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Microsoft Word - Assignment_Econ6034_Final.docx Econ6034 Econometrics and Business Statistics Individual Assignment (2020-s1) Total Marks 80 – Weighting: 30% The assignment relates to the following...

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Microsoft Word - Assignment_Econ6034_Final.docx


Econ6034
Econometrics and Business Statistics

Individual Assignment (2020-s1)

Total Marks 80 – Weighting: 30%

The assignment relates to the following learning outcomes:
• Apply basic statistical techniques to problems in economics and business
• Use econometric tools to model, estimate and forecast economic data
• Engage into further studies in econometrics
• Demonstrate the ability to work effectively in a group

Submission:
• Submit your assignment via Turnitin by 4pm on Monday 1 June 2020 (week
13 of the semester)
• The submission link will be available on iLearn towards the end of week 12
(Thursday 28th May 2020).
• No extensions will be granted except for cases in which an application for
Special Consideration is made and approved.
• Late submissions will be accepted up to 96 hours after the due date and time.
• There will be a deduction of 10% of the total available marks made from the
total awarded mark for each 24-hour period or part thereof that the
submission is late (for example, 25 hours late in submission incurs a 20%
penalty).
• Include following details in your submission file: Family Name, Given name,
Your student ID number and the Econ6034_Assignment.
• Type your assignment in MSWORD (font size 12) and save it as a pdf and
submit your PDF document. No other format will be accepted.
• Use Microsoft word equation editor to type your formulae or equation. The
Following links may be helpful in getting started with the equation editor.
• https:
www.uwyo.edu/ceas
esources/cu
ent-
students/classes/esig%20help/windows%20help%20files/microsoft%20office
word-equations.pdf
• https:
www.officetooltips.com/word_2016/tips/working_with_microsoft_equ
ation.html
• You CANNOT submit the assignment in Handwritten or scanned formats.
• Your answers must be preceded with the assignment questions and your
answers must be in the same order.
• Unless it requested in the question otherwise do not attach Eviews output.
• If required, charts and output from EViews may be copied as an image into
the document.
• Marks will be deducted for poor quality presentation.
• No appendices in your assignment.
• Maximum pages 12
• You can ONLY submit ONCE to Turnitin.

Plagiarism:
• Each assignment must represent the student's own work.
• The copying of another student's answer or textbooks, or part thereof, is
clearly regarded as plagiarism.
• Students who have plagiarised will be awarded a mark of zero, will not be
permitted to resubmit, and may be reported to the University Disciplinary
Committee for further action.
• Students should be aware of the University’s Academic Integrity Policy and
the Student Code of Conduct: https:
students.mq.edu.au/study/getting-
started/student-conduct

Acknowledgement:
� I agreed and submit my assignment.



Assignment Questions

Question 1 [46 marks]

Suppose we wish to estimate the relationship between food expenditure and some of
its determinants. The file Foodexp.xlsx contains 200 observations from a cross-
section of households for the following variables.

Foodexpi: Weekly expenditure on food (excluding restaurants) in dollars.
Incomei: Weekly household income in dollars.
Childreni: Number of dependent children living in the household.
Retiredi: A binary (0/1) indicator of whether head of the household is retired
{ret.=1}.

(a) Consider a regression model
???????! = ?" + ?# log(??????!) + ?$?ℎ??????! + ?%???????! + ?!
Estimate the model using Eviews and provide the summary results (A
summary results should include fitted equation with coefficients, standard
e
or, t-statistic, p-value, sample size, F-statistic and R-squared). [4 marks]
(b) Interpret the coefficient estimates and their significance at the 5% significance
level. [4 marks]
(c) Does the sign of the coefficients agree with your expectations? Comment.
[4 marks]
(d) Based on the regression output, if Income increases by 10% what is the
estimated change in Foodexp, holding Children and Retired constant. [4 marks]
(e) Test the overall validity of the regression model at the 5% significance level.
State the hypotheses, restricted and unrestricted model, test statistics and its
distribution when null hypothesis is true, critical value and your conclusion.
[4 marks]
(f) Construct 95% confidence interval for ?#, the slope of the log(Income) variable
and interpret your results. [4 marks]
(g) Based on your answer in part (f), without performing a hypothesis test,
would you reject the hypothesis ?": ?$ = 100,                ?#: ?$ ≠ 100. Explain? [4
marks]
(h) Plot the least squares residuals against log(Income) and comment on the
pattern. Is there any evidence of heteroscedasticity? [4 marks]
(i) Test for the existence of heteroscedasticity at the 5% significance level. Use
the Breusch-Pagan-Godfrey test and attach your Eviews results. Clearly states
all steps in your test; null and alternative hypotheses, the auxiliary regression
and the test statistic, critical value, your decision and the conclusion. [6
marks]
(j) Re-estimate your model with robust standard e
ors and attach your Eviews
output. Compare your results with the output in part (a). Comment.
[4 marks]
(k) Now run the following regression model:
???????! = ?" + ?# log(??????) + ?!
(l) Compare your model that with part (a). Which model would you choose?
And Why? [4 marks]
Question 2 [34 marks]

A researcher wants to analyse the relationship between the three-month T-bill rate,
tb3, the annual inflation rate, inf based on the consumer price index, and the federal
udget deficit, def as a percentage of GDP and develops the following model. The
data is stored under the file name is intdef_A.xlsx

??3& = ?" + ?#???& + ?$    ???& + ?&
(a) Use Eviews to obtain a line plot among these three variables (one graph) and
comment on the plot. (Hint: to make the graph in Eviews: Quick à Graph à tb3
inf def àOk àLine & Symbol à OK.) [4 marks]
(b) Obtain sample co
elation coefficient between these variables and comment
on the strength of the relationships? (Hint: to obtain co
elation in Eviews: Quick
à Group Statistics à Co
elations à tb3 inf def à Ok.) [4 marks]
(c) Estimate the above regression model and provide the Eviews output.
[4 marks]
(d) Interpret the coefficients ?#and    ?$. Does the sign of the coefficients agree with
your expectations? Explain. [4 marks]
(e) Provide and interpret the coefficient of determination, ?$. [4 marks]
(f) Plot the residuals of the model and comment on any pattern. [2 marks]
(g) Add a one lag of inf and def to the equation in part (a) and re-estimate your
model. and report the result. Are the coefficients for the two lag variables
individually significant at the 5% level? To add lag variables in Eviews type
inf(-1) def(-1) [4 marks]
(h) Conduct the second order autoco
elation test for the model in part (c) at the
5% significance level. Attach your Eviews results. Clearly states all steps in
your test; Null and alternative hypotheses, the auxiliary regression and the
test statistic, critical value, your decision and the conclusion. [6 marks]
(i) Re-estimate your model with Newey-West standard e
ors and provide your
output. Compare your results with the output in part (c). Comment.
[2 marks]
Answered Same Day May 27, 2021 ECON6034 Macquaire University

Solution

Komalavalli answered on May 31 2021
128 Votes
Question 1:
Model 1:
(a)
???????i = ?0 + ?1log(??????i) + ?2?h??????i + ?3???????i + ?i
Foodexp = 174 + 0.0432*Income + 22.8*Children - 13.8*
P-value of individual coefficients: Variables Income , children and Retired are having P-values less than 0.0001
t statistics: Income= 7.406, children= 4.541, Retried = -13.8065.
Standard e
or: Income = 11.51, children= 0.005, Retried = 14.57
F stasticts : F(3, 196) is 34.34155, P-value(F) is 0.000
R squared value of this model is 0.34, Adjusted R-squared is 0.34
(b)
One percent increase in weekly household income on an average increases the weekly expenditure on food by $0.04 holding retired and children variable constant. Log household income variable is significance at 5% significance level.
An increase in one dependent children living in the household on an average increases the weekly expenditure on food by $22.82 holding other variables constant. Number of dependent children living in the household variable is significance at 5% significance level.
The variable Retired is not statistically significant, so it has no influence on weekly expenditure on food.
(c)
Yes, the sign of the coefficients agree with your expectations .In general there is a positive relationship between weekly expenditure on food and Income, weekly expenditure on food and number of dependent children in the household.
(d)
Estimated change in food expenditure when income increases by 10 %
Foodexp = 174 + 0.0432*Income (10)
y = 174.49
When income increases by 10 % the estimated expenditure on food is $174.49 by holding other variable constant.
(e)
The model is significant at the 5% level of significance. Hypothesis of this model is H0 null hypothesis: β0= β1= β2= β3=0, this means none of the variable has influence on the model. H1 β0≠β1≠β2≠β3≠0, this means all variables have influence on the model. Unrestricted or full model is y = β0+ β1 x1+ β2 x2 + β3 x3 and restricted model is y = 0.If null hypothesis is true we use restricted model and vice versa. Here the critical value of F(3, 196) =2.7 is less than the F value (3,196) = 23.50, so we reject null hypothesis and accept alternate hypothesis H1.Therefore we use unrestricted model for the analysis.
(f)
The 95% confidence interval of log Income is (0.0367794, 0.0608741) indicating the variable is significant and has the influence on weekly food expenditure.
(g)
Above analysis show that the income variable is significant .So we reject the null hypothesis ?0: ?2 = 100 and accept the alternate hypothesis ?1: ?2 ≠ 100, which indicates the variable has influence on the model.
(h)
Residual plot against log(income)
The above plot shows a pattern, indicating the presence of heteroscedasticity which means that there is no evidence for constant variance of residuals.
(i)
Breusch-Pagan test for heteroskedasticity - Null hypothesis: heteroskedasticity not present
Test statistic: LM = 27.6621
with p-value =...
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