Install this application on your home screen for quick and easy access when you’re on the go.
Just tap then “Add to Home Screen”
Install this application on your home screen for quick and easy access when you’re on the go.
Just tap then “Add to Home Screen”
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 Summer/Winter 2024.
Monday 30 July - Friday 3 August
14:00-15:30 / 16:00-17:30
This course places an emphasis on the connections between panel data methods and causal inference, which is the primary goal of social science research. Panel data is particularly advantageous for causal inference as it allows researchers to control for entity-specific factors (individual heterogeneity) such as geography and culture, which may not be observed and can be difficult to measure. Furthermore, panel data usually contain more degrees of freedom and more sample variability than purely cross-sectional data or time-series data, and hence improve the efficiency of the parameter estimates. The course begins with an overview of causal inference in the context of observational studies. It then moves on with simple panel data methods, fixed and random effect estimators and more advanced methods such as clustered samples, panel instrumental variable methods, panel corrected standard error estimator and dynamic panel regressions, depending on time availability. Two lab sessions are arranged in this course to put these methods into practice (using Stata). The last day of the course is devoted to student presentations whereby students will present their research or research proposals and receive feedbacks from the instructor and their peers. Thus, students are encouraged to bring their own data to the course.
Tasks for ECTS Credits
For one additional credit, participants will be required to complete and submit a replication/extension exercise in Stata applying panel data techniques.
For two additional credits, in addition to the above participants will be required to submit in writing a research paper or research proposal (2500-3000 words) that employs panel data analysis as part of the research design.
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.
While there should be a large overlap between this course and the ones offered by various universities in the world on panel data methods, this course places an emphasis on the connections between these methods and causal inference, which is the primary goal of social science research. Thus, the course begins with an overview of causal inference in the context of observational studies. We will quickly survey the idea of causality and concepts such as average treatment effect (ATE). Importantly, we will draw the connections between these concepts and regression analysis.
In the second half of Day 1, we will look at simple panel data methods. Specifically, we make a distinction between “independently pooled across section” data and “panel” data, with the latter being the focus of this course. We shall see that panel data differs in some important respects from an independently pooled cross section in that a panel consists of the same entities (individuals, firms, countries or whatever) across time. We will then study two basic panel data methods, namely two-period panel data analysis and first differencing.
On Day 2, we focus on slightly more advanced methods for estimating the unobserved effects in the context of panel data analysis. we introduce the fixed effects estimator, which, like first differencing, uses a transformation to remove the unobserved effect prior to estimation. As a result, any time-constant explanatory variables are removed in the process. In contrast to the fixed effect estimator, we also introduce the random effect estimator, which looks attractive when we think the unobserved effect is uncorrelated with all the explanatory variables. For example, when we have good knowledge about the factors affecting the dependent variable and have controlled for these factors in the equation, random effect estimator can sometimes be the preferred strategy. With these foundations, we will then look into a relatively new correlated random effects approach, which provides a synthesis of fixed effects and random effects methods and has been shown to be practically very useful.
Day 3 begins with a lab session, whereby we put into practice the methods that has been introduced in the previous two days. We shall carry out these analyses in Stata and learn about the interpretation of the results. This is also a good opportunity for students who have brought their own data to carry out the analysis for their current research projects. In the second session of the day, we shall come back to the classroom to learn more about the research designs and methods for which panel data can be applied to. Depending on the time available, we will learn about match-pairs samples, cluster samples and the combination of panel data methods with instrumental variable (IV) method. Again, we emphasize the connection between these methods and causal inference.
The first session on Day 4 continues with the discussion on applying panel data methods to other data structures and model specifications. Specifically, we study panel-corrected standard error (PCSE) estimates and dynamic panel methods (Arellano-Bond estimator). For these more advanced methods, we shall not be looking into the technical details but rely more on an intuitive understanding of the challenges these methods address. In the second session, we return to the lab and put these more advanced methods into practice. We will learn how to carry out panel IV analysis, obtain the PCSE and Arellano-Bond estimators and calculate cluster robust standard errors. Again, this is a good opportunity for students to carry out the analyses using their own datasets and check robustness of the results across various model specifications.
Day 5 consists of two seminar sessions in which students will present on their research or research proposals that employ any panel data methods and receive feedback from the instructor and their peers.
Note from the Academic Convenors to prospective participants: by registering to this course, you certify that you possess the prerequisite knowledge that is requested to be able to follow this course. The instructor will not teach again these prerequisite items. If you doubt whether you possess that knowledge to a sufficient extent, we suggest you contact the instructor before you proceed to your registration.
This course builds on ordinary least square (OLS) regression and extends it to data with a time-series-cross-sectional structure. Participants should be familiar with basic statistical concepts such as sample mean and sample variance as well as their properties. In addition, participants should possess basic knowledge of regression analysis (up to multiple regression). Basic skills in Stata are also necessary. Participants who do not meet the above criteria are strongly encouraged to take other relevant courses offered by the Summer School, either before or concurrently with this course.
Day | Topic | Details |
---|---|---|
Monday | Introduction to panel data analysis, clustered standard errors and panel corrected standard errors. |
90min lecture, 90min lab |
Tuesday | Within and between effects, fixed effects models |
90min lecture, 90min lab |
Wednesday | Random effects and modelling dynamics in panel data |
90min lecture, 90min lab |
Thursday | Random coefficients, spatial autocorrelation, binary, multinomial and censored dependent variables |
90min lecture, 90min lab |
Friday | Unbalanced data sets, time invariant and slowly moving variables, instrumental variable and simultaneous equation models |
90min lecture, 90min lab |
1 | Causal inference Simple panel data methods |
Lecture (1.5 hours) Lecture (1.5 hours) |
2 | Fixed effect estimator Random effect estimator Correlated random effects approach |
Lecture (3 hours in total) |
3 | Practical session 1 Advanced panel data methods |
Lab (1.5 hours) Lecture (1.5 hours) |
4 | Advanced panel data methods (continued) Practical session 2 |
Lecture (1.5 hours) Lab (1.5 hours) |
5 | Student presentation |
Seminar (3 hours in total) |
Day | Readings |
---|---|
Monday |
Beck, Nathaniel and Jonathan N. Katz. 1995. What to do (and not to do) with time-series cross-section data. American Political Science Review 89 (3): 634-647. Beck, Nathaniel 2001: “Time-Series-Cross-Section Data: What Have We Learned in the Past Few Years?” Annual Review of Political Science 4, 271-293. |
Tuesday |
Green, Donald P., Soo Yeon Kim and David H. Yoon. 2001. Dirty pool. International Organization 55(2):441-468. Wooldridge, Jeffrey M. 2002: Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, chpts. 10-11 Plümper, Thomas, Troeger, Vera E. and Philip Manow 2005: “Panel Data Analysis in Comparative Politics. Linking Method to Theory.” European Journal of Political Research 44, 327-354. |
Wednesday |
Linzer, Drew and Tom Clark. 2012. Should I use random effects or fixed effects? Manuscript in progress. Beck, Nathaniel and Jonathan N. Katz. 2011. Modeling Dynamics in Time-Series-Cross-Section Political Economy Data. Annual Review of Political Science 14:331-352. Wawro, Gregroy. 2002. Estimating dynamic panel data models in political science. Political Analysis 10(1): 25-48. |
Thursday |
Beck, Nathaniel and Jonathan N. Katz. 2007. Random coefficient models for time series-cross-section data. Political Analysis 15(2): 182-195 Franzese, Robert J. and Jude C. Hays. 2007. Spatial econometric models of crosssectional interdependence in political science panel and time-series-cross-section data. Political Analysis 15(2): 140-164. Beck, Nathaniel Kristian Skrede Gleditsch and Kyle Beardsley. 2006. Space is more than geography: Using spatial econometrics in the study of political economy. International Studies Quarterly 50(1): 27-44. Beck, Nathaniel, Jonathan N. Katz and Richard Tucker. 1998. Taking time seriously: time-series-cross-section analysis with a binary dependent variable. American Journal of Political Science 42(4): 1260-1288. Carter, David B. and Curtis S. Signorino. 2010. Back to the Future: Modeling Time Dependence in Binary Data. Political Analysis 18(3): 271-292 |
Friday |
Wooldridge, Jeffrey M. 2003: Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, chpts. 5-9, pp. 83-247. Plümper, Thomas and Vera E. Troeger 2007: “Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects.” Political Analysis 15, 124-139. Plümper, Thomas and Vera E. Troeger 2011: “Fixed Effects Vector Decomposition: Properties, Reliability and Instruments.” Political Analysis 19, 147-164. Wilson, Sven E. and Danial M. Butler 2007: A Lot More to Do: The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications. Political Analysis 15: 101-123 |
2 |
Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach. Cengage Learning, 2016: Chapter 14, pp.434-457 Beck, Nathaniel. "Time-series–cross-section data: What have we learned in the past few years?." Annual Review of Political Science 4, no. 1 (2001): 271-293 |
1 |
Holland, Paul W. "Statistics and Causal Inference." Journal of the American Statistical Association 81, no. 396 (1986): 945-960 Rubin, Donald B. "Estimating causal effects of treatments in randomized and nonrandomized studies." Journal of educational Psychology 66, no. 5 (1974): 688-701 Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach. Cengage Learning, 2016: Chapter 13, pp.402-433 |
3 |
Cameron, Adrian Colin and Pravin K. Trivedi. Microeconometrics Using Stata. College Station, TX: Stata Press, 2009. Chapter 8, pp. 229-280 Wooldridge, Jeffrey M. Econometric Analysis of Cross Section and Panel Data. MIT Press, 2010. Chapter 11, pp. 345-394 |
4 |
Beck, Nathaniel, and Jonathan N. Katz. "What to do (and not to do) with time-series cross-section data." American Political Science Review 89, no. 3 (1995): 634-647 Imbens, Guido W., and Jeffrey M. Wooldridge. "Recent developments in the econometrics of program evaluation." Journal of Eeconomic Literature 47, no. 1 (2009): 5-86 Cameron, Adrian Colin and Pravin K. Trivedi. Microeconometrics Using Stata. College Station, TX: Stata Press, 2010. Chapter 9, pp. 281-312 Wooldridge, Jeffrey M. Econometric Analysis of Cross Section and Panel Data. MIT Press, 2010. Chapter 20, pp. 853-902 |
5 |
No required readings |
Stata (version 15)
None - the course will be held in a lab
Nickell, Stephen. "Biases in dynamic models with fixed effects." Econometrica: Journal of the Econometric Society (1981): 1417-1426.
Blundell, Richard, Stephen Bond, and Frank Windmeijer. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator." In Nonstationary panels, panel cointegration, and dynamic panels, pp. 53-91. Emerald Group Publishing Limited, 2001.
Summer School
Introduction to STATA
Introduction to Inferential Statistics: What you need to know before you take regression
Multiple Regression Analysis: Estimation, Diagnostics, and Modelling
Summer School
Advanced Topics in Applied Regression
Causal Inference in the Social Sciences II: Difference in Difference, Regression Discontinuity and Instruments