Essays on Analytical Methods in Economics and Finance Assignment

Tags: Finance

The paper "Analytical Methods in Economics and Finance" is a perfect example of an assignment on macro and microeconomics. (ii) My expectations for the covariance and correlation matrix were that the figures would all be positive in line with the life satisfaction level but this is not the case as the figures are mixed with others exhibiting a big range from others. This may have resulted from the fact that some variables used don’ t conform to others hence the change of trend. Part B (i) ‘ Money can’ t buy happiness’ is an English proverb that provides a twist to human life.

The question that many people ask themselves is whether having money guarantees someone a happy life. Money alone cannot guarantee someone a happy life but its factor in determining whether one gets to enjoy life. When it comes to material things that one may wish for, money is able to provide but it cannot buy other life-fulfilling aspects such as health, love, and filling acceptable to the society. When we come up with a regression analysis of life satisfaction on the income we find that an individual income is significant to one’ s life satisfaction level.

Regression outputs are: β 1 = 7.3; β 2 = 0.172; e = 0.08885 Steps and calculations: Log on and open excel, click on the Data menu then choose Data analysis From the dialogue box choose regression Input they-range by highlighting the column “ LIFESAT” Input the x-range by highlighting the column “ INCOME” Click on “ labels” then ok. Calculations are found in excel documents. (ii) According to the calculations in excel the regression output of life satisfaction on people living in Melbourne is β 1 = 7.79: β 2 = -0.249 and e = 0.07.

This shows that most people in this town are satisfied with life as compared to people who are not residents of this town. Steps and calculations: Log on and open excel, click on the Data menu then choose Data analysis From the dialogue box choose regression Input they-range by highlighting the column “ LIFESAT” Input the x-range by highlighting the column “ MELB” Click on “ labels” then ok. Calculations are found in excel documents. (iii) Majority of married people are happier with life according to data collected. Individuals who are married recorded a high level of life satisfaction as compared to unmarried individuals. Steps and calculations: Log on and open excel, click on the Data menu then choose Data analysis From the dialogue box choose regression Input they-range by highlighting the column “ LIFESAT” Input the x-range by highlighting the column “ MARRIED” Click on “ labels” then ok. Calculations and regression outputs are found in excel documents. Part C LIFESAT = 7.35 – 0.01MEDCON + 0.002HRSWORK + 0.094GENDER -0.049AGE + 0.0006AGESQ + 0.38ALONE + 0.18INCOME + 0.1 Β 1= y-intercept while β 2, β 3, β 4, β 5, β 6, β 7, β 8 are slopes of respective variables.

e represents the standard error. Part D Participant (X) Age = 25(years) Age squares = 625 Hours worked = 10 hours Medical condition = 2 (duration of illness in years) Gender = 1(female) Relationship status = 0 (has never been married) Income category = 2 (annual income ranges between \$31000 - \$60000) Lives alone = 1 (lives by themselves) Substituting into the equation below: LIFESAT = 7.35 – 0.01MEDCON + 0.002HRSWORK + 0.094GENDER -0.049AGE + 0.0006AGESQ + 0.38ALONE + 0.18INCOME + 0.1 LIFESAT = 7.35 -0.01(2) + 0.002(10) + 0.094(1) – 0.049(25) + 0.0006 (625) + 0.38(1) + 0.18 (2) + 0.1 LIFESAT = 8.679 – 1.245 LIFESAT = 7.434 Part E (i) According to the above hypothesis test AGE and AGESQ are jointly significant.

They both fall along the same path with only a few deviations being pointed out. From the test, age significantly affects the level of life satisfaction; young people feel life to be more satisfying as compared to old people. The majority of people aged between 20 and 30 years scored the highest levels of satisfaction. Steps and calculations: Log on and open excel, click on the Data menu then choose Data analysis From the dialogue box choose regression Input they-range by highlighting the column “ AGE” Input the x-range by highlighting the column “ AGESQ” Click on “ labels” then ok. Calculations and regression outputs are found in excel documents. (ii) AGESQ is a significant variable to this model.

Without AGESQ then age would not have been a significant parameter to this model. The non- linear relationship between age and life satisfaction demonstrates that the higher the age the lower is the satisfaction level i. e. age and life satisfaction are inversely proportional. Steps and calculations: Log on and open excel, click on the Data menu then choose Data analysis From the dialogue box choose regression Input they-range by highlighting the column “ LIFESAT” Input the x-range by highlighting the column “ AGESQ” Click on “ labels” then ok. Calculations and regression outputs are found in excel documents. (iii)From the above hypothesis test the model incorporates the use of significant variables to determine the level of life satisfaction.

The use of R-squared also shows that the model is effective and is able to meet its goal. Steps and calculations: Log on and open excel, click on the Data menu then choose Data analysis From the dialogue box choose regression Input they-range by highlighting column “ LIFESAT” Input the x-range by highlighting all the columns in a contiguous manner. Click on “ labels” then ok. Calculations and regression outputs are found in excel documents.