INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN 85 First, when evaluating the influence of a given descriptive variable on the dependent variable, we would now have to face the problem of discriminating between its effects and the effects of the other descriptive variables. Second, we shall have to tackle the problem of model specification. Often some variables might bethought to influence the behaviour of the dependent variable though, they might be unconnected. We shall have to decide which should be included in the regression equation and which should be omitted. However, the arrangement of flow for the multiple regression analysis is to firstly, carryout derivation of formula, then estimation procedures using values, followed by presentation of results and lastly interpretations. 2.3.6.0 CONCLUSION The features of multiple regression analyses and multicollinearity introduced in this unit are extension of unit 2. Here, we pointed out some of the complications arising from the introduction of several descriptive variables. In the discussions, we explained that when we go beyond the two-variable model and consider multiple regression models we add the assumption that there is no perfect multicollinearity (assumption 10 of CLRM). That is, there are no perfect linear relationships among the descriptive variables when two or more of these variables move together and difficult to determine their separate influences. 2.3.7.0 TUTOR-MARKED ASSIGNMENT 1.) The following earnings functions were fitted separately for males and females standard errors in parentheses Males SEE (2.8420) (0.2434) (0.0600) Females SEE (2.6315) (0.1910) (0.0577) 3.) Explain why the standard errors of the coefficients of S and ASVABC are greater for the male subsample than for the female subsample, and why the difference in the standard errors are relatively large for S.
