EAKIN MARK E wrote:
Besides independent normal errors with mean zero and constant
variance, some (many?) econometric text books do make the assumption that
the independent variables are uncorrelated. For example see
Gujarti, Damodar (1988), _Basic Econometrics 2nd edition_, McGraw
"David A. Heiser" wrote:
- Original Message -
From: Warren Sarle [EMAIL PROTECTED]
To: [EMAIL PROTECTED]
Sent: Thursday, May 04, 2000 12:23 PM
Subject: Re: no correlation assumption among X's in MLR
If the independent variables in a multiple linear regression are
On Wed, 3 May 2000, Alan McLean wrote in part:
With regard to correlation and collinearity - I have become used to
'explaining' collinearity to my classes in terms only of pairs of
explanatory variables, forgetting that the collinearity could involve a
set of three or more variables, and
In article 002501bfb563$e8e6e8e0$[EMAIL PROTECTED],
[EMAIL PROTECTED] (David A. Heiser) wrote:
- Original Message -
From: Herman Rubin [EMAIL PROTECTED]
To: [EMAIL PROTECTED]
Sent: Wednesday, May 03, 2000 8:20 AM
Subject: Re: no correlation assumption among X's in MLR
- Original Message -
From: Herman Rubin [EMAIL PROTECTED]
To: [EMAIL PROTECTED]
Sent: Wednesday, May 03, 2000 8:20 AM
Subject: Re: no correlation assumption among X's in MLR
In article [EMAIL PROTECTED],
Alan McLean [EMAIL PROTECTED] wrote:
'No collinearity' *means* the X variables
Hi Don,
There are times when I realise the rust that has accumulated, and this is one
of them.
Changing the order of things a little, you (and DS) are of course quite
correct that X variables are typically correlated, and that if they are not
the coefficients are the same as if a set of simple
Actually Gujarati is just listing his version of the assumptions which
guarantee that OLS is BLUE. One of these is that there is no
collinearity between the X variables. By this he means that the matrix
of independent variables must have full rank, otherwise OLS estimates
cannot be calculated.
'No collinearity' *means* the X variables are uncorrelated!
The basic OLS method assumes the variables are uncorrelated (as you say). In
practice there is usually some correlation, but the estimates are reasonably
robust to this. If there is *substantial* collinearity you are in trouble.
Alan
On Tue, 2 May 2000, Alan McLean wrote:
'No collinearity' *means* the X variables are uncorrelated!
This is not my understanding. "Uncorrelated" means that the correlation
between two variables is zero, or that the intercorrelations among
several variables are all zero. "Not collinear"
Besides independent normal errors with mean zero and constant
variance, some (many?) econometric text books do make the assumption that
the independent variables are uncorrelated. For example see
Gujarti, Damodar (1988), _Basic Econometrics 2nd edition_, McGraw Hill, p.
166
Mark
At 11:09 AM 4/28/00 -0500, EAKIN MARK E wrote:
Besides independent normal errors with mean zero and constant
variance, some (many?) econometric text books do make the assumption that
the independent variables are uncorrelated. For example see
Gujarti, Damodar (1988), _Basic Econometrics 2nd
On Fri, 28 Apr 2000, EAKIN MARK E wrote:
Besides independent normal errors with mean zero and constant
variance, some (many?) econometric text books do make the assumption
that the independent variables are uncorrelated. For example see
Gujarti, Damodar (1988), _Basic Econometrics 2nd
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