BEEM012 – Empirical Assignment Brief
Assignment Overview
The goal of this assignment is to use the tools you have learned so far in your R assignments and apply them to an independent project on time series data of your choice. I will be providing a few sample datasets that are easy for you to use from which you can choose which one relates to a research question you find interesting.
• You cannot use exactly the same data as I use as an example in tutorials! I will primarily be using UK quarterly GDP growth as my Yt variable and UK quarterly unemployment as my Xt.
• Remember that you can always subtract one time series from another if you are interested in the di↵erence between two outcomes. For example, we considered the term spread, the di↵erence between long and short run interest rates, in some of our R assignments as a predictor of GDP growth.
You can also use this as an outcome, and look at the di↵erence between profits in two di↵erent sectors as your Yt or Xt or di↵erences in outcomes for men and women as your Yt or Xt, etc.
• You are welcome to seek out your own data and explore an independent research project if you wish to go above and beyond the assignment. You will, however, need to complete the same analysis tasks listed in the assignment.
The grading scheme will be consistent for those using data I provide and for those who find their own.
• If you want to use this empirical work as the basis for your dissertation that would be an excellent use of your e↵ort. You should be aware, however, that you cannot submit the exact same report for your dissertation as you submit for this module, and your dissertation would need to contain substantively additional content.
The first task is choosing an outcome variable that will be your Yt for your analysis, and a primary Xt that will be the main explanatory variable you explore.
Once you have chosen some data of interest, the first part of this assignment will involve using the tools we learned in the first part of the module (up to our
work with Dynamic Causal E↵ects) in order to explore what we can learn about your outcome Yt as an Autoregressive process. You will complete the analytical tasks outlined below by adapting the code provided in R tutorials and write up an explanation of the task and the results. You will also use the tools of Volatility Analysis we will cover later in the course to test whether the volatility or variance of a time series is serially correlated.
The next step is to consider an additional explanatory variable, and estimate the Dynamic Causal E↵ects of this explanatory variable on your outcome of inter
est. You will complete the analytical tasks outlined below by adapting the code provided in R tutorials and write up an explanation of the task and the results.
Where we have learned a manual tool to complete a task, you should use this in your assignment. You are, however, free to use the automatic tools
to check your work.
You will then test two variables for Cointegration, in a formal test of whether they move together and receive the same shocks. This can be the same as the
variables you have used previously, but you can also choose di↵erent variables.
Finally, you will estimate a model testing for Volatility Clustering in your time series Yt using the Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models.
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