multicollinearity, howto
Posted: Sat Dec 18, 2010 12:45 pm
Could you explaine me, how can I test my regression on multicollinearity in eviews?
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Re: multicollinearity, howto
Posted: Sat Dec 18, 2010 2:20 pm
I can't imagine why you would want to do this, but look under View/Coefficient diagnostics/Variance inflation factors
Re: multicollinearity, howto
Posted: Thu Jun 09, 2011 4:28 am
Sorry? but I do`n undirstand haw to calculated the Variance Inflation Factors?
I have the equation of regression: y=c0+c1x1+c2x2+c3x3
In help says: The uncentered VIF is the ratio of the variance of the coefficient estimate from the original equation divided by the variance from a coefficient estimate from an equation with only one regressor (and no constant) =>
for x1 I must to take the equation y=x1 and the variance of the coefficient estimate from this equation.
and divided variance of the coefficient estimate (x1) on variance of the coefficient estimate from auxiliary equation, but i have different result. :( :( :(
Please, help me...
I have the equation of regression: y=c0+c1x1+c2x2+c3x3
In help says: The uncentered VIF is the ratio of the variance of the coefficient estimate from the original equation divided by the variance from a coefficient estimate from an equation with only one regressor (and no constant) =>
for x1 I must to take the equation y=x1 and the variance of the coefficient estimate from this equation.
and divided variance of the coefficient estimate (x1) on variance of the coefficient estimate from auxiliary equation, but i have different result. :( :( :(
Please, help me...
Re: multicollinearity, howto
Posted: Thu Jun 09, 2011 9:08 am
The manual isn't quite right in the description, since the calculations hold sigmasq constant between the two regressions. The uncentered VIF is actually the variance of the coefficient from the original regression, divided by sigmasq (from original regression) / inner_product(Xi).
As an example, you can run the following program that calculates it manually:
As an example, you can run the following program that calculates it manually:
Code: Select all
create u 100
series y=nrnd
series x1=nrnd
series x2=nrnd
equation eq1.ls() y x1 x2 c
freeze(vif) eq1.varinf
scalar vif1 = eq1.@coefcov(1,1) / (eq1.@se^2 / @inner(x1))
scalar vif2 = eq1.@coefcov(2,2) / (eq1.@se^2 / @inner(x2))
Re: multicollinearity, howto
Posted: Fri Jun 10, 2011 12:54 am
Thanks :D , I'll try this option
Re: multicollinearity, howto
Posted: Thu Jun 16, 2011 12:18 am
Hello, i'm realy soory, but second test on multicollinearity also fails(((
in help written, V is a matrix whose columns are equal to the corresponding eigenvectors and mu(j) is the j-th eigenvalue, and v(ij) is the (i,j)-th element of V, but I calculat eigenvalue and eigen vector in Eviews 7 (eigenvalue decomposition) and get other numbers!!!
i have matrix
6
69 983
26 278 152
31 359 130 199
in Coefficient Variance Decomposition
Eigenvalues 275.1193 2.574688 1.316740 0.088263
Eigenvectors
Variable 1 2 3 4
C -0.995697 -0.001729 -0.065226 0.065798
X1 0.030188 -0.098771 0.441103 0.891494
X2 0.065934 -0.644378 -0.709543 0.277450
X3 0.057690 0.758300 -0.545640 0.352038
and in
eigenvalue decomposition
Eigenvalues:
1 1216.375
2 81.53567
3 41.69875
4 0.390235
Eigenvectors (loadings):
Variable PC 1 PC 2 PC 3 PC 4
C1 0.065798 0.065226 -0.001729 0.995697
C2 0.891494 -0.441103 -0.098771 -0.030188
C3 0.277450 0.709543 -0.644378 -0.065934
C4 0.352038 0.545640 0.758300 -0.057690
please explain to me the paradox ...
in help written, V is a matrix whose columns are equal to the corresponding eigenvectors and mu(j) is the j-th eigenvalue, and v(ij) is the (i,j)-th element of V, but I calculat eigenvalue and eigen vector in Eviews 7 (eigenvalue decomposition) and get other numbers!!!
i have matrix
6
69 983
26 278 152
31 359 130 199
in Coefficient Variance Decomposition
Eigenvalues 275.1193 2.574688 1.316740 0.088263
Eigenvectors
Variable 1 2 3 4
C -0.995697 -0.001729 -0.065226 0.065798
X1 0.030188 -0.098771 0.441103 0.891494
X2 0.065934 -0.644378 -0.709543 0.277450
X3 0.057690 0.758300 -0.545640 0.352038
and in
eigenvalue decomposition
Eigenvalues:
1 1216.375
2 81.53567
3 41.69875
4 0.390235
Eigenvectors (loadings):
Variable PC 1 PC 2 PC 3 PC 4
C1 0.065798 0.065226 -0.001729 0.995697
C2 0.891494 -0.441103 -0.098771 -0.030188
C3 0.277450 0.709543 -0.644378 -0.065934
C4 0.352038 0.545640 0.758300 -0.057690
please explain to me the paradox ...
Re: multicollinearity, howto
Posted: Thu Jun 16, 2011 8:12 am
I can't follow what you've done there, but for an equation, you can match the eigenvalues reported in the coefficient variance decomposition, by using this command:
where eq1 is the name of the equation.
If you're trying to decipher the technical details behind these type of diagnostic statistics, I highly recommend getting the Belsley, Kuh and Welsch (2004) book. It is a great book.
Code: Select all
=@eigenvalues(eq1.@coefcov)
If you're trying to decipher the technical details behind these type of diagnostic statistics, I highly recommend getting the Belsley, Kuh and Welsch (2004) book. It is a great book.
Posted: Sat Aug 04, 2012 1:22 am
can u tell whether we should look ''centered vif'' or ''uncentered vif' nd y'???plz help me... :( :(