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Monday 10 ꟷ Friday 14 August 2020
2 hours of live teaching per day
Courses will be either morning or afternoon to suit participants’ requirements
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 (the Instructor plus one highly qualified Teaching Assistant) can cater to the specific needs of each individual.
This course provides a general survey of the methods and techniques that can be applied to the analysis of panel and/or time-series-cross-sectional (TSCS) data.
It aims to equip you with theoretical knowledge and practical skills related to panel data so that you can investigate economic, political and social phenomena scientifically and provide answers to your own research questions.
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 that are 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.
The course starts with a quick review of OLS regression, emphasising the key assumptions required for the OLS estimator to be the ‘best’ estimator. We then move on with simple panel data methods, namely two-period panel data analysis and first differencing.
We focus on slightly more advanced methods for estimating unobserved effects in the context of panel data analysis. We introduce fixed and random effect estimators, discuss their properties, and the assumptions needed for them to be valid. With these foundations, we 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 begin with a lab session, during which we put into practice the methods introduced in the previous two days. We carry out these analyses in Stata and/or R and learn about the interpretation of the results. This is followed by a lecture on the instrumental variable (IV) method, which deals with violations to the strict exogeneity assumption.
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).
A second lab session and a seminar. If you want 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.
Reading materials will be provided via CEU’s e-learning system Moodle, in case you do not have access to physical libraries. Readings will be supplemented by around eight hours of pre-recorded lectures covering the theoretical part of the materials. There will be around seven hours of live sessions during the week.
The live sessions are mainly devoted to:
The live components will also include getting to know each other on the first day and, potentially, an online social event. You are welcome to set up appointments with the Instructor or TA for one-to-one consultations, during office hours.
This course builds on 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.
Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.
Please check your course format before registering.
Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.
In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.