Students need to take the Statistics course at the Master in Finance since this course builds on knowledge acquired in this preliminary course.
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. We will also cover advanced Monte Carlo simulations techniques such as Markov chain Monte Carlo and Approximate Bayesian Computation.
Learning Method / Style of Lessons
There will be theoretical and applied (with the R statistical software) frontal and online lectures compliant with
Class participation is a mandatory component of the course grade.
There will be a final exam that will comprise 100% of the final grade.
The exam will consist on a project comprising a statistical data analysis performed using the inferential tools introduced in the course. The students have to turn in the R code, a PPT presentation and the final report where the data are described together with the research questions, the analysis, the conclusions and possible further research directions. The project will be presented and discussed with possible questions ranging on the topics covered in class.
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 here https://www.r-project.org
Lecture notes will also be provided.