance on referencing correctly will help you
avoid plagiarism.
Please familiarise yourself with the Universitys policy on
academic offences:
http://www.essex.ac.uk/academic/docs/regs/offpro.shtm
Extenuating circumstances for late submission of coursework
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The university has guidelines on what is acceptable as
extenuating circumstances for later submission of coursework. If
you need to make a claim, you should upload your coursework to
FASer and submit a late submission of coursework form which can be
found here:
http://www2.essex.ac.uk/academic/students/ug/crswk_pol.htm. This
must be done within seven days of the deadline. FASer closes for
all deadlines after seven days. The Late Submissions committee will
decide whether your work should be marked and you will be notified
of the outcome. If you experience significant longer-term
extenuating circumstances that prevent you from submitting your
work either by the deadline or within seven days of the deadline,
you should submit an Extenuating Circumstances Form for the Board
of Examiners to consider at the end of the year
http://www2.essex.ac.uk/academic/students/ug/extenug.html.
READINGS
Autumn Term 2014/2015
Basic Readings
Greene, William. 2003. Econometric Analysis. New Jersey:
Prentice Hall.
Wooldridge, Jeffrey. 2003. Introductory Econometrics: A Modern
Approach. Mason, OH:
Thomson.
There are two types of core readings for the module. The
readings from Wooldridge are
required for all students. Students interested in learning more
should cover the assignments
from Greene. In other words, Wooldridge is mandatory and Greene
is not (but students are
strongly encouraged to read him).
WEEK 2
Introduction to Advanced Research Methods: Testing Theories
Bueno de Mesquita, Bruce. 2009. Principles of International
Politics. 4th Edition. Washington
D.C.: CQ Press. Introduction, Chapters 1 and 2. Appendix B.
Przeworski, Adam, and Frank Salomon. 1995. The Art of Writing
Proposals. Social Science
Research Council.
Nagler, Jonathan. 1995. Coding Style and Good Computing
Practices. PS: Political Science
and Politics 28 (3): 488-492.
King, Gary. 1986. How Not to Lie With Statistics: Avoiding
Common Mistakes in Quantitative
Political Science. American Journal of Political Science 30 (3):
666-687.
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King, Gary, Michael Tomz, and Jason Wittenberg. 2000. Making the
Most of Statistical
Analyses: Improving Interpretation and Presentation. American
Journal of Political Science
44 (2): 341-355.
WEEK 3
Random Variables, Distributions, Expectations, and (some) Large
Sample Distribution
Theory
Greene: Appendix B1-B3.
Continuous and Discrete Distributions: Selection from Wackerly,
Dennis D., William
Mendenhall, and Richard L. Scheaffer (WMS). 2002. Mathematical
Statistics with
Applications, 6th ed., Pacific Grove, CA: Duxbury.
Joint Distributions: Greene: Appendix B7-B8
Central Limit Theorems: Greene: Appendix D2.6
Delta Method: Greene: Appendix D2.7
WEEK 4
Relationships Between Variables, Estimation, and Inference
Samples: Greene: Appendix C; Wooldridge: Appendix C
Measures of Association: Covariance, Correlation, and Scale
Invariant Measures of
Association
Point Estimation: Greene: Appendix C; Wooldridge: Appendix C
Interval Estimation: Greene: Appendix C; Wooldridge: Appendix
C
Hypothesis Tests: Greene: Appendix C; Wooldridge: Appendix C
WEEK 5
The Linear Regression Model and Least Squares
Simple Regression Model: Wooldridge: Chapter 2.1-2.2
Multiple Linear Regression Model: Greene: Chapter 2; Wooldridge:
Chapter 3.1-3.2
Assumptions: Greene: Chapter 2; Wooldridge: Chapter 3
Estimation by Least Squares: Greene: Chapter 3
Partitioned Regression: Greene: Chapter 3
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Model Fit and ANOVA: Greene: Chapter 3
WEEK 6
Finite and Large Sample Properties of Least Squares: The
Gauss-Markov Theorem
Unbiased Estimation: Greene: Chapter 4; Wooldridge Chapter 3
Variance and the Gauss-Markov Theorem: Greene: Chapter 4;
Wooldridge: Chapter 3
Statistical Inference: Greene: Chapter 4; Wooldridge: Chapter
4
Asymptotic Properties: Greene: Chapter 5; Wooldridge: Chapter
5
WEEK 7
Inference and Prediction in Least Squares
Nested Models: Greene: Chapter 6
Testing Hypotheses (t Test, F Test, and Loss of Fit): Greene:
Chapter 6; Wooldridge: Chapter
4
Prediction and its Intervals: Greene: Chapter 6; Wooldridge:
Chapter 4
WEEK 8
Functional Form, Structural Change, and Model Selection
Binary Variables: Greene: Chapter 7; Wooldridge: Chapters 6 and
7
Functional Forms: Greene: Chapter 7; Wooldridge: Chapters 6 and
7
Structural Change Tests: Greene: Chapter 7
Specification Analysis: Greene: Chapter 8
Non-nested Models: Greene: Chapter 8.
Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006.
Understanding Interaction
Models: Improving Empirical Analyses. Political Analysis 14 (1):
63-82.
WEEK 9
Non-spherical Disturbances, Heteroscedasticity, and Generalized
Least Squares
Generalized Least Squares: Greene: Chapter 10; Wooldridge:
Chapter 8
Feasible Generalized Least Squares: Greene: Chapter 10;
Wooldridge: Chapter 8
Covariance Matrix: Greene: Chapter 11; Wooldridge: Chapter 8
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Tests for Heteroscedasticity: Greene: Chapter 11; Wooldridge:
Chapter 8
Weighted Least Squares: Greene: Chapter 11
WEEK 10
Auto-correlation and Basic Issues in Time Series
Time Series: Greene: Chapter 12; Wooldridge: Chapter 10
Disturbance Processes: Greene: Chapter 12; Wooldridge: Chapter
10
Tests for Autocorrelation: Greene: Chapter 12; Wooldridge:
Chapter 10
Efficient Estimation: Greene: Chapter 12
WEEK 11
Advanced Time Series
Simple Time Series: Greene: Chapter 12 and 19; Wooldridge:
Chapter 10
Stationary and Weakly Dependent Time Series: Greene: Chapter 19;
Wooldridge: Chapter 11
Serial Correlation: Wooldridge: Chapter 12
Cointegration: Greene Chapter 20; Wooldridge: Chapter 18.
Beck, Nathaniel, and Jonathan N. Katz. 1995. What to Do (and Not
to Do) with Time-Series
Cross-Section. American Political Science Review 89 (3):
634-647
Beck, Nathaniel, and Jonathan N. Katz. 1996. Nuisance vs.
Substance: Specifying and
Estimating Time-Series-Cross-Section Models. Political Analysis
6 (1): 1-36.
Beck, Nathaniel. 2008. Time-Series Cross-Sectional Data
Techniques. In The Oxford
Handbook of Political Economy. Edited by Janet Box
Steffensmeier, Henry E. Brady, and
David Collier. Oxford University Press.
Beck, Nathaniel. 1991. Comparing Dynamic Specifications: The
Case of Presidential
Approval. Political Analysis 3 (1): 51-87.
De Boef, Suzanna, and Luke Keele. 2008. Taking Time Seriously.
American Journal of Political
Science 52 (1): 184-200.
Enders, Walter. 2009. Applied Econometric Times Series. Hoboken,
NJ: Wiley.
Spring Term 2014/2015
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WEEK 16
Panel Data
Heterogeneity: Greene Chapter 13
Panel Data: Wooldridge: Chapters 13 and 14
Fixed and Random Effects: Greene Chapter 13; Wooldridge: Chapter
14.
Random Coefficient Models: Greene Chapter 13
Beck, Nathaniel, and Jonathan N. Katz. 2007. Random Coefficient
Models for Time SeriesCross-Section Data: Monte Carlo Experiments.
Political Analysis 15(2): 182-195
Plmper, Thomas, and Vera E. Troeger. 2007. Efficient Estimation
of Time-Invariant and Rarely Changing Variables in Finite Sample
Panel Analyses with Unit Fixed Effects. Political Analysis 15 (2):
124-139.
Symposium on Fixed-Effects Vector Decomposition. Political
Analysis 19 (2).
WEEK 17
Data Problems: Endogeneity
Measurement Error: Wooldridge: Chapter 9
Non-Random Sampling: Wooldridge: Chapter 9
Omitted Variable Bias: Wooldridge: Chapter 15
Simultaneity: Wooldridge: Chapter 16
WEEK 18
Instrumental Variables
Instrumental Variables: Greene: Chapter 5.4; Wooldridge :Chapter
15.
Two Stage Least Squares: Wooldridge: Chapter 15.
Miguel, Edward, Shanker Satyanath, and Ernest Sergenti. 2004.
Economic Shocks and Civil
Conflict: An Instrumental Variables Approach. Journal of
Political Economy 112 (4): 725-753.
Gawande, Kishore, and Hui Li. 2009. Dealing with Weak
Instruments: An Application to the
Protection for Sale Model. Political Analysis 17 (3):
236-260
WEEK 19
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Simultaneous Equations and Seemingly Unrelated Regressions
Seemingly Unrelated Regression: Greene: Chapter 14
Simultaneous Equations: Greene Chapter 15; Wooldridge: Chapter
16
Jackson, John E. 2002. A Seemingly Unrelated Regression Model
for Analyzing Multiparty Elections. Political Analysis 10 (1):
49-65.
Reuveny, Rafael, and Quan Li. 2003. The Joint Democracy-Dyadic
Conflict Nexus: A Simultaneous Equations Model. International
Studies Quarterly 47 (3): 325-346
WEEK 20
Other Estimation Frameworks: Maximum Likelihood and the Method
of Moments
Least Squares: Greene: Chapter 16
Maximum Likelihood: Greene: Chapter 16
GMM: Greene: Chapter 16
WEEK 21
Non-Linear Models, Discrete Choice, and Maximum Likelihood
Estimation
Bayes Theorem and Likelihood: Greene: Chapter 17
Properties: Greene: Chapter 17
Tests: Greene: Chapter 17
Applications: Greene: Chapter 17
Binary Choice (Logit/Probit/Scobit): Greene: Chapter 21;
Wooldridge: Chapter 17
Random Utility: Greene: Chapter 21
Przeworski, Adam, and James Raymond Vreeland. 2002. A
Statistical Model of Bilateral Cooperation. Political Analysis 10
(2): 101-112
Philip, Paolino. 2001. Maximum Likelihood Estimation of Models
with Beta-Distributed Dependent Variables. Political Analysis 9
(4): 325-346
WEEK 22
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Advanced Discrete Choice Models
Multiple Choices: Greene: Chapter 21
Count Data (Poisson, Negative Binomial, ZIP Model): Greene:
Chapter 21
Bivariate Probit: Greene: Chapter 21.
Beck, Nathaniel, Jonathan Katz and Richard Tucker. 1998. Taking
Time Seriously: Time-
Series-Cross-Section Analysis with a Binary Dependent Variable.
American Journal of
Political Science 42 (4): 1260-1288.
Nagler, Jonathan. 1994. Scobit: An Alternative Estimator to
Logit and Probit. American
Journal of Political Science 38 (1): 230-255.
Carter, David B., and Curtis S. Signorino. 2010. Back to the
Future: Modeling Time Dependence in Binary Data. Political Analysis
18 (3): 271-292
Beck, Nathaniel. 2010. Time is Not A Theoretical Variable.
Political Analysis 18 (3): 293-294
Freedman, David A., and Jasjeet S. Sekhon. 2010. Endogeneity in
Probit Response Models. Political Analysis 18(2): 138-150
WEEK 23
Limited Dependent Variables
Truncation: Greene: Chapter 22; Wooldridge: Chapter 17
Censored Data: Greene: Chapter 22; Wooldridge: Chapter 17
Sample Selection: Greene: Chapter 22; Wooldridge: Chapter 17
Geddes, Barbara. 1990. How the Cases You Choose Affect the
Answers You Get: Selection Bias in Comparative Politics. Political
Analysis 2 (1): 131-150
Sigelman, Lee, and Langche Zeng. 1999. Analyzing Censored and
Sample-Selected Data with Tobit and Heckit Models. Political
Analysis 8 (2): 167-182
WEEK 24
Survival Models
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Survival Models: Box-Steffensmeier, Janet M., and Christopher
J.W. Zorn. 2001. Duration
Models and Proportional Hazards in Political Science. American
Journal of Political Science
45 (4): 951-67.
Geddes, Barbara. 1990. How the Cases You Choose Affect the
Answers You Get: Selection Bias in Comparative Politics. Political
Analysis 2 (1): 131-150
Sigelman, Lee, and Langche Zeng. 1999. Analyzing Censored and
Sample-Selected Data with Tobit and Heckit Models. Political
Analysis 8 (2): 167-182
WEEK 25
What Else is Out There? Causal Inference and List
Experiments
Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart.
2007. Matching as Nonparametric Preprocessing for Reducing Model
Dependence in Parametric Causal Inference. Political Analysis
15(3): 199-236
Gilligan, Michael J., and Ernest J. Sergenti. 2008. Do UN
Interventions Cause Peace? Using
Matching to Improve Causal Inference. Quarterly Journal of
Political Science 3 (2): 89-122.
Gordon, Sanford C., and Gregory Huber. 2007. The Effect of
Electoral Competitiveness on
Incumbent Behavior. Quarterly Journal of Political Science 2
(2): 107-138.
Corstange, Daniel. 2009. Sensitive Questions, Truthful Answers?
Modeling the List Experiment with LISTIT. Political Analysis 17
(1): 45-63
Blair, Graeme and Kosuke Imai. Statistical Analysis of List
Experiments. Manuscript.
