The purpose of the project is for you to demonstrate that you can code everything correctly in R and that you can correctly interpret the results from the estimations of the different approaches and models.
statistics
Description
Project
The purpose of the project is for you to demonstrate that you can code everything correctly in R and that
you can correctly interpret the results from the estimations of the different approaches and models. It is
not enough to run the code correctly. You also need to give your interpretation of the results. For example:
if you find that two series are cointegrated, what does that mean? Should we expect these series to be
cointegrated in the first place? Why? Should we expect A to cause B, or for B to cause A? Why? When you
run your ARDL model, what interpretation do you offer for the coefficient estimates? Why would you
prefer a VEC model over any other model? When you run your panel data model with fixed effects, how
do you interpret the regression estimates?
Instructions
Complete part 1 of this project. Part 2 is optional but I encourage you to complete it to train your panel data and coding skills, which are valuable skills in the job market and they look good on your CV. There is no grade penalty if you only complete part 1.
Write the R code for all steps. Submit your project together with your R code as a single PDF file on the Moodle VLE. The R code embedded within the project file does not count toward the 2,000-word limit. A template in Word format is available on the Moodle page.
The easiest way to format your project is to write it in MS Word, combining the R code and the R output step-by-step for each task, pasting both the output text and the plots. Then save the Word file as a PDF and submit it on the Moodle VLE. The submission is anonymous but please include your student ID# on the cover page.
If you are the type of student who loves a new challenge, you can write your entire project in R using
Rmarkdown, which is a free open source package for R. Rmarkdown is fully integrated into Rstudio, even
though you can run Rmarkdown without Rstudio. More information on Rmarkdown is available on our
Moodle page. Rmarkdown allows you to automate many features in your own project. On our Moodle
page you will also find a template Rmd file which contains the R code to render your project in PDF, HTML,
and Word formats using Rmarkdown. I suggest you render your project in PDF format as in my template.
