Hi
The test of this white Regression model does not have a constant...
Right?
Sent from my iPhone
Apologize for the brevity/grammar/spelling
No dia 10/01/2014, às 18:12, "Riccardo (Jack) Lucchetti"
<r.lucchetti(a)univpm.it> escreveu:
> On Fri, 10 Jan 2014, Ana Amaro ISG wrote:
>
> Hi everyone
> I need some help on this topic:
> 1- simple model one regressor (x)
> 2- error is heteroskedastic (and residual analysis shows that the error variance
increases with x variable)
> 3- rerun the analysis dividing both model members by the sqrt(x) - no constant
>
> Eviews runs the white test with a constant
> Gretl runs the white test with no constant!
> The decision is, of course, different.
>
> Which software is doing it the right way? Eviews, right?
Uhm, this is not what I'm seeing here.
<hansl>
nulldata 100
set seed 987
x = normal()
e = normal() * sqrt(1 + x^2)
y = x + e
ols y x
modtest --white
</hansl>
Of course, generating data which _does_ contain heterskedasticity is totally irrelevant
here: if I understand the point correctly, the issue here is on whether a constant should
be present or not in the auxiliary regression for the White test. And the answer is: yes,
the constant should be there. And that's what gretl does:
<output>
? ols y x
Model 1: OLS, using observations 1-100
Dependent variable: y
coefficient std. error t-ratio p-value
--------------------------------------------------------
x 0.955352 0.128086 7.459 3.40e-11 ***
Mean dependent var 0.340859 S.D. dependent var 1.747793
Sum squared resid 201.0589 S.E. of regression 1.425096
R-squared 0.359770 Adjusted R-squared 0.359770
F(1, 99) 55.63201 P-value(F) 3.40e-11
Log-likelihood −176.8152 Akaike criterion 355.6305
Schwarz criterion 358.2357 Hannan-Quinn 356.6849
? modtest --white
White's test for heteroskedasticity
OLS, using observations 1-100
Dependent variable: uhat^2
coefficient std. error t-ratio p-value
--------------------------------------------------------
x −0.103511 0.315278 −0.3283 0.7434
sq_x 0.798762 0.152451 5.239 9.24e-07 ***
Unadjusted R-squared = 0.243094
Test statistic: TR^2 = 24.309362,
with p-value = P(Chi-square(1) > 24.309362) = 0.000001
</output>
-------------------------------------------------------
Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
-------------------------------------------------------
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