2G

Course Content

This course provides a survey of regression models for time series (TS) and time series cross-section (TSCS) data, with an emphasis on modeling dynamics and panel structures.  After a review of the theory and estimation of linear regression and maximum likelihood, we will cover the following topics: modeling time series dynamics using trends, ARIMA models, lagged dependent variables, and seasonality; cointegration and error correction models; modeling cross-sectional variation using fixed and random effects in with either many or few time periods; coping with panel heteroskedasticity; and presentation and interpretation of TS and TSCS models.  Time permitting, we will cover advanced topics included in-sample simulation for panel data, multiple imputation for missing data in panel data, and linkages between panel data and hierarchical linear models.

Course Objectives

For many political science subfields, including political economy, international relations, and comparative politics, panel data are ubiquitous, and training in TSCS analysis essential for quantitative research.  Participants will gain an introductory understanding of the theory behind TSCS models and a working understanding of how to estimate, select, and interpret these models.  Emphasis is placed on the development of both conceptual understanding and the ability to apply tools learned in the class using a variety of packages available for the R statistical language.

Course Prerequisites
Students should enter the course with a solid understanding of first year statistics as taught in a standard political science doctoral program, an interest in data with either a time series or time series cross-sectional (panel) data structure, and either exposure to, or willingness to try, the R statistical package. Specifically, students should be familiar with basic data structures and computing, elementary matrix algebra, and the basic theory and application of the linear regression model.

Required texts

This text will be provided by ESS:

Janet M. Box-Steffensmeier, John R. Freeman, Matthew P. Hitt, and Jon C.W. Pevehouse. 2014. Time Series Analysis for the Social Science. Cambridge University Press 

Students unfamiliar with R will benefit from reading this book ahead of the class:
Alain F. Zuur, Elena N. Ieno, and Erik H.W.G. Meesters. 2009. A Beginner’s Guide to R. Springer-Verlag.

Background knowledge required

Statistics

OLS = moderate

Computer Background

R = elementary (or willingness to quickly learn)

Maths

Calculus = elementary

Linear regression = moderate

Course outline

Goals. This course provides a survey of regression models for time series (TS; also called longitudinal)
and time series cross-section (TSCS; also called panel) data, with an emphasis on modeling
dynamics and panel structures. Because panel data are ubiquitous in many political science
subfields, including political economy, comparative politics, and international relations, training
in TSCS analysis is essential preparation for performing and understanding quantitative research.
Participants will gain an introductory understanding of the theory behind TSCS models
and a working understanding of how to estimate, select, and interpret these models.

Overview. After a review of the theory and estimation of linear regression, we will cover the
following topics: diagnosing dynamic behavior in time series; modeling time series dynamics
using ArIMA models, lagged dependent variables, and distributed lags; cointegration and error
correction models; modeling cross-sectional variation using fixed and random effects; appropriate
techniques for fixed effects models used on data with many time periods available versus
those used with few time periods; coping with panel heteroskedasticity; and presentation and
interpretation of TS and TSCS models, especially using dynamic simulation techniques. Time
permitting, we will cover advanced topics based on student interest, which in past years have
included multiple imputation for missing data in panel datasets, models for binary TSCS data,
and linkages between panel data and more general approaches to hierarchical linear models.
Prerequisites. Students should enter the course with a solid understanding of first year statistics
as taught in a standard political science doctoral program, an interest in data with either
a time series or time series cross-sectional (panel) data structure, and either exposure to, or
willingness to learn, the R statistical package.
R. Extensive in-class code examples will use the R statistical package, which is powerful, free,
open source, widely used, and rapidly becoming the standard for quantitative work in political
science and other fields. You can obtain R at http://www.r-project.org/. Throughout the
course, I will provide example code in R, and can only promise detailed homework help for R,
not other statistics packages.

Course Readings
Selections from the following books and articles will be provided as part of the course materials.
Students seeking to get a headstart should focus on the assigned readings for the first week;
those without a background in R should especially concentrate on Zuur.
Nathaniel Beck and Jonathan N. Katz. 1995. “What to Do (And Not to Do) With Time
Series Cross-Section Data.” American Political Science Review 89(3): 634–647.
Nathaniel Beck and Jonathan N. Katz. 2011. “Modeling dynamics in Time-Series–Cross-
Section political economy data.” Annual Review of Political Science 14:331–52.
Nathaniel Beck, Jonathan N. 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.
Janet M. Box-Steffensmeier, John R. Freeman, Matthew P. Hitt, and Jon C.W. Pevehouse.
2014. Time Series Analysis for the Social Science. Cambridge University Press.
Paul S.P. Cowpertwait and Andrew V. Metcalfe. 2009. Introductory Time Series with R.
Springer-Verlag.
Andrew Gelman and Jennifer Hill. 2007. Data analysis using regression and multilevel/hierarchical
models. University of Cambridge Press.
James Honaker and Gary King. 2010. “What to do about missing values in Time-Series
Cross-Section data.” American Journal of Political Science 54(2): 561–581.
Gary King, Michael Tomz, and Jason Wittenberg. 2000. “Making the Most of Statistical
Analyses.” American Journal of Political Science 44(2): 347–361.
Giovanni Millo. 2014. “Robust standard error estimators for panel models: a unifying approach.”
MPRA Paper No. 54954.
Bernhard Pfaff. 2008. Analysis of Integrated Series with R. Springer-Verlag.
David Roodman. 2009. “How to do xtabond2: An introduction to difference and system
GMM in Stata.” The Stata Journal. 9(1): 86–136.
Jeffrey M. Wooldridge. 2010. Econometric Analysis of Cross-Sectional and Panel Data. MIT Press.
2nd Edition.
Alain F. Zuur, Elena N. Ieno, and Erik H.W.G. Meesters. 2009. A Beginner’s Guide to R.
Springer-Verlag.

Readings assigned for a particular day should be read in advance of class. Students seeking feedback on homework should turn in the listed problems each day. It is strongly recommend that students new to R work through the example code presented in Zuur as they read.