Argomenti dell'insegnamento
M1 - Basics of stochastic processes theory and characteristics of time series data - Smoothing, filtering and decomposing a time series - Introduction to AR, MA, ARIMA and SARIMA models - Maximum likelihood estimation - Box & Jenkins procedure to analyse a time series - Forecasting methods: time series forecasting, density forecasting, forecasting from ARIMA models - Volatility models: ARCH and GARCH models and forecasting - Case studies M2 TBD

Modalità di insegnamento
Lectures, pre-recorded videos, and laboratory sessions. The course adopts a blended, student-centred approach that emphasises problem-based learning and active engagement. A portion of the lecture content is made available online in advance, allowing students to explore key concepts independently and at their own pace before attending class. This preparatory work enables inperson sessions to focus on the application of knowledge through real-world problems, collaborative activities, and guided discussions - fostering critical thinking and deeper learning. The course is fully aligned with the principles of the Italian Universities Digital Hub (EDUNEXT) initiative (https://edunext.eu), which promotes the integration of digital resources and active learning strategies within university teaching.

Obiettivi formativi e risultati di apprendimento (ulteriori info.)
M1 The course will provide students with the ability to analyze and interpret data using econometric models. 1) Knowledge and understanding. The course will equip students with the ability to organize and combine economic and business data starting from structured databases. It will also enable students to acquire knowledge about state-of-the-art of models to represent time series data. 2) Applying knowledge and understanding: Students will be able to implement data management techniques and econometric models in order to extract proper information from data, useful to analyse real phenomena in several fields of economics and management, and to understand their most important aspects. 3) Making judgements: students who successfully complete this course will be able to select the most appropriate data management approaches and apply proficiently statistical model to obtain inferences and predictions using statistical software, and organize results in order to draw conclusions and decide in uncertain situations, like in specific economic and business situations. 4) Communication skills: students who successfully complete this course will be able to communicate, to experts and non-experts the results of their analyses using specific software. 5) Learning skills: the course is aimed to provide the methodological and applied knowledge of data management for subsequent econometric modeling, and necessary to address subsequent analyses. M2 TBD

Modalità d'esame
The overall exam mark will be determined by the assessment of the two modules (M1+M2) M1 Attending students: Written exam composed of exercises and theoretical questions (50% of the final grade), group project and presentation (50% of the final grade). Non-attending students: Written exam composed of exercises, theoretical questions, tasks related to data analysis (100% of the final grade). M2 TBD

Criteri di valutazione
M1 Attending students: 50% written exam (consisting of theoretical questions and exercises), 50% group project report (consisting of analysis tasks on data sets assigned during the semester to be carried out through the use of statistical software) and presentation of the project. Non-attending students: 100% written exam consisting of theoretical questions, exercises, and data analysis tasks. Evaluation criteria for both written exams and projects: clarity in exposition, knowledge and understanding of statistical methods, ability to apply appropriate statistical procedures, correctness of results. M2 TBD