ECMT 674: Economic Forecasting
Final Research Paper; Due Date: April 29, 2019 (end of the day, i.e. 11:59pm)
This is a team assignment that could be conducted in teams of at most two. Please mark every person’s
name on the document that is being returned. I would also like to get the signatures of every team member
under a statement: “I have contributed to this assignment to a sufficient degree to get equal credit with
my teammates, and all other collaborations are properly acknowledged.” Please submit the files through
eCampus. Everyone in the team will receive the same grade.
You will need R/RStudio to complete this assignment. You could also use Excel to do the data work for
the assignment. I would like you to turn in one file containing the content of the report as well as figures and
tables. Throughout the semester you have used R Markdown to generate the reports, and you should do the
same in this assignment as well. Please name your files in an informative manner: an example would be the
course name/number + the first initials of the team members + the assignment number + a file identifier.
In this assignment you will be looking at GDP growth forecasts and recession probabilities using the
information in the US yield curve. Some useful references for the project are below. The list is not limited
to the references below, there are many papers about this topic. Some of the references within these links
could be useful as well (some of the articles below are available for reading only if you are connected to the
university network):
the Cleveland Fed at
https://www.clevelandfed.org/our-research/indicators-and-data/yield-curve-and-gdpgrowth.aspx
the San Francisco Fed at
https://www.frbsf.org/economic-research/publications/economic-letter/2018/august/inf
ormation-in-yield-curve-about-future-recessions/
the Journal of Finance at
http://www.jstor.org/stable/pdf/2328836.pdf?refreqid=excelsior:5a689814d6f94399d93a
c94641339695
ECMT 674作业代做、代写Economic Forecasting作业、代写R/RStudio课程作业、代做R程序作业
the Journal of Applied Econometrics at
https://onlinelibrary.wiley.com/doi/full/10.1002/jae.2485
There are also various news media articles on the flattening of the yield curve and its implications for a
recession in the US. Consider recent articles in Bloomberg and Forbes: (i) https://www.bloomberg.com/ne
ws/articles/2018-04-09/yield-curve-entering-danger-zone-as-inversion-reappears-on-radar;
(ii) https://www.forbes.com/sites/simonmoore/2019/03/23/the-yield-curve-just-inverted-put
ting-the-chance-of-a-recession-at-30/#312c122413ab, among others.
The overall objective of this project is to evaluate how well the slope of the yield curve predicts changes
in real output growth, and consequently, how well it forecasts recessions. The literature has taken different
approaches to it: (i) you can forecast the GDP growth and its forecast distribution, figure out the probability
associated with negative GDP growth. Ideally, you would like to figure out the probability associated with
two consecutive quarter negative GDP growth. (ii) You can model the probabilities directly, by defining the
recessions consistent with the definition used by the National Bureau of Economic Research (NBER, for a
reference see https://www.nber.org/cycles/main.html). The idea here is that the recession probabilities
are directly predicted by the spread. Either approach would be acceptable.
1
Your analysis should touch on model selection, proper transformation of the variables, include some discussion
on how the proper transformation is selected. It should include an out-of-sample forecast evaluation
exercise. You can experiment with various spreads as well as various estimation schemes, i.e. fixed, rolling,
recursive forecasting. Given the references for the assignment and what you have learned from the current
project (and perhaps homework 4), you should provide your insights on how good the yield curve is for
predicting recessions.
The paper should not be more than 15 pages long and should use reasonable font size, spacing and
margins. The text should be at most 5 pages, the rest can be allocated to figures and tables.
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