Oil Prices and Investment in Foreign Financial Securities

Question: Discuss about The relationship between oil prices and investment in foreign financial securities. Answer: 1. Best test to be used for the given sample of variables Solution The best test that can be used to test the small sample of variables is regression analysis. This is because regression analysis would give an idea about the relationship between the dependent variable and independent variables (Kleinbaum et al. 2013). In this assignment, the relationship between the dependent variable, Investment outside the country and the independent variables Oil price and budget surplus is to be found. This relationship can be best determined with the help of regression analysis. 2. In this assignment, the dependent variable would be investment outside the country and the independent variable would be Oil price and budget surplus. This is because oil price is an important part of the economy of any country. Budget surplus is also required to have a healthy economy of the country (Draper and Smith 2014). Investment outside the country generally depends on the economic condition of the country, which in turn depends on the Oil price and budget surplus of the country. Thus, investment outside the country is an apt dependent variable and Oil price and budget surplus are the appropriate independent variables. 3. The regression equation that would be used in this analysis is given as Y = a + bx1 + cx2; where Y is the investment outside the country, a is the constant, b is the coefficient of x1; i.e. oil price and c is the coefficient of x2; i.e. budget surplus. On performing regression analysis, it was seen that a = -157092.4871, b = 6462.39397 and c = -1105.12953. Thus, the regression equation that would be used in this sample is given as Y = -157092.4871 + 6462.39397x1 -1105.12953x2. 4. The methodology that would be used for applying regression model is given as follows: Firstly, the independent and the dependent variables would be identified from the sample. The relationship between the dependent variable and the independent variables would be found using the methods of regression analysis (Chatterjee and Hadi 2015). The data would undergo regression analysis methods to find the coefficients of the regression analysis. The intercept and the two coefficients would be identified and the model would be framed accordingly for the data. 5. The analysis of the regression methods for the given data is given below: SUMMARY OUTPUT Regression Statistics Multiple R 0.982119912 R Square 0.964559521 Adjusted R Square 0.955699402 Standard Error 34592.58637 Observations 11 ANOVA df SS MS F Significance F Regression 2 2.60547E+11 1.3E+11 108.8653 1.57761E-06 Residual 8 9573176254 1.2E+09 Total 10 2.7012E+11 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -157092.4871 29894.50637 -5.25489 0.000769 -226029.3423 -88155.63181 -226029.3423 -88155.63181 Oil Price 6462.39397 464.095905 13.9247 6.85E-07 5392.186895 7532.601045 5392.186895 7532.601045 Budget surplus -1105.12953 279.070013 -3.96004 0.004177 -1748.666133 -461.5929262 -1748.666133 -461.5929262 Table 1: regression analysis of the variables (Source: created by author) It is seen that the value of r squared is 0.964559521. It can be interpreted that there is a strong association between the dependent variable and independent variables. The strength of the relationship is highly positive between the dependent variable and independent variables (Montgomery et al. 2015). Thus, the change in oil price and budget surplus would strongly influence the Investment outside the country in positive direction. 6. On performing the hypothesis test, it was seen that the p value of the test is 1.57761E-06. This value is less than 0.05; i.e. the alpha level of significance. This would lead to rejection of null hypothesis and it can be interpreted that there is correlation between the Investment outside the country and the independent variables, oil price and budget surplus. References Chatterjee, S. and Hadi, A.S., 2015.Regression analysis by example. John Wiley Sons. Draper, N.R. and Smith, H., 2014.Applied regression analysis. John Wiley Sons. Kleinbaum, D.G., Kupper, L.L., Nizam, A. and Rosenberg, E.S., 2013.Applied regression analysis and other multivariable methods. Nelson Education. Montgomery, D.C., Peck, E.A. and Vining, G.G., 2015.Introduction to linear regression analysis. John Wiley Sons.