Metodi quantitativi e analisi statistica per l'accounting e la finanza

Semestre 1 · 25408 · Corso di laurea magistrale in Accounting e Finanza · 6CFU · EN

The course provides statistical and computational tools useful in accounting and finance applications. The main objectives are:
1) learn R as a computing environment;
2) apply statistical tools already familiar to students (exploratory statistics, statistical distributions, statistical inference, correlation and linear regression) on real data using R;
3) learn new statistical methods frequently used in accounting and finance: logistic regression, repeated cross sections, panel data analysis, difference-in-difference inference, propensity score matching, Heckman model; this is achieved in a practical way by applying them to real data using R.

Docenti: Fabrizio Cipollini
Teaching assistants: Patrick Osatohanmwen

Ore didattica frontale: 36
Ore di laboratorio: -
Obbligo di frequenza: Strongly suggested, but not mandatory

Argomenti dell'insegnamento
- 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

Modalità di insegnamento
Traditional classes, mixing statistical theory and practice using R.

Modalità d'esame
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) in February, 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.

Criteri di valutazione
Option 1) mid-term exam: 40%, final-term exam: 60% Option 2) final-term exam: 100% The two options are defined in the Assessment field.

Bibliografia obbligatoria

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.

Bibliografia facoltativa

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

Altre informazioni
All course material is made available in OLE

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Obiettivi di sviluppo sostenibile
Questa attività didattica contribuisce al raggiungimento dei seguenti Obiettivi di Sviluppo sostenibile.