LM tests

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LM tests

Jill Caviglia-Harris
Actually wouldn't the row standardization imply that the weights become
(1/dij)/ sum dij)?  The objective is get the rows to sum to 1.  I
answered your other question in the last email.  (I won't repeat) It
appears that row standardizing will change the economics interpretation
(therefore the coefficient values).  Something that I will find
interesting to look into empirically.  I'll get back to you.  -Jill

***************************************************
Jill L. Caviglia-Harris, Ph.D.
Assistant Professor
Economics and Finance Department
Salisbury University
Salisbury, MD 21801-6860
   phone: (410) 548-5591
   fax: (410) 546-6208
*********************************************************

>>> "Munroe, Darla K" <dkmunroe at email.uncc.edu> 02/27/04 03:06PM >>>
 
oops - I meant to say - with row-standardization your weights become:
1/dij/n-1 (with 0 elements on the diagonal).

So, you've just rescaled dij by n-1 for all i, but you haven't lost
the
ranking of the distances from low to high.
-----Original Message-----
From: Roger Bivand
To: Jill Caviglia-Harris
Cc: r-sig-geo at stat.math.ethz.ch
Sent: 2/27/04 2:40 PM
Subject: Re: [R-sig-Geo] LM tests

On Fri, 27 Feb 2004, Jill Caviglia-Harris wrote:

> List members:
>
> I have been using the function lm.LMtests developed using the spdep
> package to test for spatial lag and error.  My problem is that these
> tests assume that the weights matrix is row standardized, while I
have
a
> weights matrix that is set up as the inverse distance between
neighbors.

Certainly lm.LMtests() prints a warning, and the tradition it comes
from

usually presupposes row standardisation. Curiously, quite a lot of the

distribution results in Cliff and Ord actually assume symmetry, which
can
lead to fun with negative variance in Geary's C and join count
statistics
even with row standardised weights.

>   Converting it into a row standardized matrix would result in the
loss
> of important information.  Have there been any functions developed
that
> any of you know about that are not dependent upon this assumption?

Have you tried (probably yes) and does it make a difference? Are the
results from a binary IDW and a row standardised IDW very different? Is

your IDW matrix full or sparse? Can Moran's I be applied instead
(despite
its covering lots of misspecification problems)? Are the IDW weights
symmetric (probably, but not always)?

I'm not sure why distances should be helpful if the data are observed
on

areal units, so that measuring distances is between arbitrarily chosen

points in those units, a change of support problem. That may be why
there
aren't methods too, though there's no reason not to try to develop
things.
But error correlation specified by distance does movbe rather close to

geostatistics, doesn't it?

Any other views, anyone?

Roger

> Thanks.  -Jill
>
>
> ***************************************************
> Jill L. Caviglia-Harris, Ph.D.
> Assistant Professor
> Economics and Finance Department
> Salisbury University
> Salisbury, MD 21801-6860
>    phone: (410) 548-5591
>    fax: (410) 546-6208
>
> _______________________________________________
> R-sig-Geo mailing list
> R-sig-Geo at stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/r-sig-geo 
>

--
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School
of
Economics and Business Administration, Breiviksveien 40, N-5045
Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 93 93
e-mail: Roger.Bivand at nhh.no

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