Markus Quandt wrote:
>
> First, thanks for your reply!
>
> Bob Hayden schrieb:
>
> > This may not fully answer all your questions, but the various formulas
> > for inference for regression have a place where you plug in THE
> > variance of the points around the regression model, i.e., they trea
Thanks to Robert Dawson (again), Steve Simon, and Rich
Ulrich for their responses.
What begins to dawn on me is that I might be confusing
residuals (as taken into account when estimating
regression equations or as displayed in the respective
plots after a regression has been estimated) and
unobse
On Tue, 30 May 2000 19:03:49 +0200, Markus Quandt
<[EMAIL PROTECTED]> wrote:
> Hello all,
>
> when discussing linear regression assumptions with a colleague, we
> noticed that we were unable to explain WHY heteroscedasticity has
> the well known ill effects on the estimators' properties. I know
Markus Quandt writes:
>when discussing linear regression assumptions with a colleague, we
>noticed that we were unable to explain WHY heteroscedasticity has
>the well known ill effects on the estimators' properties. I know
>WHAT the consequences are (loss of efficiency, tendency to
>underestimate
Markus Quandt wrote:
> Well, I said that I wanted an intuitive explanation. But at the same
time, I
> need an idea where and why the estimation procedure goes wrong, so
pointing at
> some specific formulas/terms might be very helpful. I have tried to
restate my
> exact problems in my reply to Bob
Thanks for the reply!
But it seems I was not clear enough about the level of answer I need - see
below for a hopefully more precise version of my query. Of course, pointers to
literature or web sites are highly appreciated, too. I certainly cannot ask
anyone to give lengthy explanations of convolu
OK, a simple answer:
Given heteroscedastic data, the least-squares method will put
undue emphasis on trying to fit the line to the widely-scattered part
of the plot.
This is especially troublesome when:
(a) the wide scattering occurs near one or both ends
(b) the sample size
First, thanks for your reply!
Bob Hayden schrieb:
> This may not fully answer all your questions, but the various formulas
> for inference for regression have a place where you plug in THE
> variance of the points around the regression model, i.e., they treat
> this as a constant. If it is not
der to
[EMAIL PROTECTED] using -f
To: [EMAIL PROTECTED]
Date: Tue, 30 May 2000 19:03:49 +0200
From: Markus Quandt <[EMAIL PROTECTED]>
Message-ID: <[EMAIL PROTECTED]>
Organization: Universitaet zu Koeln
X-Sender: [EMAIL PROTECTED]
Subject: WHY is heteroscedasticity bad?
Sender: [
Hello all,
when discussing linear regression assumptions with a colleague, we
noticed that we were unable to explain WHY heteroscedasticity has
the well known ill effects on the estimators' properties. I know
WHAT the consequences are (loss of efficiency, tendency to
underestimate the standard er
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