The seminar is about time series. The emphasize of this seminar is on financial data. The student will learn independently about basic stochastic models, stationary models, nonstationary models, and their use with the software R. Each student should choose a project in the second semester of the second year, make a report and a final presentation to the class. The project and presentation should include model, numerical study and data implementation. Projects outside of the field of financial econometrics are also possible.
Students must be familiar with theoretical statistics and probability taught in Statistics I and II, and also familiar with the software R.
The course follows Introductory Time Series with R from Paul S.P. Cowpertwait and Andrew V. Metcalfe book content. The book can be downloaded for free from Keio University. We aim to cover Chapter 4 in the first semester of the first year, Chapter 6 in the second semester of the first year, and Chapter 7 in the first semester of the second year. The study of the book will be done independently by the student.
This course is not all about R, but some learning of this programming language is necessary to understand the book.
Students need to be confident with the language R, so that they can interpret the results obtained when following the book content.
Some students have no background at all in any programming language, and it will require more effort from them. The goal of this course is not that you become an expert of R, but rather that you know enough.
Before starting to work on Homework 1, you need to download R (without R, you cannot follow the book and do the homework). You can actually download R
here. Now that you have installed it, you can start to launch it, and you will see the R console.
Try to type 2+3, and then press "enter", and you should see 5 as a result. Students have to use the friendly interface user for R called Rstudio. RStudio is available
here. Once you have installed RStudio, you can launch it. From now on, you do not need to use the
R console, so you do not need to launch R application directly again (except for the part 1.2 of the tutorial on R that follows). Now it is time that you work
scrupulously going through Chapter 1 and Chapter 2 of this tutorial.
Once you have completed the tutorial, you can start reading the book from Chapter 4.
Dataset can be found here: Maine.dat ; USunemp.dat ; cbe.dat ; pounds_nz.dat ; global.dat ; Herald.dat ; wave.dat ; Fontdsdt.dat ; ApprovActiv.dat ; motororg.dat ; wine.dat ; stockmarket.dat. To import the data on R, you can follow the example on specialresearch.R using the dataset file. You can also use other datasets here .
The final grade will be the average of each semester grade. For each semester, the evaluation will be based on the report (50% of the final grade) and the final presentation (50% of the final grade). The report should be written in a typing sofware such as Word, and should be around 5-10 page long. Its style should be based on a research paper, with different sections and including a bibliography. The presentation will be 15 minute long, including 5 minutes for questions.