6. Suppose you have the following regression results, where the Model is given t

Question

6.         Suppose you have the following regression results, where the Model is given to be:

Interpret the following from the respective tables below and provide the level of significance used and the null and alternative hypotheses for the appropriate texts (from EViews).

6.1       Marginal Significance Level for each coefficient, including the intercept

6.2       Adjusted- R-square

6.3       Test for Multicollinearity

6.4       Correlation Matrix of Independent Variables (Discuss possibilities)

6.5       Durbin-Watson Test

6.6       Serial Correlation Test

6.7       White’s Heteroskedasticity Test

6.8       Brief summary of results.

Dependent Variable: LOG(SALES)

Method: Least Squares

Sample: 1986Q1 1992Q4

Included observations: 28

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

-7.694778

4.521676

-1.701754

0.1017

LOG(CONFI)

0.794025

0.379527

2.092142

0.0472

LOG(PURABI)

1.704483

0.617981

2.758149

0.0109

D4

0.457947

0.069696

6.570595

0.0000

R-squared

0.658786

    Mean dependent var

4.558091

Adjusted R-squared

0.616135

    S.D. dependent var

0.243529

S.E. of regression

0.150883

    Akaike info criterion

-0.813057

Sum squared resid

0.546378

    Schwarz criterion

-0.622742

Log likelihood

15.38280

    Hannan-Quinn criter.

-0.754876

F-statistic

15.44571

    Durbin-Watson stat

1.483548

Prob(F-statistic)

0.000008

6.6 Breusch-Godfrey Serial Correlation LM Test:

F-statistic

7.394817

    Prob. F(2,22)

0.0035

Obs*R-squared

11.25615

    Prob. Chi-Square(2)

0.0036

6.7 Heteroskedasticity Test: White

F-statistic

1.753318

    Prob. F(7,20)

0.1533

Obs*R-squared

10.64816

    Prob. Chi-Square(7)

0.1547

Scaled explained SS

9.124387

    Prob. Chi-Square(7)

0.2438

Dependent Variable: LOG(SALES)

Method: Least Squares

Sample: 1986Q1 1992Q4

Included observations: 28

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

C

-7.694778

4.521676

-1.701754

0.1017

LOG(CONFI)

0.794025

0.379527

2.092142

0.0472

LOG(PURABI)

1.704483

0.617981

2.758149

0.0109

D4

0.457947

0.069696

6.570595

0.0000

R-squared

0.658786

    Mean dependent var

4.558091

Adjusted R-squared

0.616135

    S.D. dependent var

0.243529

S.E. of regression

0.150883

    Akaike info criterion

-0.813057

Sum squared resid

0.546378

    Schwarz criterion

-0.622742

Log likelihood

15.38280

    Hannan-Quinn criter.

-0.754876

F-statistic

15.44571

    Durbin-Watson stat

1.483548

Prob(F-statistic)

0.000008

Explanation / Answer

The level of significance used is 0.05

.

The hypothesis are as follows:

Null Hypothesis: Sales of fashion good doesnot depend on consumer confidence index and real disposable income

Alternate Hypothesis: Sales of fashion goods depends on consumer confidence index or real disposable income or both .

.

Answer to 6.1)

Marginal siginificance level for each coefficient is :

constant : 0.1017

Confidence : 0.0472

Income: 0.0109

D4 : 0.000

.

Answer to 6.2)

Adjusted R square is 0.616135

This implies that this model is able to explain 61.6135% of the variation in sales of fashion goods

.

Answer to 6.3)

Since the value of standard errors is small this means the effect of multicolinearity is less

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