Online, Italy

Dynamic Panel Data Analysis

Dynamic Panel Data (DPD) are of interest in a wide range of economics, financial and social models. Consequently, DPD analysis has become increasingly popular due to its ability to take into account both short and long term effects and unobserved heterogeneity across economic agents in the estimation of the model parameters.


This course provides a rigorous overview of existing DPD techniques, thus offering students the opportunity to acquire the more advanced technical capabilities currently available for panel data analysis. Students are provided with a theoretical and applied overview of Instrumental Variable analysis (IV) and Generalized Methods of Moments (GMM), both of which being an important class of estimators for DPD models. The course then turns to address more recent issues in DPD analysis, such as weak instruments with persistent data; instrument proliferation; gaps in the data; estimation with serially correlated errors; robust inference with multiway clustering; maximum likelihood DPD models; sample selection, tests and corrections, and the Monte Carlo evaluation of the finite-sample performance of estimators and tests. The course concludes by addressing the issues of; i) non-stationarity in long panels, where the time series (as opposed to cross-sectional) characteristics of the data dominate; and ii) panel cointegration.


Lectures consist of theoretical sessions (in which the techniques and underlying principles are explained), supplemented by hands-on segments in Stata, during which participants have the opportunity to implement the techniques using real-world or simulated data and to replicate some of the results in published articles.


During the course, particular attention is paid (using a combination of both official Stata and community written dynamic panel data analysis commands) to: i) evaluating which specific econometric methodology/specification is the more appropriate for the analysis in hand; ii) the selection of appropriate instruments; iii) rigorous post-estimation diagnostic/specification testing; and iv) the problems of inference resulting from weak instrument bias, instrument-proliferation bias and small-sample bias. Special attention is also given to the interpretation and presentation of results.


In common with TStat’s training philosophy, each session is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained) and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the course, theoretical sessions are reinforced using applied case studies, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques.


At the end of the course, it is expected that participants are able, with the aid of the Stata routines implemented during the sessions, to independently implement the methodologies and techniques acquired during the course in their own particular research needs.

Target group

Our DPD Analysis course is of particular interest to Ph.D. students, researchers in public and private research centres, and professionals working in the following fields: Agricultural Economics, Economics, Finance, Management, Public Health, and the Political and Social Sciences, wishing to acquire the necessary applied and theoretical skills in order to be able to independently conduct applied empirical research on DPD.

Course aim

This course provides a rigorous overview of existing DPD techniques, thus offering students the opportunity to acquire the more advanced technical capabilities currently available for panel data analysis. Students are provided with a theoretical and applied overview of Instrumental Variable analysis (IV) and Generalized Methods of Moments (GMM), both of which being an important class of estimators for DPD models. The course then turns to address more recent issues in DPD analysis, such as weak instruments with persistent data; instrument proliferation; gaps in the data; estimation with serially correlated errors; robust inference with multiway clustering; maximum likelihood DPD models; sample selection, tests and corrections, and the Monte Carlo evaluation of the finite-sample performance of estimators and tests. The course concludes by addressing the issues of; i) non-stationarity in long panels, where the time series (as opposed to cross-sectional) characteristics of the data dominate; and ii) panel cointegration.

Fee info

EUR 1170: Full-Time Students*: € 1170.00
Ph.D. Students: € 1500.00
Academic: € 1735.00
Commercial: € 2330.00


*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters students, access to student participation rates. Part-time master and doctoral students who are also currently employed will, however, be allocated academic status.


Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however be applied to companies, Institutions or Universities providing a valid tax registration number.