LABORATORY OF STATISTICS FOR INTERNATIONAL MARKETS ANALYSIS

The course aims at equipping the students with the statistical tools most suitable for the evaluation of international markets, starting from economic, financial, socio-economic and institutional data which are relevant for firms decisions.

The students, at the end of the course should learn:
- which are the data sources relevant for the international market analysis
- select the relevant variables to support firm internazionalization
- analyse macroeconomic variables to profile foreign regions and Countries in terms of risks/opportunities for firms

- Basic knowledge of statistics and probability (with particular reference to simple regression)
- basic knowledge of R

- Relevant data sources for international market analysis
- Data cleaning: statistical tools for missing data imputation
- Selection of the relevant variables: stepwise methods, shrinkage methods
- Reduction to essential dimensions: principal components and principal component regression, partial least squares
- Protocols for parameter tuning: Validation and cross-validation

The main referral text for statistical methods is:
G. James, et al. An introduction to statistical learning: with applications in R. 2nd edition Springer, 2023.

Class notes, commented R scripts and other materials will be uploaded in the moodle page of the course

The exam will consist in the writing of a report in which the students will analyze, through R, the international markets offering the best opportunities for a specific region or country. Students will be evaluated both on the way they apply the R code to the new data and on the comments provided to highlight the results.

written

Assessment Scheme

A. Grades between 18 and 20 are assigned if the student demonstrates the ability to apply the R code seen in class to new data at the country level, providing a ranking of nations based on their best model.

B. Grades between 21 and 26 are assigned if the student, in addition to meeting the requirements of level A, provides appropriate comments on the data, models, and output, interpreting the different solutions obtained.

C. Grades between 27 and 30 are assigned if the student, in addition to meeting the requirements of level B, includes a ranking by region at the NUTS 2 level and an analysis of the importance of different variables for the ranking.

D. Lode is awarded to students who reach level C and add originality to their project. This could be achieved by using a different dependent variable and highlighting differences, applying the analysis to other countries or to personally collected data, or by implementing a different model.

Interactive, hands-on approach: lectures and R lab sessions will follow a leading case study