Corso di studio in Economia e Management

This class contributes to the overall objectives of the degree and in particular to the objective of training graduates with a multidisciplinary approach in the areas of macro and microeconomics, quantitative skills and management studies.

Further, this class focuses on analysing empirical data in the international context and therefore is aligned with the overall degree’s objective of training graduates able to act in the the international scenario.

The class’s specific objectives include:

• Become proficient in data analysis and its applications to business, economics and policy.
• Equip students with the most important tools, methods and skills for data analysis, with a strong focus on case studies and real-life examples.
• Learn the most widely used statistical and econometric methods to perform exploratory data analysis, data visualization, hypothesis testing and basic regression analysis.
• Apply the theoretical concepts learned to real-life data and case studies, which will provide students with the opportunity to apply the tools and methods learnt in the course.

The software used in the class will be Stata, which MUST be downloaded for free before the start of the class at: https://www.unito.it/servizi/servizi-line/licenze-software-campus-di-ateneo

1. Knowledge and Understanding

At the end of the class, students will be able to have a good understanding of the most important statistical concepts and their application using STATA. This knowledge will provide a basis for originality in developing and/or applying ideas, within a research context.

2. Applying knowledge and understanding

At the end of the course, students can apply their knowledge and understanding of statistical data analysis (e.g. data exploration and description; hypothesis testing; regression analysis, etc), and problem solving abilities in new or unfamiliar environments with in broader (or multidisciplinary) contexts related to their field of study. This is also requested in Assessment 1 and 2

3. Making judgements

At the end of this class, students will have the ability to integrate their statistical knowledge and handle complexity, and formulate judgements with incomplete or limited information, but that include reflecting on social and ethical responsibilities linked to the application of their knowledge and judgements (e.g. data manipulation; sample construction; generalisation of findings etc)

4. Communication Skills

At the end of this class, students can communicate their conclusions related to data analysis and hypothesis testing, and the knowledge and rationale underpinning these, to specialist and non-specialist audiences clearly and unambiguously, as requested in Assessment 1 and 2

5. Learning skills

At the end of this course, students will have developed those learning skills that are necessary for them to continue to undertake further study in the field of statistics and data analysis with a high degree of autonomy. They will also be able to learn more complex data analysis techniques, based on the knowledge acquired with this class. These skills wll be demostrated in the final exam.

• Origins of Data
• Preparing Data for Analysis
• Exploratory Data Analysis
• Comparison and Correlation
• Generalizing from Data
• Testing hypotheses
• Regression analysis – Simple regression
• Generalizing results of a regression
• Multiple linear regression
• Modelling probabilities
• Regression with Time Series Data
• Causal Analysis
• Introduction to Panel Data

Lectures will be in-person. Attendance is highly recommended, because we will work through case studies in class and practice using STATA together. Students will engage in a variety of group activities in class including preparing for assessments; working on data analysis; solve problem sets; exchange ideas on complex problems related to data analysis. Time devoted to group activities will vary depending on the class size and needs.

All practical activities in class will be finalised to verify understanding of the most important concepts explained in the lectures and to support students in preparation for all their assessments.

There will be two assessments for students who regularly attend classes. These assessments aim to evaluate the achievement of the class learning objectives and the progress made during the course.

1. Midterm test (40% of the final grade) based on the material explained and discussed during the lessons. The material will include theoretical explanations and practical applications, including empirical articles related to data analysis in an economic context. Students will have the opportunity to prepare for the test in class and to discuss questions and doubts in groups and with the instructor during lessons.

2. Final exam (60% of the final grade) with some open-ended questions on the analysis and interpretation of empirical data. Students will work in small groups during class to prepare for the final exam, which will include both theoretical questions and questions related to empirical data applications.

Students who are unable to attend classes regularly will take a standard exam during the exam sessions (100% of the final grade). The exam will be based on the lecture slides, and all relevant study materials will be available on Moodle.