Analytical Methods in Economics and Finance

In this assignment, Examine the determinants of ‘life satisfaction’ using a dataset from the Australian Centre on Quality of Life (ACQOL) database. This data is provided in the file MAE356_t2_2013_Assign.XLSX.
The data in this file comes from a survey conducted in 2007 and contains 1,709 individual observations on various variables. The definition and details of these variables are as follows and the actual survey questionnaire is attached at the end of this file.
LIFESAT = Life Satisfaction score from 0-10 (you can think of this as how happy a person is).
HRSWORK = The number of hours a person does paid work per week (0 = no work).
GENDER = Takes a value of 0 if male and 1 if female.
AGE = The age of the person measured in years.
AGESQ = The age of a person squared.
MELB = Takes a value of 1 if the person lives in Victoria and 0 otherwise.
INCOME = Represents the person’s annual household income (see the table at the end of this file)
Notes: This is an individual assignment. All Excel worksheets excluding the original data should be attached at the end of your submitted hard copy assignment. These should clearly demonstrate the work undertaken independently by each student. Submissions should be made both in hardcopy (report only) and online on D2L (report and excel files). On-campus students must submit to the campus specific faculty office while the off campus students should mail the assignments to the Division of Student Affairs (DSA) or contact DSA for submissions (visit http://www.deakin.edu.au/current-students/study-information/exams-assessment/assignments.php).
Part A: Descriptive Statistics
Using Excel, compute the descriptive statistics for the variable “LIFESAT” for the following below categories:
i) Males vs Females
ii) Those in income category 1 vs. those in income category 6.
Briefly discuss any important differences between the results in each group. Do the differences accord with your a priori expectations? Explain.
[2 + 2 = 4 Marks] Part B: Simple Linear Regression Analysis and Hypothesis Testing
i) Run a regression on life satisfaction vs gender and do any hypothesis test you feel appropriate in order to see if men are happier than women or is it women who are happier than men (or perhaps gender doesn’t affect happiness). Show your regressions outputs, steps and calculations and make sure you interpret your regression results.
ii) The English proverb: “Money can’t buy happiness” is quite often cited but do you actually believe this is the case? Given the data provided and given the fact that you can proxy Life Satisfaction for happiness, carry out any regressions/hypothesis test you feel necessary and see if money can/can’t buy happiness. Show your regressions outputs, steps and calculations and make sure you interpret your regression results.
iii) Melbourne was voted the most liveable city in the world by the Global Liveability Survey. Using the data provided, perform regression analysis and hypothesis testing. Test to see if this is the case by comparing Melbourne to other cities in Australia. Show your regressions outputs, steps and calculations.
[4 + 4 + 4 = 12 Marks] Part C: Multiple Regression Analysis
Estimate the following multiple regressions model and present your estimated sample regression function along with interpretation of the coefficients.
[1 + 3 = 4 Marks] Part D: Multiple Regression Analysis
The following hypothesis tests and discussion refer to the regression model in Part (C)
i) For a hypothetical male who is aged 23, lives in Melbourne and makes $45,000 per year working 50 hours per week, estimate their life satisfaction score based on your results from Part (C).
ii) Suppose now the same person 10 years later is now making $150,000 per year, still lives in Melbourne and is working 30 hours per week, what would you estimate their life satisfaction score to be?
[2 Marks] Part E: Regression – Hypothesis Testing
The following hypothesis tests and discussion refer to the regression model in Part (C)
i) Conduct a hypothesis test to see if AGESQ is a significant variable. What can you conclude about the nonlinear relationship between age and life satisfaction? Show your regressions outputs, steps and calculations.
ii) Conduct a hypothesis test to see if AGE and AGESQ are jointly significant. What can you conclude about age and its impact on life satisfaction? Show your regressions outputs, steps and calculations.
iii) Conduct a hypothesis test to test the overall significance of the model. Based on these results along with the R-squared, comment on how ‘good’ the model is. Show your regressions outputs, steps and calculations.
[3 + 3 + 4 = 10 Marks] Data Dictionary
Variable
Survey Question Asked
Value labels
LIFESAT
Thinking about your own life and personal circumstances, how satisfied are you with your life as a whole?
Scored:0-10
0 = completely dissatisfied
10 = completely satisfied
HRSWORK
How many hours each week do you normally spend on paid work?
0 = does not do paid work.
GENDER
Gender of participant
0 = male
1 = female
AGE
Age of participant measured in years
AGESQ
Age of participant Squared
MELB
Do you live in MELBOURNE?
0 = lives in another city in Australia.
1 = lives in Melbourne
INCOME
Can you please give me an idea of your household’s total annual income before tax?
0 = <$15,000
1 = $15,000-$30,000
2 = $31,000-$60,000
3 = $61,000-$100,000
4 = $100,000-$150,000
5 = $151,000-$250,000
6 = $251,000-$500,000
7 = >$500,000

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