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Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the July/August 2023 or February 2024.
Monday 16 ꟷ Friday 20 August 2021
2 hours of live teaching per day
09:30 ꟷ 11:30 CET
This course provides a highly interactive online teaching and learning environment, using state of the art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team can cater to the specific needs of each individual.
This course focuses on panel data models that are estimated with OLS, namely linear models for dependent variables that are continuous or can be reasonably assumed to be continuous.
Although the basic concepts and ideas also apply to models designed for binary or ordinal dependent variables such as probit or logit, these models are complicated by maximum likelihood estimation (MLE) and require a different set of foundations.
Due to limited course hours and the complexity of MLE, we won't cover panel data models for dependent variables that are non-continuous, such as fixed effect logit and 2SLS (IV) probit models.
3 credits Engage fully with class activities
4 credits Complete a post-class assignment
Andrew is an assistant professor at CEU's Department of International Relations. He obtained his PhD from the National University of Singapore and King’s College London.
His research interests include international political economy, research design, and quantitative methods. He teaches the Research Design and Methods in IR course series at CEU.
Akos Mate is a research fellow at the Centre for Social Sciences in Hungary. His key research area is the political economy of the European Union and its members’ fiscal governance.
He uses a wide variety of methods in his research, particularly automated text analysis (and attached various machine learning approaches), network analysis and more traditional econometric techniques.
Part I involves a thorough discussion of the logic and assumptions underlying panel data methods. You’ll learn how the development of more advanced methods is driven by the need to address potential violations of these assumptions.
Part II focuses on the various statistical approaches and tricks available to social scientists to deal with such violations and problems hidden in their data, allowing them to obtain estimates of effects as close as possible to the true causal effects.
Part III focuses on applying the wide range of panel data methods discussed in the previous parts to substantive research questions of interest. You will learn how these methods can be used to provide answers to your own research questions.
We begin with a quick review of OLS regression, emphasising key assumptions required for the OLS estimator to be the ‘best’ estimator. We move on with simple panel data methods, namely two-period panel data analysis and first differencing.
We focus on more advanced methods for estimating unobserved effects in the context of panel data analysis. We introduce fixed and random effect estimators, then discuss their properties and the assumptions needed for them to be valid. With these foundations, you will then study a relatively new correlated random effects approach, a synthesis of fixed effects and random effects methods which has been shown to be very useful. We'll apply these techniques using R and Stata during the hands on-session.
A lecture on the instrumental variable (IV) method, which deals with violations to the strict exogeneity assumption, followed by a connected group activity and a Q&A with the Instructors.
We move on to more advanced panel data methods that address further violations of the standard OLS assumptions, including clustered and robust standard error, panel-corrected standard error (PCSE) estimates and dynamic panel methods (Arellano-Bond and system GMM estimators). We apply these techniques using R and Stata during the hands-on session.
To earn extra credits, you can present your research or research proposal that uses panel data methods and receive feedback from the Instructor and fellow participants.
All course materials are uploaded to Canvas, the e-learning platform. You can access the readings and video materials, all of the R and Stata code and data.
For the R practice, we will give you RStudio Cloud accounts, and use this R environment. Access details will be provided on Canvas.
Live teaching sessions will be via Zoom.
This course builds on Ordinary Least Squares (OLS) regression and extends it to data with a panel or TSCS structure. You should be familiar with basic theories of OLS, up to multiple regression.
To participate meaningfully in the lab sessions, you should also have basic knowledge of Stata and/or R.
Some background in linear algebra would be helpful but is not required or assumed.
You will need to watch nine pre-recorded video lectures before we cover the relevant materials and their application in class.
| Day | Topic | Details |
|---|---|---|
| 1 | Review of OLS regression and causal inference Simple panel data methods |
1.5 hour lecture 1.5 hour lecture |
| 2 | Fixed effect estimator Random effect estimator Correlated random effects approach |
3 hour lecture |
| 3 | Practical session Instrumental variable methods |
1.5 hour lab 1.5 hour lecture |
| 4 | Regression in matrix form Clustering and robust estimation Panel-corrected standard error Dynamic panel models |
3 hour lecture |
| 5 | Practical session Student presentations |
1.5 hours lab 1.5 hours seminar |
| Day | Readings |
|---|---|
| 2 |
Wooldridge, Jeffrey M Beck, Nathaniel |
| 1 |
Rubin, Donald B Wooldridge, Jeffrey M |
| 3 |
Cameron, Adrian Colin and Pravin K. Trivedi Wooldridge, Jeffrey M |
| 4 |
Beck, Nathaniel, and Jonathan N. Katz Arellano, Manuel, and Olympia Bover Bond, Stephen R Wooldridge, Jeffrey M |
| 5 |
Cameron, Adrian Colin and Pravin K. Trivedi |