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Panel Data Analysis
for Comparative Research

Essex 2i

A survey of regression models for time series and time series cross-section data, with emphasis on modeling dynamics and panel structures. Participants will gain a basic understanding of the theory behind TSCS models and a working understanding of how to estimate, select, and interpret them, with a focus on the problems facing students of political economy, international relations, and comparative politics.

Essex 2i

Panel Data Analysis
for Comparative Research

Offered every Summer at the
University of Essex

For my University of  Washington
     course, click Panel Data again.


Summer 2014

Class meets:
MTWThF 2:15-5:45 pm
Teaching Center


Ron Lehrer (Essex)
Dragana Vidovic (Essex)

Lectures           PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser.

Topic 1

Review of Linear Regression / Intro to Maximum Likelihood


Supplementary material: You may want to read through Kevin Quinn's matrix algebra review in pdf format. The R code to simulate heteroskedastic data and model that data using a heteroskedastic normal maximum likelihood is here. This is an advanced example; beginning R users should work through the code examples for Lab 1 and Lab 2 first.

Lab 1

Introduction to R


Supplementary material: R code and data for exploratory data analysis using histograms and boxplots. R code and data for a simple bivariate linear regression. R code and data for a multiple regression example.

Interested students can find detailed instructions for downloading, installing, and learning my recommended software for quantitative social science here. Focus on steps 1.1 and 1.3 for now, and then, optionally, step 1.2.

Topic 2

Data Simulation, Model Fitting, and Interpretation of Results for Maximum Likelihood


Lab 2

Linear Regression in R


Supplementary material: R code for computing and plotting confidence intervals for a fitted regression line.

Topic 3

Time Series: Stochastic Processes


Supplementary material: R code showing simulation of stationary processes, and code for simulation of non-stationary processes.

Topic 4

Models of Stationary Time Series


Supplementary material: Example code and csv data for estimating and interpreting ARMA models in R.

Topic 5

Models of Non-stationary Time Series


Supplementary material: Example code for estimating and interpreting ARIMA and ECM models in R. You will also need this helper function for plotting counterfactual time series using R base graphics.

Topic 6

Basic Concepts for Panel Data


Supplementary material: For the curious, the R script used to construct the example plots in the first half of this lecture is here.

Topic 7

Variable Intercept Models for Panel Data


Supplementary material: Panel ARIMA and fixed effects models in R: example code and csv data for estimating and interpreting panel models with a large number of periods and a small number of cross-sectional units.

Topic 8

Panel and Time Series: Lingering Issues


Supplementary material: Arellano-Bond/Blundell-Bond models in R: example code, a helper function, and csv data for estimating and interpreting panel models with a small number of periods and a large number of cross-sectional units.

Advanced Topic 1

Panel and Time Series: Binary Dependent Variables


Advanced Topic 2

Multilevel Models: Maximum Likelihood and Bayesian Approaches


Advanced Topic 3

Missing Data and Multiple Imputation


Student Assignments

Course Problem Set

Problems may be turned in at any time during the course

Supplementary material: Data for problems 1, 3, and 4 in comma-separated variable format. R code for problem 4. Data for problems 5. Data for problem 6.

University of Washington link

CSSS Center for Statistics and the Social Sciences link

Chris Adolph & Erika Steiskal

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