Econometrics- Analysis of COVID-19.

Description Analysis of COVID-19.
My allocated continent is Africa and my allocated country is Ghana.
The stata programming has all been completed and screenshots of the do-file is attached alongside graphs- just need to complete the report on the findings.

EC3017 Applied Econometrics – Academic Year 2020/21
Individual Project
Introduction
This project is an individual piece of assessment that requires you to apply your learning of econometrics
analysis to the investigation of the COVID-19 pandemic. The “Our World in Data” website gives you access
to a large set of time series and cross-country data related to the pandemic. The datasets are updated daily
when new data from countries’ statistical agencies become available. A copy of the dataset collected on
10th December 2020 is available on Moodle (Covid-19 Dataset.xlsx). The dataset also contains a description
of all variables. You are asked to use this data to write an economic report based on a statistical
investigation of the data outlined in the sections below.
The Dataset
At the beginning of 2020 the organisation “Our World in Data” started the regular (daily) collection of data
concerning the Covid-19 pandemic across countries. The data is available for public use and the dataset
“Covid-19 Dataset.xlsx” (available on Moodle) was downloaded from the “Our World in Data” website on
10th December 2020. On the same site you can also find a detailed codebook with details of each variable in
the dataset and its source (please notice that details about each variable are also contained in a separate
tab in the excel dataset). You are asked to use this dataset in order to complete the tasks below.
The Tasks
The World Health Organisation has asked you to carry out an analysis of the determinants of COVID-19
cases across a given continent and a given country. On Moodle you will find details of the continent and the
country that has been allocated to you. You are required to write a 1,800-word economic report in which
you report on your findings. Your report is expected to be organised to include the following analysis.
1. For your allocated continent construct a panel dataset that, for each country, includes one observation
per month, starting from March 2020, taken at the end of each month (i.e. the 30th of each month).
Once you have set up your dataset you are asked to complete the two following tasks:
a. With the help of graphs and statistics provide a brief analysis of the evolution of COVID-19 cases
and deaths across the continent. (approx. 200 words; 10 marks)
b. Carry out an econometrics investigation of the determinants of new COVID-19 cases across the
continent. Select the variables you plan to use in your econometric model, justify your selection,
produce some summary statistics for each variable and carry out your analysis.
(approx. 500 words, 20 marks)
2. A “reproduction rate” (R) greater than one is regarded as the threshold beyond which the pandemic
becomes ‘explosive’ with sharp increases in cases. The WHO is interested in investigating what factors
are affecting the likelihood of the “reproduction rate” being greater than one. By using the
observations from the countries in your continent on 25th November 2020, explain how you would go
about evaluating such a likelihood and carefully present the findings of your analysis. In presenting your
findings briefly reflect on whether you believe that modelling R in this way is appropriate and whether
you would consider alternative modelling strategies.
(approx. 400 words; 30 marks)
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3. The WHO is also interested in supplying each individual member country with a detailed analysis of the
pandemic in the country. You have been asked to provide an analysis for the country allocated to you
over the period 1st April 2020 to 9th December 2020 and your analysis should contain:
a. A brief summary of the evolution of the pandemic over time in your allocated country. The analysis
should be supported by appropriate graphs and summary statistics. (approx. 200 words; 10 marks)
b. A regression analysis aimed at estimating the determinants of new COVID-19 cases in the country
over time. (approx. 500 words; 30 marks)
Project Guidelines
The aim of this project is to test your understanding of and ability to apply the statistical concepts and
methodologies discussed throughout the module as well as your ability to analyse and evaluate the
outcome of your analysis. The project is deliberately ‘open ended’ or, in other words, not very prescriptive
in what and how you should conduct your analysis. You should refer to the material covered in the module
and the activities carried out during the term to decide how to answer the questions and shape your
investigation. To help your thinking, you can find the following guidelines of some help.
Task 1
In constructing your dataset and in commenting on the data try to think about questions such as: what type
of data do you have in the original and in your adjusted dataset? How many variables do you have? What
are the types of variables you have? How many countries and observations do you have? Are there
variables containing missing observations? How do you handle the missing information? Overall, how
would you regard the quality of your data? In investigating the COVID-19 cases and deaths across your
continent can you see any pattern or trend?
In addressing the regression analysis make sure to explain how you construct your econometric model by
specifying its functional form and its estimated outcome. Make sure to interpret the estimated model, the
significance of each individual estimation and the overall goodness of fit of the regression. Produce a clear
account of your findings in such a way that WHO officials, who are not necessarily economist and/or
statisticians, can understand the meaning of your analysis.
Task 2
The “reproduction rate” is also commonly referred to as the R number. An R number greater than 1 leads
to an explosive behaviour in the reproduction of new cases. The dataset contains estimates of the R
number for all countries over time. You are asked to carry out an investigation on the ‘likelihood’ that the
“reproduction rate” is greater than one. In other words, what factors are likely to influence the probability
that the R number will be greater than one? This should be the focus of your analysis: identify those factors
that are most likely to make the R number greater than one. Please notice that for this task you are asked
to use a cross-section of your database i.e. one observation for each country in your continent at the
specified date (25th November 2020).
Task 3
In addressing this question reflect on what type of data and analysis you are asked to carry out. How does it
differ from the analysis you carried out in the previous two parts? Make sure to provide a brief but
informative analysis of the COVID-19 cases and deaths for the country assigned to you. You should set up
your econometrics model and estimate it. Are you, perhaps, considering more than one model because of
data availability? As in the previous two parts make sure to comment on your findings both in terms of the
estimated coefficient and the goodness of fit. Can you reassure the reader that your estimates are unbiased
and efficient? Can you use your model for some forecasting of future COVID-19 cases? Make sure to check
that your estimation is providing you with accurate and valid estimates.
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Report Style
The project gives you an indication of the number of words for each task. However, within the word limit of
1,800 words, you should feel free to arrange the number of words for each task in a way that best fits your
approach. You should also feel free to organise the report in whatever way you think is most appropriate.
You can divide the report into three parts (one for each task) but you should also consider writing one
single report that starts with a short introduction, continues with the main body that contains the analysis
of the three tasks and then concludes with a brief summary. Make sure that you carefully comment on all
the evidence emerging from your statistical analysis. Always justify your choices. Make sure that your
writing style is clear and accurate. Make sure that all tables and figures are labelled and numbered. If you
are using some external sources of information, make sure to use appropriate citation and referencing
rules. Also, please make sure to number your pages.
Missing Observations and Time Periods
The dataset is updated every day but not all countries are able to regularly report data for all observations.
This means that your analysis is likely to be affected by missing observations that will reduce the power of
your analysis. Of course, there is not much that can be done about this problem. However, it will be fine for
you to slightly change the dates given in the three tasks if you think that by changing the dates you will
have fewer missing observations and, hence, a better analysis. If you decide to change some of the time
periods please just mentions it in your report.
Managing the Dataset
The dataset is available in Excel format. While the regression and statistical analysis should be conducted in
STATA, it will be fine for you to prepare the dataset, plot graphs and produce basic statistics in Excel if you
wish to do so.
Submission details
The individual project accounts for 60% of the module’s overall mark. The project should be submitted in
word or pdf format on Moodle by the deadline of Thursday 7th January 2021 at 16.00 (London time).
Alongside the project you should also submit: a) the do file containing all your commands and b) the data
file you used for your analysis.
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Marking Criteria
Assessment criteria Fail (0 – 39) Pass (40 – 49) Satisfactory (50 – 59) Good (60 – 69) Excellent (70 – 100)
Knowledge and
understanding of theory
and techniques
Limited, confused and
not accurate
understanding of theory
and statistical techniques
learned in the module;
no clear ability of
applying such techniques
Evidence of some but
rather basic
understanding and ability
of application of the
methodologies learned
throughout the term
There is evidence of
learning even if not
always as accurate and
detailed as required by
the investigation
Clear evidence of
learning and engagement
with the subject matter
taught during the term.
Shows a firm
understanding and ability
of application
Strong and firm
understanding of the
required statistical
methodologies; clear
evidence of an ability to
apply them in context
Analysis and evaluation
of evidence from data
and statistical
investigation
Discussion is mainly
descriptive; no evidence
of deep understanding;
no ability to properly
analyse evidence
There is a tendency for
descriptive rather than
analytical interpretation
of the outcome of the
statistical investigation
Good interpretation of
the investigation results
with some ability of
analysing the evidence
even if this is not always
as informative and
accurate as it should be
Good analytical skills and,
overall, shows a ‘control’
of the subject matter and
a clear ability to interpret
and, at least partly,
critically evaluate the
evidence
Excellent analytical skills
with clear evidence of an
ability to critically
evaluate the findings
emerging from the
investigation
Quality of report: writing
style, quality of graphs
and tables; no spelling
mistakes; accurate
citation and referencing
Poor writing style and
difficult to understand;
poorly presented graphs,
tables and statistics;
mainly inaccurate citation
and referencing
Broadly a positive report
even if the writing style
should improve; some
spelling mistakes; not
always accurate
referencing and citation
Generally, a well written
report but with some
inaccuracies in citation
and referencing. No
attempt to create a
narrative of the evidence
Overall, a well written
report without spelling
mistakes and good
referencing citations.
There is an attempt to
create a narrative that,
however, is not always
successful
Very well written report
with a nice attempt to
create a narrative of the
evidence from the
investigation. Good
overall referencing and
no spelling mistakes