The course aims to provide students with the necessary tools for the development of competence in data analysis through the use of the most common statistical-economic methodologies. Students will be able to correctly apply the statistical methodologies for the analysis of complex data and communicate the results obtained.
In the first part of the course, students will deepen their knowledge of statistical inference, with particular attention to hypothesis tests and confidence intervals.
The second part focuses on linear and non-linear regression methodologies and discrete variables. The last part will focus on the analysis of data through the use of models for time variables.
The learning objectives of the course can be declined as follows:
Knowledge and comprehension: the student must be able to understand the methods of data analysis to be used to achieve the required objectives and know how to communicate the results obtained. They are learned through active participation in frontal activities and completed with individual study.
Ability to apply knowledge and understanding: the student must demonstrate that he is able to identify the most suitable analysis methodology for solving the proposed problem and make the necessary reports for the transfer of information to third parties. At the end of the course, the student must be able to analyse and communicate the results of the analyses carried out in order to make decisions in uncertain conditions.
Autonomy of judgement: once the main models of data analysis have been learned, the student will be able to understand the limits and areas of application of each of them. In this way, the student will be able to make critical use of the tools learned.
Communication skills: the student must be able to communicate in a clear, coherent and exhaustive way the characteristics of the data, the methods of analysis and the results obtained. This ability cannot be separated from the correctness of the vocabulary used and the ability to synthesize necessary to communicate the results achieved by the analyses conducted.
Learning ability: the student must demonstrate a good learning ability by being able to deepen their knowledge and interpretation skills on the basis of further relevant specialist texts. The student must demonstrate that he/she is able to apply statistical methodologies correctly and that he/she is familiar with the methods of analysis. They must also have the ability to find procedures and queues for analysis by software.
Basic tools of Statistics.
The teaching programme can be divided into several blocks of lessons, which can be summarised as follows:
Block I (about 16 hours): Descriptive and inferential statistics
Block II (approx. 32 hours): Linear, non-linear and discrete variable regression models.
Block III (approx. 24 hours): Analysis of time data
The main quantitative methods for understanding, measuring and analysing data useful for studying markets, the social and cultural environment and economic factors will be introduced.
After illustrating the basic elements of Statistics, retracing the methodologies of descriptive and inferential analysis, we will proceed with the study and application of statistical-economic modelling for complex data. The regression models useful for analysing markets and context factors will be examined in depth, highlighting the causality between the variables under study with both temporal and sectional data. These will be useful to generate scenarios to predict possible market evolutions.
The course will also deepen the models for the analysis of qualitative data and panels. During the course, the implementation of the illustrated analysis methodologies will be carried out with the help of statistical software.
Descriptive Statistics: frequencies, means and variability.
Nods of Inferential Statistics: hypothesis tests and confidence intervals.
The simple and multiple linear regression model. The model's hypotheses.
Non-linear models. Interaction between explanatory variables.
Analysis of time data: stationarity and integration. Autoregressive and distributed lag models. Multivariate time models: Vector Autoregression and Cointegration.
Dynamic causal effects.
Regression with discrete dependent variables: the logistics model.
Panel data models: pooled, fixed effects and random effects.
Traditional lectures, analysis of case studies.
Stock and Watson, Introduction to econometrics, Pearson Prentice Hall, u.e.
Greene, W., Econometric Analysis, Prentice Hall
The evaluation of the students' level of learning is constantly verified during the course through guided research and continuous comparison between the theoretical aspects of the course and the analysis of data. At the end of the course, the verification test will consist of an oral test that will be based on the content of the study program and will focus on:
- Methodological aspects
- Discussion of the results of an empirical analysis
The vote will be expressed in thirtieths and will combine the above aspects. The exam is considered passed with a minimum score of 18/30. For the maximum score, in addition to an excellent knowledge of the proposed methodologies and a thorough interpretation of the results of the proposed analysis, a correct use of the specialist vocabulary must be demonstrated.
Commented slides on the e-learning portal