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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.


Syllabus  



Summer 2016

Class meets:
MTWThF TBD
Teaching Center

TAs:

TBD (Essex)

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

Topic 1

Course Introduction / Review of Linear Regression and Simulation    

Students looking to brush up on matrix algebra may want to read through Kevin Quinn's matrix algebra review in pdf format. In conjunction with this lecture I discuss examples that can be found in slides on labor standards in Africa and intimate partner homicide in the US.


Lab 1

Introduction to R    

This lab comes with three example scripts. First, there is an R code and data for exploratory data analysis using histograms and boxplots. Next, there is an R code and data for a simple bivariate linear regression. Finally, there is an 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

Time Series: Stochastic Processes    

The univariate time series simulation function for R mentioned in the lecture is available here; this function allows for deterministic trends, stationary and nonstationary ARMA processes, and additive or multiplicative seasonality. Also available is a simpler but less flexible R script showing simulation and diagnostics of stationary processes using built-in functions.


Topic 3

Models of Stationary Time Series    

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


Topic 4

Models of Non-stationary Time Series    

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 5

Basic Concepts for Panel Data    

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


Topic 6

Panel Data Models with Many Time Periods    

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 7

Panel Data Models with Few Time Periods    

Panel GMM models (Arellano-Bond/Blundell-Bond) in R: example code, helper functions, and csv data for estimating and interpreting panel models with a small number of periods and a large number of cross-sectional units. On Nickell bias: a script to plot the asymptotic results of Nickell (1981) as well as a helper file and two Monte Carlo scripts (large N / large β and large N / small β) to produce the finite sample results in the lecture slides.


Topic 8

Heteroskedasticity in Panel Data    


Topic 9

In-Sample Simulation for Panel Data Models  

Example code for simulating in-sample unit-by-unit from a panel data model. Uses the cigarette data and helper functions from Topic 7.


Advanced Topic 1

Missing Data and Multiple Imputation for Panel Data    


Advanced Topic 2

Panel Data with Binary Dependent Variables    


Student Assignments

Course Problem Set  

Problems may be turned in at any time during the course

Data for problems 1, 3, and 4 in comma-separated variable format. Data for problem 4. Data for problem 5. Data for Bonus Problem A.



University of Washington link

CSSS Center for Statistics and the Social Sciences link

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