Assignment of Individual Econometrics.
Assignment
On the module’s Canvas page you will find a page called ‘Assignment’. This contains the file
Election_data.dta. The file contains information on the characteristics and 2019 General
Election results in 632 constituencies in the United Kingdom. The variables in the file are
id: the constituency identifier
constituencyname: the constituency name
country: the country the constituency is located in
winner19: the political party that won the most votes in 2019 General Election in the
constituency
con19: the Conservative Party’s vote share (in %)
lab19: the Labour Party’s vote share (in %)
ld19: the Liberal Democrat party’s vote share (in %)
snp19: the Scottish National Party’s vote share (in %)
other_parties: the vote share of all other parties (in %)
houseowner: share of the population in the constituency that own a house (in %)
socialhousing: share of the population in the constituency that live in social housing (in %)
econactive: share of the labour force in the constituency that are economically active (in %)
LNunemp: the natural logarithm of the unemployment rate in the constituency (in %)
retired: the retired population share in the constituency (in %)
ltsick: the share of the population that have long-term sickness in the constituency (in %)
unemp16to24: the unemployment rate among 16 to 24 year olds in the constituency (in %)
unemp50to74: the unemployment rate among 50 to 74 year olds in the constituency (in %)
finance: the share of the workforce that work in the finance industry in the constituency (in
%)
minority: the share of the population from an ethnic minority in the constituency (in %)
England: a dummy variable equal to 1 if a constituency is located in England, 0 if in Scotland
or Wales.
Question 1
Open the Election_data file in Stata and estimate the equation
con19i = β0 + β1lab19i + β2houseowneri + β3econactivei + β4LNunempi +
β5retiredi + β6ltsicki + β7financei + β8other_partiesi + β9englandi + εi
, (1)
where the variables are defined as above for each constituency i. Provide a screenshot of
your results and discuss the magnitude and statistical significance of the coefficient
estimates.
[20 marks]
Question 2
Test the hypothesis that the effect of the retired population share on the Conservative Party
votes share differs depending on whether a constituency is located in England.
[10 marks]
Question 3
Discuss what is meant by the expression ‘collinearity’. What effects does collinearity have
on regression analysis? How could you detect it?
[30 marks]
Question 4
Test the hypothesis that the mean Liberal Democrat vote share is 9% using a 5% significance
level. Show your workings.
[10 marks]
Question 5
A key assumption underlying OLS regression is homoscedasticity. Explain what is meant by
‘homoskedasticity’. What are the potential consequences on regression analysis if the
homoscedasticity assumption breaks down? Outline two tests you could use to detect
whether homoscedasticity is present.
[30 marks]
Notes on the task:
There is a 1,000 word limit (no more). Where necessary, provide Stata screenshots
to support your working.
In class we will cover the basics of statistics and linear regression. The lecture notes
and materials will help you in this assignment.
A good assignment will show (1) your understanding of the key concepts, (2) provide
clear intuition behind the tests and the steps involved, and (3)
Module Learning Outcomes:
In this assessment the following learning outcomes will be covered:
LO1. Understanding statistics
LO2. Be able to demonstrate an understanding of linear regression
LO3. Be able to assess regression output
LO4. Demonstrate written skills; independent study skills; researching skills and
organizational skills via the assignment.
Grading Criteria:
Please refer to the marking rubric on the module Canvas page under the learning materials
tab.
Feedback to Students:
Both Summative and Formative feedback is given to encourage students to reflect on their
learning that feed-forward into the following assessment tasks. The preparation for all
assessment tasks will be supported by formative feedback within the tutorials/seminars.
Written feedback is provided as appropriate. Please be aware to use the browser and not the
Canvas App as you may not be able to view all comments.
Plagiarism:
It is your responsibility to ensure that you understand correct referencing practices. You are
expected to use appropriate references and keep carefully detailed notes of all your
information sources, including any material downloaded from the Internet. It is your
responsibility to ensure that you are not vulnerable to any alleged breaches of the assessment
regulations.
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