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.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
Period | From | To |
---|---|---|
Periodo generico | Oct 1, 2022 | May 31, 2023 |
Primo semestre (lauree magistrali) | Oct 3, 2022 | Dec 23, 2022 |
Secondo semestre (lauree magistrali) | Feb 20, 2023 | May 19, 2023 |
Session | From | To |
---|---|---|
Sessione invernale (lauree magistrali) | Jan 9, 2023 | Feb 17, 2023 |
Sessione estiva (lauree magistrali) | May 22, 2023 | Jul 7, 2023 |
Sessione autunnale (lauree magistrali) | Aug 28, 2023 | Sep 22, 2023 |
Session | From | To |
---|---|---|
Sessione autunnale | Dec 5, 2022 | Dec 7, 2022 |
Sessione invernale | Apr 4, 2023 | Apr 6, 2023 |
Sessione estiva | Sep 5, 2023 | Sep 7, 2023 |
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.
Should you have any doubts or questions, please check the Enrollment FAQs
Academic staff
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 enrollment year.
1° Year
Modules | Credits | TAF | SSD |
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2° Year activated in the A.Y. 2023/2024
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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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.
Type D and Type F activities
SOFT SKILLS
Find out more about the Soft Skills courses for Univr students provided by the University's Teaching and Learning Centre: https://talc.univr.it/it/competenze-trasversali
CONTAMINATION LAB
The Contamination Lab Verona (CLab Verona) is an experiential course with modules on innovation and enterprise culture that offers the opportunity to work in teams with students from all areas to solve challenges set by companies and organisations.
Upon completion of a CLab, students will be entitled to receive 6 CFU (D- or F-type credits).
Find out more: https://www.univr.it/clabverona
PLEASE NOTE: In order to be admitted to any teaching activities, including those of your choice, you must be enrolled in the academic year in which the activities in question are offered. Students who are about to graduate in the December and April sessions are therefore advised NOT to undertake extracurricular activities in the new academic year in which they are not enrolled, as these graduation sessions are valid for students enrolled in the previous academic year. Therefore, students who undertake an activity in an academic year in which they are not enrolled will not be granted CFU credits.
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Ciclo tematico di conferenze: “Conflitti. Riconoscere, prevenire, gestire” - 2022/2023 | D |
Riccardo Stacchezzini
(Coordinator)
|
1° 2° | Securitisation transactions - Focus on securitisations of OF NPL / NPE /UTP | D |
Michele De Mari
(Coordinator)
|
1° 2° | The Fashion Lab - 2022/23 | D |
Caterina Fratea
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Economic Thinking and Thesis Writing | D |
Marco Minozzo
(Coordinator)
|
1° 2° | Data Analysis Laboratory with R (Verona) | D |
Marco Minozzo
(Coordinator)
|
1° 2° | Data Visualization Laboratory | D |
Marco Minozzo
(Coordinator)
|
1° 2° | Python Laboratory | D |
Marco Minozzo
(Coordinator)
|
1° 2° | Data Science Laboratory with SAP | D |
Marco Minozzo
(Coordinator)
|
1° 2° | Advanced Excel Laboratory (Verona) | D |
Marco Minozzo
(Coordinator)
|
1° 2° | Excel Laboratory (Verona) | D |
Marco Minozzo
(Coordinator)
|
1° 2° | Laboratory on research methods for business | D |
Cristina Florio
(Coordinator)
|
1° 2° | Laboratory on research methods for business | D |
Cristina Florio
(Coordinator)
|
1° 2° | Piano di marketing 2022/23 | D |
Fabio Cassia
(Coordinator)
|
1° 2° | Programming in Mathlab | D |
Marco Minozzo
(Coordinator)
|
1° 2° | Programming in SAS | D |
Marco Minozzo
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Business & predictive analytics for International Firms (with Excel Applications) - 2022/23 | D |
Angelo Zago
(Coordinator)
|
1° 2° | Elements of Financial Risk Management - 2022/23 | D |
Claudio Zoli
(Coordinator)
|
1° 2° | English for business and economics | F |
Claudio Zoli
(Coordinator)
|
1° 2° | Introduction to Business Plan - 2022/23 | D |
Paolo Roffia
(Coordinator)
|
1° 2° | Soft skills training for economics - 2022/23 | D |
Claudio Zoli
(Coordinator)
|
1° 2° | Topics in applied economics and finance - 2022/23 | D |
Claudio Zoli
(Coordinator)
|
1° 2° | Experience 3 Days as a Manager | D |
Riccardo Stacchezzini
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Data Discovery for Business Decisions 2022/2023 | D |
Claudio Zoli
(Coordinator)
|
1° 2° | The Chartered Accountant as a business consultant | D |
Riccardo Stacchezzini
(Coordinator)
|
1° 2° | Integrated Financial Planning 2022/2023 | D |
Riccardo Stacchezzini
(Coordinator)
|
1° 2° | Predictive Analytics for Business Decisions 2022/2023 | D |
Claudio Zoli
(Coordinator)
|
1° 2° | Professional Communication for Economics 2022/2023 | D |
Claudio Zoli
(Coordinator)
|
years | Modules | TAF | Teacher |
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1° 2° | Project "B-EDUCATION: ideas that count" - 1 cfu | D |
Roberto Bottiglia
(Coordinator)
|
1° 2° | Project "B-EDUCATION: ideas that count" - 2 cfu | D |
Roberto Bottiglia
(Coordinator)
|
Time series and forecasting (2023/2024)
Teaching code
4S008977
Teacher
Coordinator
Credits
9
Language
English
Scientific Disciplinary Sector (SSD)
SECS-P/05 - ECONOMETRICS
Period
Secondo semestre (lauree magistrali) dal Feb 26, 2024 al May 24, 2024.
Courses Single
Authorized
Learning objectives
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.
Prerequisites and basic notions
Knowledge of the following elementary topics is required:
- Calculus: derivatives, integrals, series
- Linear algebra: matrices, rank, systems of equations
- Descriptive and inferential statistics
Program
1. Introductory topics
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
Bibliography
Didactic methods
The course aims to provide an overview of the main tools of time-series analysis, with a special emphasis on applications involving the forecast of economic, financial, and business data. The main topics of the course are introduced following a bottom-up approach, starting from motivational examples and discussing in a second step the methodology in rigorous form. Applications are illustrated using publicly available datasets and the MATLAB software.
Learning assessment procedures
The exam consists of a written exam and a group homework that will be assigned to students at the end of the course.
Evaluation criteria
It is assessed the understanding of the main topics of the course, the capacity to communicate them in a rigourous form, as well as the ability to apply them in practice through the use of data.
Criteria for the composition of the final grade
70% written exam + 30% group project
Exam language
English
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: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and soon also via the Univr app.
Graduation
List of thesis proposals
theses proposals | Research area |
---|---|
PMI (SMES) and financial performance | MANAGEMENT OF ENTERPRISES - MANAGEMENT OF ENTERPRISES |
Corporate governance, financial performance and international business | Various topics |
Doppio Titolo
Grazie ad una rete di accordi con Atenei esteri, l’Università di Verona offre percorsi formativi internazionali che consentono l’acquisizione di un doppio titolo di studio. L’ammissione ad un CdS a doppio titolo consente di conseguire contemporaneamente, nel tempo di un normale ciclo di studi (di cui una parte viene svolta all'estero), sia il titolo di studio dell’Università di Verona che il titolo rilasciato dall'Ateneo partner, garantendo di vedere riconosciuto il diploma di laurea in entrambi i Paesi.
L'accesso al doppio titolo (così come l’eventuale sostegno finanziario) è regolato da uno specifico bando, e il numero di posti è limitato.