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.

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
primo semestre (lauree magistrali) Oct 5, 2020 Dec 23, 2020
secondo semestre (lauree magistrali) Mar 1, 2021 Jun 1, 2021
Exam sessions
Session From To
sessione invernale Jan 11, 2021 Feb 12, 2021
sessione estiva Jun 7, 2021 Jul 23, 2021
sessione autunnale Aug 23, 2021 Sep 17, 2021
Degree sessions
Session From To
sessione autunnale (validità a.a. 2019/20) Dec 9, 2020 Dec 11, 2020
sessione invernale (validità a.a. 2019/20) Apr 7, 2021 Apr 9, 2021
sessione estiva (validità a.a. 2020/21) Sep 6, 2021 Sep 8, 2021
Holidays
Period From To
Vacanze di Natale Dec 24, 2020 Jan 6, 2021
Vacanze di Pasqua Apr 3, 2021 Apr 6, 2021
Vacanze estive Aug 9, 2021 Aug 15, 2021

Exam calendar

Exam dates and rounds are managed by the relevant Economics Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

B C D F G M N P R S V Z

Bertoli Paola

paola.bertoli@univr.it 045 8028 508

Brunetti Federico

federico.brunetti@univr.it 045 802 8494

Buccheri Giuseppe

giuseppe.buccheri@univr.it 045 8028525

Bucciol Alessandro

alessandro.bucciol@univr.it 045 802 8278

Campolmi Alessia

alessia.campolmi@univr.it 045 802 8071

Cantele Silvia

silvia.cantele@univr.it 045 802 8220 (VR) - 0444 393943 (VI)

Chesini Giuseppina

giusy.chesini@univr.it 045 802 8495 (VR) -- 0444/393938 (VI)

Ciampi Annalisa

annalisa.ciampi@univr.it 045 802 8061

Cipriani Giam Pietro

giampietro.cipriani@univr.it 045 802 8271

De Sinopoli Francesco

francesco.desinopoli@univr.it 045 842 5450

Di Caterina Claudia

claudia.dicaterina@univr.it 0458028247

Florio Cristina

cristina.florio@univr.it 045 802 8296

Fratea Caterina

caterina.fratea@univr.it 045 802 8858

Gaudenzi Barbara

barbara.gaudenzi@univr.it 045 802 8623

Malpede Maurizio

maurizio.malpede@univr.it

Matteazzi Eleonora

eleonora.matteazzi@univr.it 045 8028741

Menon Martina

martina.menon@univr.it 045 802 8420

Minozzo Marco

marco.minozzo@univr.it 045 802 8234

Nicodemo Catia

catia.nicodemo@univr.it +39 045 8028340

Pellegrini Letizia

letizia.pellegrini@univr.it 045 802 8345

Perali Federico

federico.perali@univr.it 045 802 8486

Pertile Paolo

paolo.pertile@univr.it 045 802 8438

Picarelli Athena

athena.picarelli@univr.it 045 8028242

Piovesan Marco

marco.piovesan@univr.it 045.80.28.104

Roffia Paolo

paolo.roffia@univr.it 045 802 8012

Scricciolo Catia

catia.scricciolo@univr.it 045 802 8341

Sommacal Alessandro

alessandro.sommacal@univr.it 045 802 8716

Stacchezzini Riccardo

riccardo.stacchezzini@univr.it 045 802 8186
Virginia Vannucci,  October 25, 2020

Vannucci Virginia

virginia.vannucci@univr.it

Zago Angelo

angelo.zago@univr.it 045 802 8414

Zoli Claudio

claudio.zoli@univr.it 045 802 8479

Study Plan

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 enrolment year.

ModulesCreditsTAFSSD
9
B
SECS-P/05
1 module between the following
Training
3
F
-
ModulesCreditsTAFSSD
2 modules among the following
6
C
SECS-P/03
6
C
SECS-P/02
2 modules among the following
6
B
SECS-P/11
1 module between the following
Final exam
15
E
-

1° Year

ModulesCreditsTAFSSD
9
B
SECS-P/05
1 module between the following
Training
3
F
-

2° Year

ModulesCreditsTAFSSD
2 modules among the following
6
C
SECS-P/03
6
C
SECS-P/02
2 modules among the following
6
B
SECS-P/11
1 module between the following
Final exam
15
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Between the years: 1°- 2°
Further language skills
3
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.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S008977

Coordinatore

Giuseppe Buccheri

Credits

9

Language

English en

Scientific Disciplinary Sector (SSD)

SECS-P/05 - ECONOMETRICS

Period

secondo semestre (lauree magistrali) dal Feb 21, 2022 al May 13, 2022.

Learning outcomes

The module aims to introduce students to time series analysis, in order to understand how economic phenomena evolve over time. It will present the main econometric tools used to make forecasts and assess their accuracy on economic and financial time series. The use of statistical and econometric professional packages will complement the study of theoretical concepts. At the end of the module, students will prove to be able to critically interpret dynamic models for the analysis and forecast of economic and financial variables, in response to real problems.

Program

1. Empirical properties of economic and financial data
Review of univariate and multivariate statistics
Joint, marginal and conditional density
Correlation versus Dependence
The multivariate Normal
Distributional properties of time-series
Non-normality tests
Serial correlation, Ljung-Box and Box-Pierce test statistics
Markov property

2. Stationary linear time-series models I
Weak and strong stationarity
White noise, random walk, random walk with trend
The autocovariance of a weakly stationary process
AR(1) model: conditions for stationarity, autocovariance and autocorrelation.
AR(2) model: vector representation, conditions for stationarity, autocovariance and autocorrelation.

3. Stationary linear time-series models II
The AR(p) model: vector representation, conditions for stationarity, autocovariance and autocorrelation
The Yule-Walker equations
MA(q) model: stationarity, autocovariance and autocorrelation
Invertibility of MA(1) and identification issues
ARMA(p,q) model: stationarity, autocovariance and autocorrelation
The Wold decomposition theorem
Short versus long memory processes

4. Estimation, Identification and Diagnostic
LLN and CLT for dependent process
Consistency and asymptotic normality of the sample mean and sample autocovariance
Yule-Walker estimation of AR(p) processes
OLS estimation of AR(p) process
Violation of strict exogeneity in time-series models
Maximum-likelihood estimation
MLE of sample mean and sample variance under normality
Asymptotic properties of MLE
Conditional Maximum-likelihood estimation
Exact and conditional likelihood estimation of the AR(1) model
Conditional likelihood estimation of the MA(1) model
Quasi-maximum likelihood
Partial autocorrelation and information criteria
Diagnostic

5. Forecasting
Loss functions and mean square error
Forecasting based on conditional expectations
Forecasting with AR, MA and ARMA models
Multistep ahead forecasts
Direct versus iterated forecasts
Density forecasts
Some remarks on non-linear time-series models and realized volatility

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Examination Methods

The exam consists of a written exam and a group homework that will be assigned to students at
the end of the course. Each group is formed by a maximum of four students, and is assigned a
different homework. The goal of the homework is to reproduce (a part of) the empirical results of
a scientific paper using a computer code. The final result is a weighted average of the written
exam grade (70%) and the homework grade (30%), with the constraint that a minimum grade of
16/30 in the written exam is required to pass the exam. The homework must be submitted by the written exam date. The homework grade remains valid until the lectures of the following academic year start.

Bibliography

Type D and Type F activities

primo semestre (lauree) From 9/28/20 To 12/23/20
years Modules TAF Teacher
Future matters D Alessandro Bucciol (Coordinatore)
Future matters D Alessandro Bucciol (Coordinatore)
primo semestre (lauree magistrali) From 10/5/20 To 12/23/20
years Modules TAF Teacher
The fashion lab (1 ECTS) D Maria Caterina Baruffi (Coordinatore)
The fashion lab (2 ECTS) D Maria Caterina Baruffi (Coordinatore)
secondo semestre (lauree) From 2/15/21 To 6/1/21
years Modules TAF Teacher
Design and Evaluation of Economic and Social Policies D Federico Perali (Coordinatore)
Public debate and scientific writing - 2020/2021 D Martina Menon (Coordinatore)
Wake up Italia - 2020/2021 D Sergio Noto (Coordinatore)
secondo semestre (lauree magistrali) From 3/1/21 To 6/1/21
years Modules TAF Teacher
Professional Communication for Economics D Claudio Zoli (Coordinatore)
1° 2° Business analytics: make your data make an impact - 2020/2021 D Claudio Zoli (Coordinatore)
List of courses with unassigned period
years Modules TAF Teacher
Ciclo di video conferenze: "L’economia del Covid, Verona e l’Italia. Una pandemia che viene da lontano?" - 2020/21 D Sergio Noto (Coordinatore)
Ciclo tematico di conferenze (on-line): “Come saremo? Ripensare il mondo dopo il 2020” - 2020/21 D Federico Brunetti (Coordinatore)
Elements of financial risk management D Claudio Zoli (Coordinatore)
Integrated Financial Planning - 2020/21 D Riccardo Stacchezzini (Coordinatore)
Introduction to business plan - 2020/21 D Paolo Roffia (Coordinatore)
The fashion lab (3 ECTS) D Not yet assigned
Marketing plan - 2020/21 D Virginia Vannucci (Coordinatore)
1° 2° Data Analysis Laboratory with R (Verona) D Marco Minozzo (Coordinatore)
1° 2° Data Visualization Laboratory D Marco Minozzo (Coordinatore)
1° 2° Python Laboratory D Marco Minozzo (Coordinatore)
1° 2° Advanced Excel Laboratory (Verona) D Marco Minozzo (Coordinatore)
1° 2° Programming in Matlab D Marco Minozzo (Coordinatore)
1° 2° Programming in SAS D Marco Minozzo (Coordinatore)
1° 2° 3° Excel Laboratory (Verona) D Marco Minozzo (Coordinatore)

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.


Linguistic training CLA


Graduation


Internships


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