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BEEM012 – Empirical Assignment Brief

创建日期：2024-05-21 17:02:31

所属分类：计算机computer science

标签： BEEM012

Empirical Assignment Brief

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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 difference between two outcomes. For example,
we considered the term spread, the difference 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 difference between
profits in two different sectors as your Yt or Xt or differences 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 effort. 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.
1
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 Effects) 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 Effects 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 different 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.
Grading Criteria
Your assignment will be assigned a grade as follows:
• (Weight: 50%) Interpretation and Understanding of Econometric
Tools Part of your grade will be based on whether you correctly use and
interpret the tools of Time Series Econometrics that we learned. This means
that you use the appropriate models for the given task, that you interpret
results correctly, using the proper critical values for inference as well as
interpreting null hypotheses correctly. This also depends on whether you
explain why you use different tools, and the problems these are selected to
deal with.
• (Weight: 25%) Programming and R Code Part of your grade will
depend on correctly using R to implement the tasks you are assigned and
whether your R code correctly implements the work that you describe in the
2
write-up of your assignmnet. Marks will be given for R code that is correct,
and with comments to clarify you understand the tools you are using.
• (Weight: 25%) Economic Analysis and Discussion This part of your
grade will depend on the economic analysis of your results and the depth
of your discussion. Marks will be given for the economic content of your
analysis and your interpretation of the economic reasoning of your results.
Assignment Outputs to Submit
• A PDF write-up of the results of your analysis, including graphs and tables.
See the outline of the analysis tasks to complete below for details on exactly
what tables & graphs you need to complete. Include your R code at the
end of the document. You can write this document in Word or you can use
LATEX. There are additional videos on the assignment page about exporting
tables and using LATEX.
Word Count: Maximum 2,500 words, excluding R code, tables and
figures.
1 Analysis Tasks to Complete
1.1 Descriptive Analysis – Week 1 Exercises
Before running regressions, we will first examine our data and use some simple
tools to look at the time series.
1.1.1 Data Description
First, write a very brief (just a few sentences) description of the outcome variable
you are interested in analysing. Next write a brief description of your primary
explanatory variable, and the rough research question.
1.1.2 Time Series Plots
Next, plot your Yt time series., and give a few sentences of description. Do there
appear to be significant outliers in this time series? Either exclude them if they
are near the beginning or end of your time series, and if not, make note of this
outlier and make sure to take it into account in your analysis.
3
1.2 Autoregression Analysis of a Time Series
1.2.1 Estimate an Autoregression Model
• First, run an AR(1) regression of your outcome variable. Then use the Bayes
Information Criterion to select the appropriate lag length for your model,
setting a maximum of four lags. Write down the four values of the BIC(p)
you calculate, and explain which model length you end up selecting. Now,
estimate this model. (See Week 1 & 2 Exercises)

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