UNANSWERED Issue #3 In Gujarati, Econometrics By Example number: 5.5: Refer to T
ID: 3385111 • Letter: U
Question
UNANSWERED Issue #3 In Gujarati, Econometrics By Example number:
5.5: Refer to Tbale 5.5. Assume that the error variance is related to the square of income instead of to the square of ABORTIONF. Transform the original abortion rate function replacing ABORTIONF by income and compare your results with those given in Table 5.5. A priori, would you expect a different conclusion about the presence of heteroscedasticity? Why or why not. Show the necessary calculations
Table 5.5 Transformed Eq. (5.1)
Dependent Variable: ABORTION/ABORTIONF
Method: Least Squares
Sample: 1 50
Included observations: 50
Coefficient
Std. Error
t-Statistic
Prob.
1/ABORTIONF
12.81786
11.22852
1.141545
0.2601
RELIGION/ABORTIONF
0.066088
0.068468
0.965239
0.3400
PRICE/ABORTIONF
-0.051468
0.017507
-2.939842
0.0053
LAWS/ABORTIONF
-1.371437
1.819336
-0.753812
0.4552
FUNDS/ABORTIONF
2.726181
3.185173
0.855897
0.3969
EDUC/ABORTIONF
-0.228903
0.147545
-1.551408
0.1283
INCOME/ABORTIONF
0.002220
0.000481
4.616486
0.0000
PICKET/ABORTIONF
-0.082498
0.031247
-2.640211
0.0116
R-squared
0.074143
Mean Dep. Var.
1.011673
Adj. R^2
-0.080166
S.D. Dep. Var.
0.334257
S.E. of Regression
0.347396
Akaike info criterion
0.868945
Sum squared resid.
5.068735
Schwarz criterion
1.174869
Log likelihood
-13.72363
Durbin-Watson Stat
2.074123
Eq. 5.1: ABR i = B1 + B2 Rel i + B3 Price i + B4 Laws i + B5 Funds i + B6 Educ i + B7 Income i + B8 Picket i + ui i = 1,2,…,50
Table 5.5 Transformed Eq. (5.1)
Dependent Variable: ABORTION/ABORTIONF
Method: Least Squares
Sample: 1 50
Included observations: 50
Coefficient
Std. Error
t-Statistic
Prob.
1/ABORTIONF
12.81786
11.22852
1.141545
0.2601
RELIGION/ABORTIONF
0.066088
0.068468
0.965239
0.3400
PRICE/ABORTIONF
-0.051468
0.017507
-2.939842
0.0053
LAWS/ABORTIONF
-1.371437
1.819336
-0.753812
0.4552
FUNDS/ABORTIONF
2.726181
3.185173
0.855897
0.3969
EDUC/ABORTIONF
-0.228903
0.147545
-1.551408
0.1283
INCOME/ABORTIONF
0.002220
0.000481
4.616486
0.0000
PICKET/ABORTIONF
-0.082498
0.031247
-2.640211
0.0116
R-squared
0.074143
Mean Dep. Var.
1.011673
Adj. R^2
-0.080166
S.D. Dep. Var.
0.334257
S.E. of Regression
0.347396
Akaike info criterion
0.868945
Sum squared resid.
5.068735
Schwarz criterion
1.174869
Log likelihood
-13.72363
Durbin-Watson Stat
2.074123
Eq. 5.1: ABR i = B1 + B2 Rel i + B3 Price i + B4 Laws i + B5 Funds i + B6 Educ i + B7 Income i + B8 Picket i + ui i = 1,2,…,50
Explanation / Answer
The multiple regression model after fitting the data and model is given below,
ABR i = 12.81786 + 0.066088 * Rel i -0.051468 * Price i -1.371437 * Laws i + 2.726181 * Funds i - 0.228903 * Educ i + 0.002220 * Income i - 0.082498 * Picket i ; i = 1,2,…,50
among the independent variable, Price, Income and Picket variables have significance at 0.05 level of significane and rest are non significance. R- square value is 0.074143 which is indicate that model is not explaine much better. they only approx 7 % explaine the dependent variable ABR. its means residuals sum of square is near about the total sum of square, thats's why R- square is less and Adj. R^2 is negative. Another way residuals sum of square is more, variance is not constant. if variance is not constant than independent variable may be correlated to each other it is a case of hetroskeductisity . so need to test the hetroskeductisity using White's Test and Breusch-Pagan then we say any thing about the hetroskeductisity is presentsor not.
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