Learning goals.
The aim of the course is to introduce students to the main methods of analysis and forecasting of the economic and financial time series.
In particular, it covers
i) Linear stochastic processes. Stationarity. Invertibility. Causality. ARMA processes. Identification, estimation, interpretation and forecasting.
ii) Measurement and analysis of volatility. ARCH and GARCH models. Identification, estimation, interpretation and forecasting.
Knowledge of the econometric theory for cross-section analysis, inference and probability theory is a prerequisite.

Knowledge and understanding.
After attending the course the students know and understand the main problems related to time series (for example: absence of stationarity) and the main methods to be used to solve such problems (for example: unit root tests).

Applying knowledge and understanding.
At the end of the course the students are able to formalize real problems in terms of linear regression models and to apply the methods specific to the discipline to solve them.
They are also able to apply the methods to concrete situations and to interpret the results.

Making judgements.
Students develop a knowledge of the analytical properties of the presented methodologies and the ability to build programs for their implementation.
They also learn to critically interpret the results obtained by applying the procedures to concrete situations.

Communication skills.
Students acquire the technical-scientific language of the discipline, which it must be used appropriately in both the intermediate and final written tests and in the oral tests.
Communication skills are also developed through group activities.

Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle the study of analytical properties in more complex modeling contexts in subsequent quantitative area teachings.
They are also able to produce sound empirical analyzes and forecasts.