PREDICTIVE BUSINESS AND FINANCE

This course is one of the teaching activities of the Master's Degree Programme in "Data Analytics for Business and Society". In tandem with the educational objectives of this course, students will be exposed to data analytic techniques and methods for handling economic-financial prediction related problems. Precisely, this activity seeks to present the main mathematical and statistical tools necessary for forecasting.

1. Visualize time series data
2. Specify appropriate metrics to assess forecasting models
3. Introduction to basic filtering methods in the time domain (moving average, exponential smoothing)
4. Understand the structural decomposition in components of time series data
5. The use of time series models for forecasting

Mathematical Tools:
Matrix Algebra
Differential Calculus
Integral Calculus

Statistical Tools:
Random Variables and Distribution Theory
Point and Interval Estimation
Hypothesis Testing
Least Squares and Standard Linear Model

1. Introduction to time series decomposition.
2. Basic smoothing and filtering methods.
3. Regression models.
4. Time series models: ARIMA models.
5. Dynamic regression models
6. Advanced forecasting methods

Hyndman, R. J. and Athanasopoulos, G. (2021): Forecasting: Principles and Practice (3rd Edition). https://otexts.com/fpp3/
Shumway, R. H. and Stoffer, D. S. (2017): Time Series Analysis and Its Applications, With R Examples. https://link.springer.com/book/10.1007/978-3-319-52452-8
Bee Dagum, E. and Bianconcini, S. (2016): Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation. (Ch.2-5) https://link.springer.com/book/10.1007/978-3-319-31822-6
Harvey, A. C. (1993): Time Series Models (2nd Edition). https://books.google.ge/books/about/Time_Series_Models.html?id=s1ScQgAACAAJ&redir_esc=y

By way of evaluation, a main examinations covering both the theory and application of the concepts developed in class will be conducted. However, I will also propose an end-of-course project (homework) that examines students' capability in developing a solution to a problem without limiting themselves to the information given in class. The course grade will be based on the homework and a the final examination. The final grade will be determined using the following weights: 30\% Homework, 70\% final written exam.

Series of lectures on the various topics

Inglese

Il corso è svolto in collaborazione con il partenariato esteso GRINS - Growing Resilient, INclusive and Sustainable, codice PE0000018, CUP H73C22000930001, avviso pubblico n. 341/2022 del Piano Nazionale di Ripresa e Resilienza (PNRR), Missione 4 - Componente 2 - Investimento 1.3, finanziato dall’Unione europea - NextGenerationEU.
All’interno del corso possono essere proposti incontri con testimoni aziendali aderenti al progetto, incentrati sullo sviluppo di conoscenze pratiche nella materia oggetto di studio, oltre che sui risultati del progetto stesso.
Questo insegnamento tratta argomenti connessi allo Spoke 4 Sustainable Finance - Work Package n. 3.

scritto

Questo insegnamento tratta argomenti connessi alla macroarea "Cambiamento climatico e energia" e concorre alla realizzazione dei relativi obiettivi ONU dell'Agenda 2030 per lo Sviluppo Sostenibile