Workshop

There will be a post-conference workshop on Dynamic Panel Data Analysis in Stata on Wednesday, July 8, 2026. We reserve the right to cancel this workshop if there is insufficient interest.

Part 1: Short-T Dynamic Panel Data Analysis

Sebastian Kripfganz

Sebastian Kripfganz is a Senior Lecturer (advanced assistant professor) in Econometrics at the University of Exeter Business School. He earned his Ph.D. in Economics from Goethe University Frankfurt in 2015, following an early career stint in economics at Bayerische Landesbank. His research focuses on dynamic panel data models, and he has developed influential Stata programs – including xtdpdgmm, xtdpdqml, xtdpdbc, xtivdfreg, xtdpdserial, and ardl – for estimation and testing in dynamic panel data and time series models. He is also a scientific co-organizer of the UK Stata Conference.

Tentative list of topics to be covered in part 1:

Stativ versus dynamic models and short-run versus long-run effects
Nickell bias of the fixed-effects estimator
Bias-corrected estimation (Stata command xtdpdbc)
Quasi-maximum likelihood estimation (Stata command xtdpdqml)
Generalized method of moments (GMM) estimation (Stata command xtdpdgmm)
Specification tests (Stata commands xtdpdserial, underid)

Part 2: Large-T Dynamic Panel Data Analysis

Jan Ditzen

Jan Ditzen is an Assistant Professor in Econometrics at the Free University of Bozen‑Bolzano, where he joined in 2021. He earned his Ph.D. in Economics from Heriot‑Watt University in Edinburgh and previously served as a Research Associate at Heriot‑Watt’s Centre for Energy Economics Research and Policy from 2017 to 2020. His research centers on applied econometrics, especially panel time series and spatial econometrics, growth empirics, simulation methods, and software implementation – culminating in Stata packages like xtdcce2, xtbreak, xthst, xtgrangert, xtnumfac, and simulate2. Since 2021, he has also served as Co‑Editor/Digital Media Editor of Spatial Economic Analysis.

Tentative list of topics to be covered in part 2:

Characteristics of large-N, large-T panels
Introduction to cross-sectional dependence
Pooled versus individual slope coefficients
Common correlated effects (CCE) estimator
Testing for cross-sectional dependence using the CD test (Stata commands xtcd2, xtcde2, xtnumfac)
CCE estimation of static and dynamic models (Stata command xtdcce2)
Analysis of slope heterogeneity (Stata command xthst)
Advanced estimation methods (Stata commands regife, xtivdfreg)