Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

A.A. 2024/2025 A.A. 2023/2024 A.A. 2022/2023 A.A. 2021/2022 A.A. 2020/2021 A.A. 2019/2020 A.A. 2018/2019 A.A. 2017/2018 A.A. 2016/2017 A.A. 2015/2016 A.A. 2014/2015 A.A. 2013/2014 A.A. 2012/2013 A.A. 2011/2012 A.A. 2010/2011 A.A. 2009/2010

This information is intended exclusively for students already enrolled in this course.
If you are a new student interested in enrolling, you can find information about the course of study on the course page:

Laurea in Matematica applicata - Enrollment from 2025/2026

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

CURRICULUM TIPO:

1° Year 

ModulesCreditsTAFSSD
12
A
MAT/02 ,MAT/03
12
A
MAT/05
6
A
MAT/08
12
A
FIS/01
12
A
INF/01
6
B
MAT/01

2° Year   activated in the A.Y. 2021/2022

ModulesCreditsTAFSSD
6
A
MAT/02
12
B
MAT/05
6
B
MAT/08
6
B
MAT/03
6
C
SECS-P/01
6
C
SECS-P/01
9
B
MAT/06
6
B
MAT/05
English language B1 level
6
E
-

3° Year   activated in the A.Y. 2022/2023

ModulesCreditsTAFSSD
6
C
SECS-P/05
9
C
SECS-S/06
6
B
MAT/08
6
B
MAT/09
6
B
MAT/06
Final exam
6
E
-
activated in the A.Y. 2021/2022
ModulesCreditsTAFSSD
6
A
MAT/02
12
B
MAT/05
6
B
MAT/08
6
B
MAT/03
6
C
SECS-P/01
6
C
SECS-P/01
9
B
MAT/06
6
B
MAT/05
English language B1 level
6
E
-
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
6
C
SECS-P/05
9
C
SECS-S/06
6
B
MAT/08
6
B
MAT/09
6
B
MAT/06
Final exam
6
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°- 3°
12
D
-
Between the years: 1°- 2°- 3°
Other activities
6
F
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Econometrics  (2022/2023)

Teaching code

4S01951

Teacher

Diego Lubian

Coordinator

Diego Lubian

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

SECS-P/05 - ECONOMETRICS

Period

Semester 1 dal Oct 3, 2022 al Jan 27, 2023.

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

Statistic tools and economic theory will be applied in order to provide students with capabilities to understand and perform empirical analysis of economic phenomena. Empirical problems and applications will be discussed during the course to provide students with the tools needed for the analysis of economic data.

Prerequisites and basic notions

We require basic knowledge of calculus. The course material relies on prior knowledge of basic statistics and probability theory.

Program

1. INTRODUCTION (Stock-Watson, ch.2-3)
1.1. What is econometrics?
1.2. Probability
1.3. Statistics
2. REGRESSION ANALYSIS (Stock-Watson, ch.4-9)
2.1. Linear regressione with a single regressor and hypothesis testing
2.2. Linear regression with multiple regressions and hypothesis testing
2.3. Diagnostics of the regression model: specification, heteroskedasticity, autocorrelation
3. EXTENSIONS (Stock-Watson, ch.11-12)
3.1. Regression with instrumental variables
3.2. Regression with binary dependent variable

Bibliography

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Didactic methods

This module is composed of 52 hours of frontal lectures and exercises. During the semester students will be given problem sets to attempt at home to encourage systematic studying and self-feedback.
The rights of students will be preserved in situations of travel limitation or confinement due to national provisions to combat COVID or in particular situations of fragile health. In these cases, you are invited to contact the teacher directly to organize the most appropriate remedial strategies.

Learning assessment procedures

The exam is made of one written essay and one individual homework. In order to pass the exam, it is necessary to obtain a grade not below 18/30 in the written essay.
The homework can be of two types (Homework 1 and Homework 2). Each student is free to choose either Homework 1 or Homework 2 but must deliver one of them. Once the deadline for delivery of Homework 2 has expired, it is possible to deliver Homework 1 only. The homework grade remains valid throughout the academic year.
Homework 1
The homework aims to develop critical skills with respect to empirical applications. Each student is free to choose one article from www.lavoce.info, www.voxeu.org/, www.ilsole24ore.com or other webiste, provided that it discusses an economic topic and makes use of data.
The homework consists in an essay of max. 2000 words, to be delivered to the address diego.lubian[at]univr.it within the day in which the exam is scheduled. The homework will pass through an antiplagiarism analysis by means of the Compilation software; it is advisable to make a personal preliminary analysis before submitting the homework.
The essay must be divided in sections in such a way to contain a) a reference to the chosen article (title, authors, link), b) a summary of the article, briefly describing its motivation, goal, methodology and results, and c) a critical comment on the methodology, also proposing alternative analyses and possible future developments. The essay must also report the word count.
Homework 2
The homework aims to develop analytical skills through personal data analysis in Gretl. Any student interested in this homework must write to the address diego.lubian[at]univr.it communicating name, surname and ID number. He or she will then receive a number, corresponding to the dataset to be used. The text of the homework will be made available at the end of the lectures; the solution must be delivered by email within the following three days.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

To obtain full marks, students should show knowledge of the various econometric methodologies to understand and solve the diverse issues posed by regression models.

Criteria for the composition of the final grade

The exam is made of one written essay and one individual homework; the final grade is given by the average of the grades in the essay and the homework, with 75% and 25% weights respectively.

Exam language

Italiano