Whites general heteroscedasticity test, Advanced Statistics

The Null Hypothesis - H0:  γ1 = γ2 = ...  =  0  i.e.  there is no heteroscedasticity in the model

The Alternative Hypothesis - H1:  at least one of the γi's are not equal to zero i.e. the squared residuals are related to one of the independent variables.

Reject H0 if nR2 > 1640_Tests for Heteroscedasticity.png

MTB > let c23 = c7*c7

MTB > let c24 = c8*c8

MTB > let c25 = c9*c9

MTB > let c26 = c10*c10

MTB > let c27 = c7*c8

MTB > let c28 = c7*c9

MTB > let c29 = c7*c10

MTB > let c30 = c8*c9

MTB > let c31 = c8*c10

MTB > let c32 = c9*c10

C7 = totexp

C8 = income

C9 = age

C10 = nk

C23 = sqtotexp

C24 = sqincome

C25 = sqage

C26 = sqnk

C27 = totexpincome

C28 = totexpage

C29 = totexpnk

C30 = incomeage

C31 = incomenk

C32 = agenk

Regression Analysis: sqres versus totexp, income, ...

* sqnk is highly correlated with other X variables

* sqnk has been removed from the equation.

The regression equation is

sqres = 0.0178 - 0.000232 totexp + 0.000023 income + 0.000298 age - 0.00555 nk

        + 0.000001 sqtotexp + 0.000000 sqincome - 0.000005 sqage

        - 0.000000 totexpincome + 0.000003 totexpage + 0.000015 totexpnk

        - 0.000001 incomeage + 0.000035 incomenk - 0.000021 agenk

 

Predictor            Coef     SE Coef      T      P

Constant         0.017804    0.007900   2.25  0.024

totexp        -0.00023207  0.00005370  -4.32  0.000

income         0.00002344  0.00003865   0.61  0.544

age             0.0002978   0.0003511   0.85  0.396

nk              -0.005551    0.003233  -1.72  0.086

sqtotexp       0.00000060  0.00000011   5.65  0.000

sqincome       0.00000004  0.00000002   1.79  0.074

sqage         -0.00000464  0.00000427  -1.09  0.277

totexpincome  -0.00000041  0.00000013  -3.27  0.001

totexpage      0.00000259  0.00000110   2.36  0.018

totexpnk       0.00001477  0.00001740   0.85  0.396

incomeage     -0.00000110  0.00000090  -1.22  0.223

incomenk       0.00003506  0.00001355   2.59  0.010

agenk         -0.00002146  0.00008647  -0.25  0.804

S = 0.0123952   R-Sq = 3.4%   R-Sq(adj) = 2.5%

Analysis of Variance

Source            DF         SS         MS     F      P

Regression        13  0.0080446  0.0006188  4.03  0.000

Residual Error  1505  0.2312304  0.0001536

Total           1518  0.2392750

 

Source        DF     Seq SS

totexp         1  0.0003007

income         1  0.0000070

age            1  0.0000053

nk             1  0.0000429

sqtotexp       1  0.0037616

sqincome       1  0.0000507

sqage          1  0.0001055

totexpincome   1  0.0010903

totexpage      1  0.0005678

totexpnk       1  0.0009260

incomeage      1  0.0001557

incomenk       1  0.0010217

agenk          1  0.0000095

 

MTB > let k4=1519*0.034

MTB > print k4

 

Data Display

 

K4    51.6460

 

MTB > InvCDF 0.95;

SUBC>   ChiSquare 13.

 

Inverse Cumulative Distribution Function

Chi-Square with 13 DF

P( X <= x )        x

       0.95  22.3620

MTB > # Since nrsq = 1519*0.034= 51.6460 > chi=22.360 we have hetero from white test# Also both B-P and White test seem to indicate that totexp is the culprit

Since nrsq = 51.6460 > 22.360 = , there is sufficient evidence to reject H0 which suggests that there is heteroscedasticity in the model from White's general heteroscedasticity test at the 5% significance level.  Both Breusch Pagan test and White's general heteroscedasticity test seem to indicate that totexp is the culprit as the T value is significant and the P-value is 0.000.

Posted Date: 3/4/2013 5:59:01 AM | Location : United States







Related Discussions:- Whites general heteroscedasticity test, Assignment Help, Ask Question on Whites general heteroscedasticity test, Get Answer, Expert's Help, Whites general heteroscedasticity test Discussions

Write discussion on Whites general heteroscedasticity test
Your posts are moderated
Related Questions
Chebyshev's inequality: A statement about the proportion of the observations which fall within some number of the standard deviations of the mean for any of the probability distri

This is acronym for the Epidemiological, Graphics, Estimation and Testing of the program developed for the analysis of the data from studies in epidemiology. It can be made in use

Homoscedasticity - Reasons for Screening Data Homoscedasticity is the assumption that the variability in scores for a continuous variable is roughly the same at all values of

MEANING ,IMPORTANCE AND RELEAVANCE OF SCATTER DIAGRAM

This graph for Cross Correlation Function for RES1, RES1 shows that there is possibly negative autocorrelation as there are alternating spikes; also the first spike is negative whi

Indirect least squares: An estimation technique used in the fitting of structural equation models. Commonly least squares are first used to estimate reduced form parameters. Usi

Hello , I have a business statistic HW that is due after 23 hours exactly for now . I need full and details answers please , plus they must be in a done and typed in a word or exce

Prevalence : The measure of the number of people in a population who have a certain disease at a given point in time. It c an be measured by two methods, as point prevalence and p

The method or technique for displaying the relationships between categorical variables in a type of the scatter plot diagram. For two this type of variables displayed in the form o

In a mathematics examination the average grade was 82 and the standard deviation was 5. all students with grade from 88 to 94 received grade of B. if the grade are approximately no