Online, Italy

Dynamic Panel Data Analysis

online course
when 27 September 2021 - 6 October 2021
language English
duration 2 weeks
fee EUR 1065

Dynamic panel data analysis has become increasingly popular in a wide range of fields, due to its ability to take into account both: i) short and long term effects and; ii)unobserved heterogeneity between economic agents in the estimation of the parameter estimates.

This course offers a rigorous overview of existing dynamic panel data analysis techniques, thus providing participants with the opportunity to acquire the more advanced technical capabilities currently available for panel data analysis. In the opening session, participants are given, through a series of illustrative examples, 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 the estimation of dynamic linear panel data models. The course then turns to address more recent issues in dynamic panel data analysis, such as weak instruments with persistent data; instrument proliferation; gaps in the data; estimation with serially correlated errors; robust inference with multiway clustering and the finite-sample performance of estimators and tests. The concluding session addresses issues of: i) non-stationarity in long panels, where the time series (as opposed to cross-sectional) characteristic of the data dominates; and ii) cointegration.

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 will also be given to the interpretation and presentation of results. At the end of the course, participants are expected to be able, with the aid of the Stata routines implemented during the sessions, to correctly implement independently the methodologies and techniques acquired during the course.

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. Moreover, 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.

Target group

Our Dynamic Panel Data 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, Political Sciences and the Social Sciences, wishing to acquire the necessary applied and theoretical skills in order to be able independently conduct applied empirical research on dynamic panel data.

Course aim

This course offers a rigorous overview of existing dynamic panel data analysis techniques, thus providing participants with the opportunity to acquire the more advanced technical capabilities currently available for panel data analysis. In the opening session, participants are given, through a series of illustrative examples, 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 the estimation of dynamic linear panel data models. The course then turns to address more recent issues in dynamic panel data analysis, such as weak instruments with persistent data; instrument proliferation; gaps in the data; estimation with serially correlated errors; robust inference with multiway clustering and the finite-sample performance of estimators and tests. The concluding session addresses issues of: i) non-stationarity in long panels, where the time series (as opposed to cross-sectional) characteristic of the data dominates; and ii) cointegration.

Fee info

EUR 1065: Full-Time Students*: € 1065.00
Full-Time PhD Students: € 1365.00
Academic: € 1515.00
Commercial: € 2020.00

*To be eligible for full-time student prices, participants must provide proof of their full-time student status for the current academic year.