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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 25 February – Friday 1 March, 14:00 – 17:30 (finishing slightly earlier on Friday)
15 hours over five days

Prerequisite Knowledge

You should know:

  • 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

By registering for this course, you confirm that you possess the knowledge required to follow it. Henriette will not teach the items in the list above. If in doubt, contact Henriette before registering.


Short Outline

Panel data or, to use the older terminology, longitudinal data, refers to a dataset 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 is that it allows us to analyse individual dynamics and identify causal effects.

This course presents different methods for analysing panel data, including:

  • fixed-effects
  • random effects
  • difference-in-differences
  • age-period-cohort models
  • growth curve models
  • hybrid models.

This is an applied course; no mathematics is involved but the intuition behind the models will be emphasised. We will discuss real data examples using the German Socio-Economic Panel (SOEP) and Stata.

Tasks for ECTS Credits

2 credits (pass/fail grade). Attend at least 90% of course hours, participate fully in in-class activities and carry out the necessary reading and/or other work prior to, and after, class.

3 credits (to be graded) As above, plus work on assignments using real data on a daily basis.

4 credits (to be graded) As above, plus 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.

Recommended Courses to Cover After this One

<p><strong>Winter School</strong></p> <p>Advanced Discrete Choice Modelling</p> <p>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 in due time.

Note from the Academic Convenors

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, contact the instructor before registering.