RELEVENT ECONOMETRIC OUTPUTS FROM EVIEWS Assignment

RELEVENT ECONOMETRIC OUTPUTS FROM EVIEWS - Assignment ExampleThus, while the of import coefficients measure the original order concerns, i.e., the slope of the partial functions, the theta coefficients measure the second order impacts or the curvature. The assumeed signs on these coefficients impart depend upon the nature of the relationship that the variable has with sales revenue. If the true relationship that is being estimated is truly nonlinear, thus the beta coefficients themselves would be functions of the corresponding in symbiotic variables. The signs would depend upon the value of the independent variable itself. For instance, a educate in legal injury of mobile phones would lead to a certain rise in revenue if former(a) things, in particular the number of units sold remained unaltered. However, as price rises, the demand for the product would go set down thereby implying a potential f every last(predicate) in the overall sales. The final impact would depend upon t he price elasticity of demand of the product. For lower level of sales the demand would be highly inelastic implying that hike price would still generate increased revenue. But if the demand became elastic, then there would be a definite decline in revenue. Since demand for average mobile phones tend to be relatively inelastic, we should expect to see a positive beta coefficient and a negative theta coefficient. In case of advertising, again the beta coefficient measures the impact of a rise in advertising on total sales while the theta coefficient measures the fringy impact. We should expect that increase in advertising should stimulate additional sales. However, the incremental benefits of more advertising typically are found to be declining. In simpler terms, as there is more and more advertising, the incremental impact on sales declines. Thus, we should expect a positive beta but a negative theta coefficient for all the advertising variables. Table 1 Results of OLS regression, problem 1 Dependent Variable REVENUE Method to the lowest degree Squares Date 09/29/11 Time 1310 Sample 1 60 include observations 60 Coefficient Std. Error t-Statistic Prob. C 359.1101 76.04848 4.722120 0.0000 PRICE 2.880176 1.411429 2.040609 0.0465 PRICE2 -0.011268 0.006384 -1.765162 0.0835 TV 6.383748 3.514018 1.816652 0.0751 TV2 -0.418966 0.359010 -1.167003 0.2486 composition 3.480550 2.251321 1.546003 0.1283 NEWSPAPER2 -0.107221 0.160149 -0.669510 0.5062 RADIO 11.10707 1.184501 9.377007 0.0000 RADIO2 -0.336564 0.053449 -6.296872 0.0000 R-squared 0.876161 Mean dependent var 646.5073 Adjusted R-squared 0.856736 S.D. dependent var 30.92782 S.E. of regression 11.70626 Akaike info criterion 7.895606 Sum squared resid 6988.868 Schwarz criterion 8.209758 Log likelihood -227.8682 Hannan-Quinn criter. 8.018488 F-statistic 45.10326 Durbin-Watson stat 2.333861 Prob(F-statistic) 0.000000 2. We test the joint significances of the variables first in levels (table 2) and then in squares (tab le 3). Table 2 Testing formulate significance of the variables in their levels Wald Test Equation Untitled Test Statistic Value df Probability F-statistic 8.295663 (3, 51) 0.0001 Chi-square 24.88699 3 0.0000 bootless Hypothesis Summary Normalized Restriction (= 0) Value Std. Err. C(2) - C(8) -8.226895 1.877380 C(4) - C(8) -4.723323 3.679021 C(6) - C(8) -7.626522 2.427360 Restrictions are linear in coefficients. The