ECON7350: Applied Econometrics for Macroeconomics
and Finance
Research Report 1
Due date: 3 May 2022, 3:59pm
Instruction
The project consists of two research questions. Please answer both questions as clearly and
completely as possible. Each question is worth 50 marks, for a total of 100 marks. This
report will constitute 35% of your overall grade in this course.
We suggest that you use R for all empirical work involved. However, you should be able to
use another statistical software (e.g. Eviews, Stata, Python, etc.) without a problem. If you
do choose to work with an alternative software, please note that support for software-specific
issues from the course coordinator and tutors may be very limited.
Please upload your report via the “Turnitin” submission link (in the “Assessment /
Research Report 1” folder). Please note that hard copies will not be accepted. At the
moment, the due date is 3:59 PM on 3 May 2022, but please check BlackBoard regularly
for announcements regarding any changes to this. Your report should be a write-up of your
answers (in PDF format, single-spaced, and in 12 font size)1
.
You are allowed to work on this assignment in groups, i.e., you can discuss how to answer
the questions with your classmate(s). However, this is not a group assignment, which means
that you must answer all the questions in your own words and submit your report separately.
The marking system will check for similarities, and UQ’s student integrity and misconduct
policies on plagiarism strictly apply.
Questions
The dataset for this project is contained in report1.csv. The variables are daily time-series
of COVID-19 related measures in the United Kingdom for the period 11 March 2020—07
March 2021. In particular, the dataset contains the following variables:
❼ immobility: index measuring mobility restrictions, where 0 indicates average (unrestricted) mobility and 100 indicates 100% less mobilility than the average;
❼ cases: the natural logarithm of new daily COVID-19 cases;
1Please do not include or attach any software specific material such as R source code and output
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❼ icu: the natural logarithm of total daily COVID-19 patients in the intensive care unit;
❼ deaths: the natural logarithm of new daily COVID-19 deaths.
1. Use the data provided to forecast ICU occupancy for the period 08 March 2021—31
March 2021. In doing so, please consider how such forecasts may be useful for policy,
and consequently, ensure your inference is aligned with the underlying motivation.
Make sure to address all potential sources of uncertainty on a conceptual level, and to
the extent possible, quantitatively. The break down of marks for this question is as
follows (total 50 marks):
❼ motivation and practical purpose (10 marks);
❼ forecast model identification (10 marks);
❼ forecasts computation (10 marks);
❼ interpretation and inference (10 marks);
❼ writing and organisation (10 marks).
2. Use the data provided to obtain inference on the dynamic relationships between mobility restrictions, new cases and ICU occupancy. In particular, investigate the following
questions:
(a) Are there any identifiable equilibrium relationships between immobility and cases,
immobility and icu, cases and icu, or all three together?
(b) If immobility is increased by 1 and maintained at the new level thereafter, what
is the expected effect (all else constant) on cases (i) at the time of impact, (ii) 28
days after impact, (iii) 92 days after impact, (iv) in the long run?
(c) If immobility is increased by 1 and maintained at the new level thereafter, what
is the expected effect (all else constant) on icu (i) at the time of impact, (ii) 28
days after impact, (iii) 92 days after impact, (iv) in the long run?
Again, in answering these questions, please consider how the answers may be useful for policy, and consequently, ensure your inference is aligned with the underlying
motivation. The break down of marks for this question is as follows (total 50 marks):
❼ motivation and practical purpose (10 marks);
❼ model identification (10 marks);
❼ estimation and testing (10 marks);
❼ interpretation and inference (10 marks);
❼ writing and organisation (10 marks).
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