ACFI/LCBS2302 – Group Assignment
Empirical Econometrics Project Using E-Views
Weight – 55%
Groups – 3 students (Exceptionally 4)
Due: Friday 31st of March 2017 Please mark for the attention of Vitor Leone.
INSTRUCTIONS
1. The assignment must be word-processed and physically submitted to the SAC by 16.00.
2. The whole assignment must be no more than 3,500 words in length including TABLES, GRAPHICS BUT NOT REFERENCES. The word count must be stated at the end of the assignment.
3. Your Word file MUST be submitted to Turnitin via Blackboard.
4. Your Eviews file MUST emailed to the module leader until 16.00 on the deadline day.
5. The assessment criteria detailed overleaf will be used to mark your assignment. Please bear the criteria in mind when preparing your assignment.
6. Group policy work should be followed see appendix I of this document.
7. The assignment is worth 55% of the total module mark.
PURPOSE OF THE ASSIGNMENT:
The purpose of this assignment is to engage students into a practical empirical project where the regression methods taught in the course can be applied to estimate a regression equation for the US House Market. Students are expected to show the knowledge of the techniques that can be employed to determine a parsimonious and sound model which does not suffer of issues such as heteroskedasticty, autocorrelation, multicollinearity to cite a few. Students are required to reflect upon economic theory and to show an ability to use the statistical software E-Views appropriately.
SCENARIO:
The Economic Report of the President (ERP) is a document published by the President of the United States’ Council of Economic Advisers (CEA). Released in February of each year, the report reviews what economic activity was of impact in the previous year, outlines the economic goals for the coming year (based on the President’s economic agenda), and makes numerical projections of how the economy will perform. Criticism usually follows, sometimes attacking the importance placed or not placed on particular data, and also on the importance of particular goals presented in the Overview. One of the sections of the report discusses the state of the US house market and is of great interest to households, investors, financial institutions, housing associations, state agents and policy makers.
As part of an economics practice of a major consultancy company your group was commissioned by an external
client to prepare a 3,500 word report based on the (ERP) section of the US housing market. Your line manager
limited the number of people working in the project to a maximum of three colleagues exceptionally four to
complete this task. Your group has also been informed that key members who commissioned the study won’t have a
sound knowledge of econometrics; therefore it is important that you consider your audience when communicating
your findings. It is important that you clearly define key terms as necessary and use appropriate references.
All of the data that you require is readily available on the US Government Publishing Office website and on
Blackboard.
https://www.gpo.gov/fdsys/browse/collection.action?collectionCode=ERP&browsePath=2016&isCollapsed=false&le
afLevelBrowse=false&isDocumentResults=true&ycord=0
REQUIREMENTS:
You and your team are required to develop a regression model in terms of a list of explanatory variables used to
explain New Housing Starts. The model should be robust and BLUE. Your independent variables are:
US seasonally adjusted civilian unemployment rate (%);
US seasonally adjusted M2 money supply(US$ billions);
New home mortgage yield (%);
Prime Rate charged by banks (%);
Real GDP, US$ billions (2005=base)
1. Determine the question of interest.
2. Discuss the appropriate economic theories relating to this question of interest that may explain the expected
relationship between New Housing Starts and the independent variables.
3. Using the above theory express the economic and econometric model, discuss what the differences are
between these two models.
4. Data collection.
– Define the type of data used, e.g. cross-sectional, time series, pooled cross-sectional, panel data.
– Discuss the various limitations to using economic data of this form.
5. Regression Analysis.
– Define and briefly explain, using references, the six Gauss-Markov Assumptions that should be in place
before running a regression, why are these assumptions important?
– Using E-Views, perform a regression analysis on the ERP data, you will need to determine the values for
the parameters, calculate R2 and the adjusted R2.
– Explain what each of the parameters indicates with reference to ‘ceteris paribus’ and the ‘partial effect’.
– Explain what the values of R2 and the adjusted R2 indicate about our sample of data.
– Discuss what the implications would be if we omitted one of the independent variables with reference to
the estimated parameters.
– Interpret the standard errors and t-values for each of the parameters; explain what each of these terms
mean with reference to your results.
– Interpret the F-statistic of your regression explaining suitability of your model.
– Determine whether the error terms are normally distributed by performing the Jarque-Bera test in EViews.
Interpret your results and discuss the advantages and limitations of the test.
– Using the appropriate test in E-Views determine whether multicollinearity is present within the ERP
data. Interpret the results and discuss the advantages and limitations of the test.
6. Determine whether heteroskedasticity is present in the errors using the appropriate tests. Interpret the
results from each of those tests and discuss their advantages and limitations. What can be done if
heteroskedasticity is present within our ERP data?
7. Define autocorrelation. Determine whether autocorrelation is present within the ERP data by conducting the
graphical test, the Durbin Watson Test and the LM test. Interpret your results and discuss the advantages
and limitations of the test. If autocorrelation is detected correct your model as appropriate and re-estimate
the regression. Interpret your results in terms of the consequences of autocorrelated residuals.
8. Write a 3,500 word report with your findings.
9. An executive summary should be provided which clearly defines the key terms and findings within the report
to allow those that do not have a substantial knowledge of econometrics to be able to understand the
findings.
WRITTEN REPORT:
The written report should be around 3,500 words in length (including tables, plots and pictures but not the reference
list) word-processed and should be of a professional format suitable for the audience. You can also attach all
supportive tables with summaries of the results and the group meeting minutes as appendices in the back of your
report.
ASSESSMENT AND MARKING SCHEME:
The majority of the marks will be given to students that produce evidence of:
A good understanding of the economic theory.
A good understanding of statistical procedures and E-Views.
An ability to link properly the first two points above.
Critical thinking in the interpretation of the findings.
EXECUTIVE SUMMARY:
An executive summary should be no more than 10% of the final word count, so you are allowed 350 words for this
part of the assignment.
GROUP MEETING MINUTES:
The group meeting minutes are expected to have been prepared to a professional standard following the group
procedures rules established by the group members. As a minimum they must include the date, name of attendees,
name of apologies, location, date of next meeting and location, review of previous meeting minutes and a
breakdown of points discussed. Please make it clear in the meeting minutes who is assigned each task.
The grade you achieve for this part will depend entirely on the level of understanding demonstrated in your report
and your sound empirical backing.
In terms of referencing, you are required to use the Harvard system of citation. Also, beware of the academic
regulations regarding plagiarism. As you can see from the marking scheme, marks are awarded for clear
referencing.
HINTS
I strongly recommend that you get a copy of E-Views Illustrated (available on blackboard)as soon as you
have your assignment brief, so as to familiarise yourself with elementary statistics procedures.
ADDITIONAL SUGGESTED READINGS:
Asteriou, D. Hall, S.G.(2016) Applied Econometrics, 3rd Edition. Palgrave.
Brooks, C. (2014) Introductory Econometrics for Finance. 3rd Edition. Cambridge: Cambridge University Press.
Fields, Andy. (2014) Discovering Statistics Using IBM SPSS Statistics. 4th Edition. Sage Publications Ltd.
Gujarati, D. (2009) Basic Econometrics. 5th Edition. Singapore: McGraw-Hill.
Gujarati, D. (2015) Econometrics by Example. 2nd Edition. London: Palgrave.
Maddala, G.S. (2001) Introduction to Econometrics. 3rd Edition. John Wiley & Sons.
Stock, J. and Watson, M. (2015) Introduction to Econometrics. 3rd Edition. Essex: Pearson Education Limited.
Wooldridge, J. (2014) Introductory Econometrics: A Modern Approach. 5th Edition. South Western Cengage Learning.
Student ID Programme Graded by Date
Overall Grade
Class/
Grade
Assessment Criteria
l
0-29%
30-39%
40-49%
50-59%
60-69%
70-89%
90-100%
Demonstrate a
consolidated knowledge of
econometric concepts,
theories and methods
Missing/Poor
knowledge of
econometric
concepts, theories
and methods.
Minimal knowledge of
econometric concepts
or theories and
methods.
Limited/Basic coverage
of knowledge of
econometric concepts,
theories and methods.
Adequate coverage of
knowledge of
econometric
concepts, theories
and methods.
Good coverage of
knowledge of
econometric
concepts, theories
and methods.
Excellent coverage of
consolidated knowledge
of econometric concepts,
theories and methods.
Exceptional coverage of
consolidated knowledge of
econometric concepts,
theories and methods.
Demonstrate competency
in presenting economic
data, economic models and
forecasts using range of
ICT skills, including the use
of econometric computer
software
Insufficient or
inappropriate data
collected. No model
is used. No use of
econometric
computer software.
Complete dataset
collected. No clear
model is used.
Limited/basic use of
econometric
computer software.
Complete dataset
collected. Suitable
models are applied
using econometric
computer software. But
the model has
questionable problems.
Complete dataset
collected. Clear
models are applied
using econometric
computer software.
But there are some
minor errors in the
models.
Complete dataset
collected. Appropriate
models are applied
using econometric
computer software.
Data and models are
not justified.
Complete dataset
collected. Appropriate
models are applied using
econometric computer
software. Data and
models are justified.
Complete dataset
collected. Appropriate
models are applied using
econometric computer
software. Data and models
are justified extensively.
Apply econometric tools
and techniques in order to
build models for forecasting
or to evaluate alternative
policies.
No or completely
wrong application of
econometric tools
and techniques.
Very limited
interpretations are
given.
Insufficient or wrong
application of
econometric tools
and techniques with a
limited interpretation
of results.
Adequate application of
econometric tools and
techniques with basic
interpretation of results.
Adequate application
of econometric tools
and techniques with
good interpretation of
results.
Adequate application
of econometric tools
and techniques with
full interpretation of
results.
Adequate application of
econometric tools and
techniques with full
interpretation of results.
Understood implications
of findings.
Adequate application of
econometric tools and
techniques with full
interpretation of results.
Understood implications of
findings and logical
suggestions for further
analysis.
Appropriate conclusions
and recommendations
drawn from whole analysis.
Conclusions and
recommendations
missing or minimal.
Conclusions and
recommendations
weak or inconsistent
with results.
Conclusions generally
given but unsupported
and/or lacking in insight.
Conclusions clearly
expressed but lacking
in insight and/or
appropriate
recommendations.
Holistic overview of
findings and
conclusions drawn.
Links between
different elements of
analysis identified.
Some appropriate
recommendations for
further analysis.
Holistic overview of
findings. Insights provided
into information revealed
by analysis. Appropriate
recommendations and
reflections on the analysis
undertaken.
Holistic overview of
findings. Insights provided
into information revealed
by analysis. Reflections on
the analysis undertaken.
Creative suggestions for
further analysis.
Assessment criteria
0-29%
30-39%
40-49%
50-59%
60-69%
70-89%
90-100%
Report structure: use of
sections, appendices,
numbering & labelling.
Poor structure, no
clear sections, poor or
absent labelling and
little or no use of
appendices.
Some coherent
sections but lacking
in use of
appropriate
numbering, labelling
and appendices.
Reasonable structure
but some errors or
weaknesses in
numbering, labelling
and use of appendices.
Appropriate structure
but could improve on
either labelling,
numbering or use of
appendices.
Clear and appropriate
structure. Clear
labelling and
numbering.
Appropriate use of
appendices.
Excellent presentation
and structure. Attention to
detail with numbering,
labelling, appendices &
layout etc.
Incorporated imaginative
aspects of structure or
presentation that enhance
the impact of the report on
the reader.
Coherent account of
analysis. Use of English,
spelling & grammar.
Referencing.
Illogical account.
English is poor,
incomprehensible at
times. References
largely absent.
Overuse of first
person and/or
significant spelling
or grammatical
errors. Limited or
inconsistent
referencing.
Overuse of first person
and/or some spelling or
grammatical errors.
Limited referencing
and/or not Harvard
style.
Readable account
with some flaws.
English, spelling and
grammar acceptable.
Reasonable attempt
at referencing.
Clear account using
passive aspect.
Generally good
spelling and grammar.
Good use of Harvard
referencing.
Clear and concise
account using passive
aspect throughout. No
spelling or grammatical
errors detected.
Impeccable referencing.
Written account of a
publishable standard.
Final grade determined by how well the criteria have been met overall and not the sum of the individual aspects of the work:
Additional Comments (Optional):
Appendix I
Group Work Procedures
Working in groups (or teams) is complex and often challenging but is recognised as an essential skill required at work
in any small, medium or large organisation. The purpose of setting group work in your course is to help you identify
and develop the professional behaviours that promote effective group work. Prioritising the learning from group
work now will help you substantially in your future career.
The expectation is that all group members contribute equally to the group work task and that you each support each
other in this endeavour. To promote this expectation all groups will be expected to:
1. Complete a short collaborative working agreement identifying the groups mutually agreed ways of working (a
template will be provided)
2. Keep a record of attendance at all meetings and notes of actions allocated to individuals (a template will be
provided)
3. Submit the above documents as appendices to your report.
Where it is evident that all group members have committed and contributed equally to the group task, then the
grade for individual group members will be the grade awarded to the group task output.
Where a student feels that there has been unequal commitment and contribution by different group members and
this has impacted on the quality of the final output they should email me and request a Peer Assessment. This must
be done at least 3 days before the deadline for submission of the assessment. The tutor will then email the group
and request that all students individually complete the Peer Assessment Form. Confidentially each group member
should email the form back to the tutor. These forms are not revealed to other group members.
Where group members have been asked to undertake Peer Assessment, the tutor will review the evidence
submitted by the group (collaborative working agreement, meeting attendance and notes of action points). Where
there is good evidence of a differential commitment and contribution to the group output then the tutor can adjust
the individual grades. Normally only one or two group members would be expected to have their grades adjusted.
Group Collaborative Working Agreement
Purpose
The purpose of this document is to define the procedures and make clear the expectations as they relate to working
relations and group structure among team members in the above named group.
The members of the team are:
Details of the Agreement
This collaborative working agreement covers the following elements:
1. Communication
The group to insert their agreed approach here.
2. Decision making
The group to insert their agreed approach here.
3. Responsibilities
The group to insert their agreed approach here.
4. Participation
The group to insert their agreed approach here.
5. Leadership
The group to insert their agreed approach here.
6. Grievances
The group to insert their agreed approach here.
Summary
This agreement specifies the working relations agreed by the group and as such provide the best possible working
conditions for completing the project.
Date:
Signed by all group members:
Meeting record
Course:
Module:
Team name / number (if applicable):
Meeting number: Date: Venue:
Present:
Apologies:
Absent:
Agenda
Status of Action Points from [insert date]
Issue Detail Action Timeline Status
Who? When? Due
Pending
Complete
Status of Action Points from [insert date]
Issue Detail Action Timeline Status
00:00-00:10
Review of previous actions
Chair / Team Leader
00:00-00:00 Agenda item Lead person
00:00-00:00 Agenda item Lead person
00:00-00:00 Any other business All
PEER ASSESSMENT FORM
Assessment by Peers of Collaborative Working Contributions
Course:
Module:
Team name / number (if applicable):
In the space below please list the names of your team members and circle the number that best describes each team member’s
contribution to the project, using the scale below. If everyone contributed equally (similar effort and quality), then all should
receive a ‘2’. However, if you feel that a member of your team has under-performed or over-performed, in other words when
you score a team member a ‘1’ or a ‘3’, you should also provide comments to explain why you’ve given that score. Only one
member in a group might be expected to score ‘3’.
3—Highly Engaged. This individual made significant content and process contributions throughout the project, and was
clearly committed to making the whole project a success. The quality of the individual’s work was excellent. Indeed,
without this person’s contribution, the project would have been unlikely to be successful, and the student may have taken
on more than might reasonably have been expected. This student should receive a higher mark than that for the group.
2—Engaged. This individual performed as expected and should receive the group mark.
1— Intermittently Engaged. This individual frequently missed project meetings, often came unprepared, or contributed
substandard work that required correction by fellow team members; he or she exhibited very little effort and made
minimal contribution to the project with a failure to take any kind of responsibility for the task,
0 – No Meaningful Engagement. The student was totally absent from the project, and the end product in no way reflects a
contribution on her or his part. Absence from the majority of meetings may have had a negative effect on the team by
forcing work to be redistributed.
Name Score Comments
1.
2.
3.
4.
5.
6.
Confirmation statement: I confirm that the above grading is based on my honest assessment of each person’s
engagement with the team and contribution to the project. I have put aside any personal disagreements and focused on
team performance only.
Name: Signed: Date:
Instructions: Having completed your peer assessment, please email your form to your tutor.
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