32173 - Applied Econometrics

Course Unit Page


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

Quality education Affordable and clean energy Decent work and economic growth Climate Action

Academic Year 2022/2023

Learning outcomes

Students will learn the econometric methods used in applied research and the skills to perform their own empirical research using different econometric packages and software. Particularly, students will be able: - to use linear regression model in different contexts and to critically evaluate its principal economic applications; - to learn the basic methods of time series and dynamical models; - to acquire knowledge of the basic techniques of cross sections and panel data.

Course contents

Part 1

Introduction to the linear regression model.

Examples of interest.

Interpretation and use of regression coefficients.

Compact matrix representation.

Distinction between cross-section data and time series data.

Distinction between the classic linear regression model and the generalized linear regression model.

Estimation problem: OLS estimation and properties of the estimator.

Diagnostic analysis of a linear regression model: main tests.

Testing restrictions on the parameters.

Endogenous regressors: introduction to the IV estimator.

Causal effect detection: short account with a focus on environmental/climate econometric issues



Part 2

Introduction to Time Series Models:

- Stationary time series models
- AutoRegressive (AR) models
- Moving Average (MA) models
- AutoRegressive and Moving Average (ARMA) models
- Non-stationarity and Unit root
- Autoregressive Conditional Heteroskedasticity models
- Empirical applications on consumptions and prices of commodities


Slides provided by the teacher which will be available online

Verbeek, M. (2000), Modern econometrics, Wiley, The relevant chapters will be indicated by the teacher

Teaching methods

Classes and virtual labs (where by "virtual lab " it is intended a scenario in which the students bring their laptops in the classroom using free or Unibo lincenzed econmetric softwares)


Before attending the course, the student is supposed to understand relatively well the basic ingredients of empirical quantitative analysis summarized in the Chapters 2 and 3 of the textbook:

Verbeek, M. (2000), Modern econometrics, Wiley, The relevant chapters will be indicated by the teacher.

It it is not so, the student is required to fill the gap by preliminary contacting the professor or the tutor of the teacher of the Crash course.

Assessment methods

The exam aims to verify that the student has achieved the following basic targets:

• knowledge of the basic econometric models which can be applied in the environmental context;

• the ability to distinguish between the application of the classic and generalized linear regression model;

• undestanding when the OLS estimator can not be applied and therefore alternative estimation tecniques must be used;

• understanding when OLS can not be used and when and how the IV estimator can be used to estimate the causal effects of interest.

• the main topics of time series econometrics and panel models.

The exam is written and a grade of the form xx/30 is given (see below) 

The final grade will be an average of the grades the students take in Part1 and Part2, respectively. The grade will also incorporate the evaluation the students obtain from the Crash course of Econometrics provided by the RESD program.

Students are supposed to do theoretical exercises but also discuss practical cases based on estimation outputs which refer to real markets.

Overall, the meaning of grades is as follows

<18 failed
18-23 sufficient
24-27 good
28-30 very good
30 e lode excellent.

In case the course will be held online the exam will consist in the development of exercises that will be sent online to the professor according to the rules that will be negotiated 

Teaching tools

Labs and use of econometric packages: Gretl (freely downloadable)

Office hours

See the website of Luca Fanelli

See the website of Giovanni Angelini