research label grad course label undergrad course label other labelbook linkarticles linkworking paper linkAdvanced Quantitative Political Methodology
	        linkmax likelihood linkvisualizing data linkpanel data linkPolitical Science as Social Science
	       LinkIntro to Soc Stat linkCase-Based Stat linkSoftware linkData

Panel Data Analysis
for Comparative Politics

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 Politics

Offered every Summer at the
University of Essex


Summer 2013

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


Massoud Farokhi (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.

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: Example code and csv data for estimating and interpreting panel ARIMA models in R.

Topic 8

Panel and Time Series: Lingering Issues


Supplementary material: Example R code for the Arellano-Bond method using simulated data can be found here.

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 linkDepartment of Political Science
Center for Statistics and the Social Sciences
Copyright 2011–2014
    Privacy · Terms

Design: Chris Adolph
and Erika Steiskal

Jefferson (2007-2011)