engagement session of Erika & Chris in Pioneer Square and the Sc

Christopher Adolph is Associate Professor of Political Science and Adjunct Associate Professor of Statistics at the University of Washington, Seattle, where he is also a core faculty member of the Center for Statistics and the Social Sciences. His book, Bankers, Bureaucrats, and Central Bank Politics: The Myth of Neutrality (Cambridge University Press), won the International Political Science Association’s Charles H. Levine Prize for best contribution in comparative public policy and administration. He is a former Robert Wood Johnson Scholar in Health Policy Research and won the American Political Science Association’s Mancur Olson Award for best dissertation in political economy. His research on comparative political economy and quantitative methods has appeared in American Political Science Review, Political Analysis, Social Science & Medicine, and other academic journals.

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 ARIMA models, lagged dependent variables, and distributed lags; cointegration and error correction models; modeling cross-sectional variation using fixed and random effects; coping with panel heteroskedasticity, and presentation and interpretation of TS and TSCS models. Time permitting, we will cover advanced topics based on student interest, which in past years have included TSCS models for binary, ordered, or categorical data, multiple imputation for missing data in panel datasets, 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
All Readings will be provided; however, 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
OLS = moderate

Computer Background
R = elementary (or willingness to quickly learn)

Course outline

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.

Week 1

Day 1 Introduction, Review of the Linear Model and its Properties; R Basics

Readings:On R: Zuur, Ch. 1, 2, and 3 On linear regression: Woolridge, Ch. 4 On matrix algebra (opt.): Matrix Handout (Kevin Quinn)

Day 2 Estimation and Simulation; Linear Regression in R

Readings: On R: Zuur, Ch. 4 and 5 On simulation: King, Tomz, and Wittenberg 2000

Homework: Problem 1

Day 3 Basic Concepts for Time Series: Trends, Lags, and Cycles

Readings: On time series: Box-Steffensmeier et al, Ch. 1, 2 Alternate reading: Cowpertwait & Metcalf, Ch 1.1, 1.4, 1.6, 2.1-2.5 On R: Zuur, Ch. 6

Homework: Problems 2 and 3

Day 4 Modelling Stationary Time Series

Readings: On time series: Box-Steffensmeier et al, Ch. 3 Alternate reading: Cowpertwait & Metcalf, Ch. 4, Ch. 5.1-5.4, 5.9-5.11 Ch. 6

Homework: Problem 4 or Bonus Problem A

Day 5 Modelling Nonstationary Time Series

Readings: On time series: Box-Steffensmeier et al, Ch. 5. 6 Alternate reading: Cowpertwait & Metcalf, Ch. 7 On cointegration: Pfaff, Ch. 4

Homework: Problems 5 and 6

Week 2

Day 6 Basic Concepts for Panel Data

Readings: On random effects: Woolridge, Ch. 10.1-10.4 On hierarchical models: Gelman and Hill, Ch. 11, 12, 13

Homework: Problem 7

Day 7 Panel Data Models with Many Time Periods

Readings: On fixed effects: Woolridge, Ch. 10.5-10.7 On panels with large-T: Beck & Katz 2011

Homework: Begin work on Problem 8

Day 8 Panel Data Models with Few Time Periods

Readings: On panels with small-T: Roodman 2009(skip code examples)

Homework: Continue work on Problem 8

Day 9 Heteroskedasticity in Panel Data / In-Sample Simulation for Panel Data Models

Readings: On panel-corrected standard errors: Beck & Katz 1995 On heteroskedasticity and autocorrelation correction: Millo 2014

Homework: Complete Problem 8

Day 10 Course Wrap-up / Advanced Topics

Possible topics: Multiple imputation; Binary outcomes; Course Q&A;

Student research presentations

Readings: On multiple imputations for TSCS Honaker and King 2010 On binary outcomes in TSCS: Beck, Katz & Tucker 1998

Homework: No additional homework