The course aims at strengthening the knowledge of statistics and probability obtained during the bachelor, and at teaching how to apply statistical methods in the economic and financial fields.
At the end of the course, the students will gain the ability to:
a) choose, apply and test appropriate statistical methods and models for the analysis of different types of economic and financial data;
b) use the free open-source statistical software R (http://www.r-project.org) for statistical data analysis, as well as for modeling and forecasting economic and financial time series;
c) interpret the results in a decision-making perspective.
There are no formal prerequisites. However, to help students meet the learning objectives of the course, it is strongly recommended that they attend the "crash courses" offered at the beginning of the academic year.
The exam will be the same for both attending and non-attending students, and will consist in:
- multiple-choice questions about theoretical topics or short applications of the studied methods;
- exercises to be solved using the R software, aimed at evaluating the ability of the student in analyzing financial data and interpreting statistical outputs.
These two parts of the exam are each worth 50% of the total score, approximately.
- Financial variables: returns and their distributional properties.
- Review of the main statistical concepts necessary for economic and financial data analysis (e.g. random variables, probability distributions, hypothesis testing, etc.);
- Statistical methods for univariate data (histogram, QQ-plot and normal probability plot, data transformation, distribution parameters, skewness and kurtosis indexes, tests of normality, heavy-tailed distributions) and multivariate data (covariance matrix, multivariate Normal, linear combinations of random variables);
- Multiple linear regression: model estimation, ANOVA, model evaluation and selection, check of model assumptions;
- Stochastic processes and time series: AR, MA, ARMA and ARIMA models (definition, properties, estimation, and forecasting).
Attending class lectures and R labs is strongly recommended.
