On 9 October 2015 at 18:24, Allin Cottrell <cottrell(a)wfu.edu> wrote:
[...]
For the record, then, let's point out that the two basic approaches to
heteroskedasticity in gretl -- namely, switching to
"robust" standard
errors, or switching from OLS to GLS via the "hsk" command -- do not
require taking logs of negative numbers. The following script illustrates.
The series y and x contain both positive and negative values, and the
data-generating process is heteroskedastic by construction.
<hansl>
nulldata 50
set seed 3711
series x = normal()
# generate heteroskedastic y
series y = -1 + 3*x + normal()*x
# verify we have negative values in both y and x
print y x --byobs
# run OLS
ols y 0 x
# try robust standard errors: no problem
ols y 0 x --robust
# try GLS: again, no problem
hsk y 0 x
</hansl>
In this case the "hsk" command produces a closer approximation to the true
x-slope of 3.0 (2.997, versus 3.098 from OLS), although obviously one would
have to replicate the example a large number of times to verify that (as
theory says) the hsk estimates are more efficient, given heteroskedasticity.
[...]
Very interesting things happen when you keep increasing the simulation
sample size by factors of 10.
At N=500, the OLS estimate is closer to the true value of X (3.01) than the
GLS estimate (3.06). At N=5000, they were pretty much identical, yet
further away from X than before (2.97). At N=50000, they both tended back
to the true value at 2.99.
As Arthur Atkinson without his washboard would have said, "How queer!"
C