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.
Type D and Type F activities
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 magistrale in Economics and data analysis - Enrollment from 2025/2026SOFT 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 |
---|---|---|---|
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 (2022/2023)
Teaching code
4S008977
Teacher
Coordinator
Credits
9
Language
English
Scientific Disciplinary Sector (SSD)
SECS-P/05 - ECONOMETRICS
Period
Secondo semestre (lauree magistrali) dal Feb 20, 2023 al May 19, 2023.
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