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Panel Data Analysis

Henriette Engelhardt-Wölfler
henriette.engelhardt-woelfler@uni-bamberg.de

University of Bamberg

Since 2006, Henriette Engelhardt-Wölfler has been a Professor of Demography at Otto-Friedrich-University Bamberg.

She studied Sociology and Statistics at the University of Mannheim (Dipl.-Soz., 1992) and at the University of Berne (Dr. rer. soc., 1998, Habilitation, 2005).

She worked as research scientist at the University of Berne from 1992 – 98, at the Max-Planck-Institute for Human Development in Berlin from 1998 – 2000, at the Max-Planck-Institute for Demographic Research in Rostock from 2000 – 02, and at the Vienna Institute of Demography from 2002 – 06.

In 2000, Henriette was a visiting research scientist at Duke University, North Carolina. She has also held visiting research posts at the International Institute for Advanced Systems Analysis (IIASA) in Laxenburg, Austria (2002), and at the Swiss Federal Institute of Technology, Zurich (2004).

Henriette has published more than thirty articles in refereed journals on her particular topics of interest, which include:

  • social demography
  • family demography
  • population ageing
  • medical sociology
  • causal analysis.

Course Dates and Times

Monday 5 to Friday 9 March 2018
14:00-17:30
15 hours over 5 days

 

Prerequisite Knowledge

Participants should have knowledge of:

  • Least-Squares Estimation in the Linear Regression Model under the classical assumptions
  • Properties of sample means, sample variances, and the expectation operator
  • Principles of maximum likelihood estimation under standard regularity conditions
  • Statistical software Stata


Short Outline

Panel data or longitudinal data (the older terminology) refers to a data set containing observations on multiple phenomena over multiple time periods. Thus it has two dimensions: spatial (cross-sectional) and temporal (time series). The main advantage of panel data comes from its possibilities for analyzing individual dynamics and identifying causal effects. The course will present different methods for analysing panel data including fixed-effects, random effects, difference-in-differences, age-period-cohort models, growth curve models, and hybrid models. He course will be on an applied level; no mathematics but the intuition behind the models will be emphasized. Real data examples using the German Socio-Economic Panel (SOEP) and Stata will be discussed.

Tasks for ECTS Credits

  • Participants attending the course: 2 credits (pass/fail grade) The workload for the calculation of ECTS credits is based on the assumption that students attend classes and carry out the necessary reading and/or other work prior to, and after, classes.
  • Participants attending the course and completing one task (see below): 3 credits (to be graded)
  • Participants attending the course, and completing two tasks (see below): 4 credits (to be graded)
  1. The students will work on assignments using real data on a daily basis.
  2. The students shall work on their own projects using their own data and write a paper in journal style.
Day Topic Details
1 Panel data and causality, describing and modelling panel data
2 Pooled OLS, fixed and random effects
3 Growth curve models
4 Age-period-cohort models
5 Hybrid models, dynamic panel models, miscellaneous topics
Day Readings
1

Brady, Henry E. (2010) Causation and Explanation in Social Science. Pp. 217-270 in: J.M. Box-Steffensmeier, H.E. Brady and D. Collier (eds.) The Oxford Handbook of. Political Methodology. Oxford University Press.

Lynn, Peter (2009) Methods for Longitudinal Data. In Methodology of longitudinal surveys, Hrsg. Peter Lynn, Pp 1-20. Chichester: Wiley.

2

Cameron, Collin A. & Trivedi, Pravin K. (2005) Microeconometrics: Methods and Applications. Cambridge University Press, Pp 254-285.

Wooldridge, Jeffrey M. (2003) Introductory Econometrics. South-Western College Pub, Pp 426-469.  

3

Singer, Judith D. & Willet, John B. (2003) Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press, Pp 45-75.

Cameron, Collin A. & Trivedi, Pravin K. (2005) Microeconometrics: Methods and Applications. Cambridge University Press, Pp 305-313.

Rabe-Hesketh, Sophia & Skrondal, Anders (2005) Multilevel and Longitudinal Modeling Using Stata, 1stedition, Pp 57-74.

4

Robert M. O’Brien (2014) Age-Period-Cohort Models. Chapmann & Hall/CRC.

5

Wooldrige, J (2010) Econometric Analysis of Cross Section and Panel Data. MIT Press.

Hsiao, Cheng (2003) Analysis of Panel Data.Cambridge University Press.

Software Requirements

Stata 15.0

Literature

Additional literature will be provided to the students

Recommended Courses to Cover After this One

<p>Winter School: Advanced Discrete Choice Modelling Introduction to Bayesian Inference</p>


Additional Information

Disclaimer

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.