Prerequisites: students need to take the Statistics course at the Master in Finance since this course builds on knowledge acquired in this preliminary course.
Objectives: The course aims to deepen notions of frequentist inferencial statistics and to introduce the Bayesian approach, both from a theoretical and applied point of view. The students will be able to critically analyze a given data set having in mind research questions and hypothesis to be tested. The freeware statistical software R Project will be used for the applied and data driven part of the course.
Description / Program: the following topics will be critically covered both from a frequentist and from a Bayesian prospective highlighting the pros and cons of the two approaches: advanced point estimation, confidence intervals, hypothesis testing and prediction.
Learning Method / Style of Lessons
There will be theoretical and applied (with R statistical software) frontal lectures.
The exam will be written and will count for 100% of the final grade.
Students are requested to bring to class their own laptop, if available.
There is no specific text book. Class notes and slides will be distributed during the course. For the theory part of the course, a good reference book for the frequentist approach is Introduction to the Theory of Statistics by A. M. Mood, F. A. Graybill, D. C. Boes; Publisher: McGraw-Hill 1974. The book is out of print and can be downloaded from the web. For the Bayesian part of the course the suggested book is The Bayesian Choice by C. Robert.
For the applied part of the course students are referred to the online material available available here https://www.r-project.org
Lecture notes will also be provided.