The course is an advancecd study of econometrics. The course is about time series using the software R. The theoretical goal is to learn what econometric methods to use and their limitations. The applied goal is to code the econometric methods with the software R. Many examples with real time series data are presented to illustrate the econometric methods and their use with the software R.
Students must be familiar with theoretical statistics and probability taught in Statistics I and II.
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 1 and Chapter 2.
Students are required to bring their personal laptop to each lecture. They will mostly be autonomous by following and implementing the methods from the textbook. Each lecture will include around 30 minutes of explanation of the crucial points, and an hour of autonomous work by the students. They are highly encouraged to ask questions about anything even if it feels stupid.
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 and read the appendices,
you can start reading the book from its introduction and Chapter 1 and do Homework 1.
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. 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 evaluation will be based on the final take-home exam. The report must be typed in Word, or any other equivalent writing language, and then printed out, and turned in class.
The final take-home exam is available here. The dataset is available here : stockmarket.dat