Course Topics
- R computing environment - Quarto with R - Exploratory statistics - Statistical distributions - Statistical inference (point estimation, confidence intervals, test of hypothesis) - Linear regression, including model diagnostics and inference - Logistic regression, including model diagnostics and inference - Panel data analysis, including model diagnostics and inference - Difference-in-difference inference - Propensity score matching - Heckman model

Teaching format
Traditional classes, mixing statistical theory and practice using R.

Educational objectives
ILO (Intended Learning Outcomes) ILO 1 – Knowledge and Understanding: ILO 1.1 of the theories and tools for the economic analysis of firms and markets. ILO 1.2 of basic forecasting models for conducting integrated economic and financial analyses, also using econometric methodologies for time series and multivariate analysis. ILO 2 – Applying Knowledge and Understanding: ILO 2.1 for understanding the evolution of financial markets and changes in the international macroeconomic context. ILO 2.2 for analyzing economic, managerial, and financial variables to support decision-making in companies and financial intermediaries. ILO 3 – Making Judgments: ILO 3.1 ability to relate models and empirical evidence in the study of companies, intermediaries, and financial markets. ILO 4 – Communication Skills: ILO 4 Ability to effectively communicate, both orally and in writing, the specialized content of individual disciplines, using different registers depending on the audience and the communicative and educational purposes, and to assess the educational impact of one’s communication. ILO 5 – Learning Skills: ILO 5.1 ability to identify thematic connections and establish relationships between different cases and contexts of analysis. ILO 5.2 ability to develop general models based on the phenomena studied.

Assessment
Option 1) mid-term + final-term exams. Mid-term topics: statistics, linear and logistic regressions. Final-term topics: panel data analysis, difference-in-difference inference, propensity score matching, Heckman model. This option is valid only for students receiving a sufficient grade at the mid-term exam, and doing the final exam (with sufficient grade) within the academic year; Any other situation leads to option 2). Option 2) final-term exam only. Topics: statistics, linear and logistic regressions, panel data analysis, difference-in-difference inference, propensity score matching, Heckman model. All exams are composed by questions concerning the analysis of real data to be answered using R.

Evaluation criteria
Option 1) mid-term exam: 40%, final-term exam: 60% (ILO1-3) Option 2) final-term exam: 100% (ILO1-3) The two options are defined in the Assessment field. ILO 1-5 assessed

Required readings

Since there is not a unique textbook covering all topics to a level suitable for the course students, the main reference to prepare the exam are lesson notes delivered by the teacher.

Supplementary readings

Dalpiaz D. (2022). Applied Statistics with R, https://book.stat420.org/applied_statistics.pdf

Wasserman L. (2011), All of Statistics: A Concise Course in Statistical Inference https://egrcc.github.io/docs/math/all-of-statistics.pdf

Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach. Nelson Education, 7th ed

Ruppert and D. S. Matteson (2015). Statistics and Data Analysis for Financial Engineering, 2nd ed. Springer https://ethz.ch/content/dam/ethz/special-interest/math/statistics/sfs/Education/Advanced%20Studies%20in%20Applied%20Statistics/course-material-1921/FinancialData/2710528_1_ruppert.pdf

Further information
All course material is made available in OLE