92816 - Analysis Of Panel Data: Methods And Applications

Course Unit Page

  • Teacher Maria Elena Bontempi

  • Credits 5

  • SSD SECS-P/05

  • Teaching Mode Traditional lectures

  • Language Italian

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Economics and Economic Policy (cod. 8420)


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 2021/2022

Learning outcomes

The lectures aim to provide an overview, both methodological and applied, of the econometric models for panel data. In a panel dataset we have at least two dimensions of the observations: classically we can have time-series cross-sectional data (CSTS); recently we talk about multilevel data (for example, companies within industries within regions within countries, observed over time). Models can be both static and dynamic. At the beginning of the course, to ease the comprehension and to introduce important topics, some introductory econometrics will be revised. At the end of the course, students are expected to be able to: (1) understand, estimate and interpret the main models for panel data; (2) use an econometric software and conduct their own research; (3) critically evaluate some applications of panel data presented in the empirical literature.

Course contents

The schedule of the program goes along the following points:

1. Introduction to panel data; longitudinal data, cross-section time-series data, multilevel data.
2. The fundamental role of heterogeneity: how to deal with it.
3. Static panel data models: two or more levels, the error components; fixed and random effects; instrumental variables estimation and Hausman test.
4. Mixed and hierarchical models. Introduction to dynamic panel.


The material (articles, notes, programs and data-sets) will be distributed during the lectures and make available on the platform Virtuale.

Students are invited to have a look at the textbooks listed below (the order indicates an increasing level of difficulty):
Wooldridge J.M. 2020 Introductory Econometrics. A Modern Approach, Cengage, 7th Edition, Ch 13-14;
Verbeek M. 2017 A guide to Modern Econometrics, Wiley, 5th Edition, Ch. 10.
A preliminary look at chapters 1, 2, 3 and 4 of the Verbeek textbook could be a useful recap of the required basic econometrics.

Teaching methods

At each step of the course, the theoretical methodologies will be accompanied by hands-on empirical applications with an econometric software (Stata available using the CAMPUS license and students' university credentials).
At the end of the course, participants will be able to understand the main literature on basic panel data, and to model and estimate their own issue of interest, according to the problems at hand: static versus dynamic approaches, POLS, FE; RE; CRE, GMM.

Assessment methods

Attending students will present and discuss empirical applications with the teacher and the class. Each student will receive a research question, the data and the reference papers selected by the teacher; she/he will have to prepare an empirical research report on the assigned topic.

Not attending students will have a written examination on theoretical and applied issues, with open ended questions aimed at assessing their capacity in understanding models' specifications and estimated results, and in evaluating the strengths and weaknesses of alternative estimating methods.

According to the pandemic situation, the exams could be either in presence or online, but this will not alter the assessment methods.

In each case, students have to register in Almaesami so as to receive the link to the virtual class in Zoom. Not attending students will have to access to EOL (Exams Online) by using their institutional credentials.

The possible grades are:
< 18 failed
18-23 sufficient
24-27 good
28-30 very good
30L (cum laude) excellent

Teaching tools

Theoretical lectures are associated with working sessions (students can have their own laptop); 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. The distributed material (articles, notes, programs, and data-sets) will be make available on the Virtuale platform.

STATA software HERE

Office hours

See the website of Maria Elena Bontempi