90388 - Advanced Panel Data Methods

Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

No poverty Quality education Gender equality Partnerships for the goals

Academic Year 2020/2021

Learning outcomes

At the end of the course, students know the most appropriate estimating techniques for dynamic panel data models, both microeconomic (large and with more than one cross-sectional dimension) and macroeconomic (over a long time span). Specifically, they can: - critically understand theoretical and applied aspects of the vast literature based on dynamic panel data models; - apply dynamic panel data models techniques to their own analyses by programming specific routines using the STATA software.

Course contents

Nowadays panel datasets, intended as both time-series cross-sectional data (CSTS) and multilevel data with observations at higher- and lower-levels, permeate the empirical research on many topics, going from classical economics towards behavioral and political economy. The aim of the course is to provide an overview, both methodological and applied, of econometric models for panel data, where observations are available at least at two dimensions. During the course, to ease the comprehension and to introduce important topics, N will indicate individuals (cross-sections) and T will denote temporal periods (time-series). The first part of the course relates to micro panel data (where N is larger than T). After reviewing the classical fixed and random effects models with emphasis to their pros and cons, we will discuss about endogeneity of explanatory variables, intended both as correlation with individual heterogeneity (the heterogeneity bias) and as correlation with idiosyncratic shocks (due to simultaneity, measurement errors, and dynamics). The instrumental variables estimator, such as the Generalized Method of Moments (GMM), is at the core of this part. The second part of the course relates to macro panel data (where T is larger than N). The main issues will be non-stationarity and cointegration, analyzed and discussed in the light of parameters’ heterogeneity and cross correlated effects.

The teaching material will be available soon on the IOL platform


The material (articles, notes, programs, and data-sets) will be distributed during the lectures. Students are invited to have a look at:
Wooldridge J.M. 2020 Introductory Econometrics. A Modern Approach, 7th Edition, Cengage;
Verbeek M. 2017 A guide to Modern Econometrics, Wiley, 5th Edition;
Wooldridge J. M. 2010 Econometric Analysis of Cross-Section and Panel Data, 2nd ed, Cambridge Mass.: MIT Press.

Teaching methods

At each step of the course, the methodologies will be accompanied by hands-on empirical applications with an econometric software (Stata). At the end of the course, participants will be able to critically evaluate the empirical literature based on panel data, and to model and estimate their own issue of interest, according to the problems at hand: static versus dynamic approaches, heterogeneity and clustering, exogeneity versus endogeneity of covariates, GMM, unit roots and long/short run relationships.

Assessment methods

Attending students will discuss empirical applications with the teacher.

Not attending strudents will have a written examination on theoretical and applied issues.

Teaching tools

Theoretical lectures are associated with working sessions; during them students will receive the suggestions needed to run their own empirical analysis. The data-sets and the programming files to perfom applied analyses will be provided during the lectures.

Links to further information


Office hours

See the website of Maria Elena Bontempi