Financial institutions, international finance and risk management

Introductory recalls on the main concepts of statistical inference. Explanation of a phenomenon as a function of one or more concurrent phenomena: statistical regression models. The case of continuous dependent variable and the linear regression model (simple and multiple). Inferential procedures in linear regression analysis. Misspecification tests. Case study and identification of specification problems in it. The case of limited dependent variables and the corresponding nonlinear models: the binary response model for binary dependent variables; the ordered response model for dependent variables with multiple ordered categories; the multinomial model for dependent variables with multiple unordered categories; the Poisson model for discrete quantitative dependent variables (with count values) and its possible generalizations (ZIP and Hurdle models). Some notions of panel data models and time series analysis. The time devoted to each part of the program may vary from time to time depending on the students' feedback. For attending students, an essential part of the lectures is the use of the R statistical software in the laboratory, which takes at least a third of the lectures

Basic statistical course and basic notions of calculus

A possible textbook will be indicated later, before the start of the course. In any case, students may refer to the materials provided by the lecturer.

Attendance is not mandatory and is expected to be in presence

The evaluation aims to assess the knowledge acquired by the student, his skills in explaining theoretical concepts using the appropriate terminology, in quantitative analysis of real data (applying the most appropriate statistical tools) and in the critical interpretation of the results obtained in the statistical analysis. The evaluation is based on a written exam and a short oral exam. Instead of the written exam, attending students can take part in a project, aimed at the analysis of real data, to be completed with the drafting and presentation of a short paper. The written exam, lasting two hours, includes exercises, closed form questions and possibly open form questions. In particular, multiple-choice questions aim to test skills in the interpretation and in the critical evaluation of the results obtained applying statistical tools; numerical exercises aim at evaluating the skills in the use of statistical tools in a quantitative analysis to exhaustively summarize data and to study the relationships between two or more variables; open form questions mainly aim at evaluating the knowledge acquired on the methodological aspects of the program and students’ skills in discriminating among different statistical procedures. The short paper is based on the statistical software R and deals with real data analysis, specification and estimation of a regression model and the critical evaluation of the obtained results. The oral exam consists of some questions about the theoretical and methodological aspects of the program and allows to test students’ skills in explaining concepts with appropriate terminology. The final mark, expressed in thirtieths, is obtained as average score of the marks obtained in each partial evaluation. To pass the exam students must demonstrate to have acquired a basic knowledge of the topics and must get at least 18/30. To obtain the maximum grade students must demonstrate to have acquired an excellent knowledge on all the topics, language skills, ability to link the various topics and to apply the acquired knowledge to real problems.

The theoretical lectures are conducted in a traditional way. Lectures devoted to empirical analysis of real data are conducted on the computer, using the R software.

Introductory recalls on the main concepts of statistical inference. Explanation of a phenomenon as a function of one or more concurrent phenomena: statistical regression models. The case of continuous dependent variable and the linear regression model (simple and multiple). Inferential procedures in linear regression analysis. Misspecification tests. Case study and identification of specification problems in it. The case of limited dependent variables and the corresponding nonlinear models: the binary response model for binary dependent variables; the ordered response model for dependent variables with multiple ordered categories; the multinomial model for dependent variables with multiple unordered categories; the Poisson model for discrete quantitative dependent variables (with count values) and its possible generalizations (ZIP and Hurdle models). Some notions of panel data models and time series analysis. The time devoted to each part of the program may vary from time to time depending on the students' feedback. For attending students, an essential part of the lectures is the use of the R statistical software in the laboratory, which takes at least a third of the lectures

Basic statistical course and basic notions of calculus

A possible textbook will be indicated later, before the start of the course. In any case, students may refer to the materials provided by the lecturer.

Attendance is not mandatory and is expected to be in presence

The evaluation aims to assess the knowledge acquired by the student, his skills in explaining theoretical concepts using the appropriate terminology, in quantitative analysis of real data (applying the most appropriate statistical tools) and in the critical interpretation of the results obtained in the statistical analysis. The evaluation is based on a written exam and a short oral exam. Instead of the written exam, attending students can take part in a project, aimed at the analysis of real data, to be completed with the drafting and presentation of a short paper. The written exam, lasting two hours, includes exercises, closed form questions and possibly open form questions. In particular, multiple-choice questions aim to test skills in the interpretation and in the critical evaluation of the results obtained applying statistical tools; numerical exercises aim at evaluating the skills in the use of statistical tools in a quantitative analysis to exhaustively summarize data and to study the relationships between two or more variables; open form questions mainly aim at evaluating the knowledge acquired on the methodological aspects of the program and students’ skills in discriminating among different statistical procedures. The short paper is based on the statistical software R and deals with real data analysis, specification and estimation of a regression model and the critical evaluation of the obtained results. The oral exam consists of some questions about the theoretical and methodological aspects of the program and allows to test students’ skills in explaining concepts with appropriate terminology. The final mark, expressed in thirtieths, is obtained as average score of the marks obtained in each partial evaluation. To pass the exam students must demonstrate to have acquired a basic knowledge of the topics and must get at least 18/30. To obtain the maximum grade students must demonstrate to have acquired an excellent knowledge on all the topics, language skills, ability to link the various topics and to apply the acquired knowledge to real problems.

The theoretical lectures are conducted in a traditional way. Lectures devoted to empirical analysis of real data are conducted on the computer, using the R software.