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Panel Data Analysis (RSS1.13) - Closed

This one-week summer course provides a general survey of the methods used to analyze panel data (i.e., data with units observed repeatedly over time). It places a special emphasis on causal inference, which is a primary goal of social sciences. You get a theoretical understanding of these methods and have the chance to practice them using popular statistical software Stata and/or R.


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    Starting date

    17 June 2024, 9 am
    Educational method
    Main Language
    17 June 2024, 9 am - 21 June 2024, 5 pm
    Andrew X. Li
    Unique code


    Type of education
    Entry requirements
    See the requirements in cost and admission
    Study load (ECTS)
    Radboud Summer School

    Course Program

    This one-week summer course builds on ordinary least square (OLS) regression and extends it to the econometric models and techniques commonly used to analyze panel data. 

    The course begins with a quick review of the standard OLS framework. It then moves on to simple panel data methods, such as fixed and random effect estimators. The remainder of the course focuses on more advanced methods, such as methods for the study of clustered samples, panel instrumental variable methods, and dynamic panel regression, that deal with violations of the OLS assumptions. 

    Theoretical lectures are complemented by applied lab sessions that put these methods into practice. Specifically, the course is divided into three parts:

    • The first part involves a thorough discussion of the logics and assumptions underlying panel data methods. You learn how the development of more advanced methods is driven by the need to address potential violations of these assumptions. 
    • The second part focuses on the various statistical approaches and 'tricks' available to social scientists to deal with such violations and problems hidden in their data, allowing you to estimate effects that are as close as possible to the true causal effects. 
    • The final part of the course focuses on applying the wide range of panel data methods discussed in the previous parts to substantive research questions of interest. 

    Overall, this course aims to strike a balance between statistical theory and practical application. You learn to use panel data in your research and develop an understanding and appreciation for the science behind these methods.

    Total package & social events

    Andrew X. Li

    Andrew X. Li

    Andrew X. Li is Lecturer in Economics at Nanyang Technological University, Singapore. He has been an assistant professor at the Department of International Relations, Central European University. His teaching includes international political economy, macroeconomics, research design, quantitative methods and the political economy of development and international organizations. Andrew received a Joint PhD from National University of Singapore and King's College London. His research has been published in academic journals such as Economics & Politics, Journal of Common Market Studies and Science and Public Policy. He has previously taught Introduction to Stata, Panel Data Analysis and Causal Mediation Analysis at various institutes and method schools in Europe and Asia.


    This course is closed, registration is no longer possible

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    • Regular: €1049 (application deadline 1st of May)
    • Student & PhD's: €699 (application deadline 1st of May)

    Includes: your course, short morning and late afternoon courses, coffee and tea during breaks, a warm lunch every day, Official Opening, MethodsNET Café (including some drinks and snacks) Official Closing (with some drinks and snacks) and a 1-year (2024 calendar year) free membership as MethodsNET regular member.

    Excludes: transport, accommodation, social events and other costs. 

    Discounts and Scholarships


    Level of participant: 

    • Master
    • PhD
    • Postdoc
    • Professional

    Admission requirements: 

    This course presumes a working knowledge of OLS regression. Participants should be familiar with the OLS assumptions and related statistical concepts such as heteroskedasticity. A background in linear algebra would be helpful, but is not required. Participants should also have some familiarity with Stata and/or R.

    Admission documents: 


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