The main objective of this course is to develop the skills needed to conduct work in the industry or empirical research in fields operating with time series data using the software R. The course aims to provide students with techniques and receipts for estimation and assessment of quality of economic models with time series data.
The final take-home exam will be available here.
here ––>The evaluation will be based on the final take-home exam and the work in class during the semester. The report must be typed in Word, or any other equivalent writing formt, and then printed out, and turned in class.
Students are required to bring their personal laptop to each lecture. They will mostly be autonomous (in class) by following and implementing the methods from Introductory Time Series with R from Paul S.P. Cowpertwait and Andrew V. Metcalfe. The class will cover Section 1-2 of the book. Each lecture will include around 20 minutes of explanation of the crucial points, and then autonomous work by the students. They are highly encouraged to ask questions about anything (even if it feels stupid).
Dataset can be found here: Maine.dat ; USunemp.dat ; cbe.dat ; pounds_nz.dat ; global.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 .
This class is not all about R, yet (some) learning of this programming language is key to understand thoroughly the material of the book.
Students need to be at ease with basic grammar of the language so that they can quickly interpret the output obtained when following the book content.
I do know that some students have NO background at all in programming language, and it requires some kind of effort as the notion used in the book can be sometimes
a little bit tough for a student with no notion on R (or any other programming language. This is a reason why I give more details about R in this section.
Again, the goal of this class is not that you become an EXPERT of R, but rather that you know enough so that you can read through the book content without being
completely lost. Consider your knowledge on R as your lifebuoy, so that you should make a wise investment on how many hours you wanna put it to learn the basics of it.
First, you need to download R (without R, you cannot follow the book). 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. In principle, you can use only R console for this class, but this is not the easiest way.
In particular, each time you will use the console, you need to type again your code, or copy-paste from an annex file. This is rather time-consuming and you will
make more errors doing this, so I require students to also 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
go through Chapter 1 and Chapter 2 of this tutorial.