Thank you Roger,



In a paper (
http://www.ssc.wisc.edu/cde/methodology/statcore/Balleretal2001.pdf) that
uses spatial regression in homicide rates analysis (and discusses the
difference between spatial and lag models) there is a case where over time
there is a switch between the best model that fits the data, from a lag
model to an error one.



In this context – a switch over time between an error to lag or vice versa –
using just lm.LMtests function might not be very helpful in understanding
what is behind the change.



If, as an example, there is a change in better fit from an error to a lag
model what would be the steps to understand what caused the change.

So may question is: is it comparing spatial Durbin model (lagsarlm() type =
“mixed”) with spatial lag model (lagsarlm()) and also comparing Durbin model
with errorsarlm() a more structured approach?





Thanks again.



Adrian




On Sat, Jun 6, 2009 at 1:24 PM, Roger Bivand <Roger.Bivand@nhh.no> wrote:

>  On Fri, 29 May 2009, Adrian Toti wrote:
>
> Hi everyone,
>>
>> In R, the function lm.LMtests is used to select which spatial model fits
>> better the data, an error or lag one. It looks to me that there are some
>> ?limitations? using this function like being constraint to use only
>> row-standardized spatial weights and it does not support weighted lm
>> object
>> as argument. Also it is not helpful when both LM tests (LMerr and LMlag)
>> are
>> significant (let?s say their robust forms are not).
>>
>> So my question is: is there another way or approach to select between a
>> spatial error and lag model?
>>
>>
> As you appreciate, the two models are not nested, so an LR test is not
> appropriate, and comparing the AIC values probably not either (although a
> large difference in AIC should not be disregarded). Neither errorsarlm() not
> lagsarlm() use weights - this is only possible in spautolm(), which is an
> alternative implementation of the same model as errorsarlm(). So I'm not
> sure how you could fit a weighted lag model.
>
> There is a paper by Kelejian in Letters in Spatial and Resource Sciences in
> 2008 on a spatial J-test, but this relies on feasible GM and spatial TSLS
> estimates, which most often also assume row standardisation and do not
> permit weights. A recent paper by Burridge and Fingleton (from a meeting
> earlier this week) discusses some alternatives:
>
> http://sew2009.univ-fcomte.fr/english/Burridge_Fingleton.pdf
>
> but these are too fresh to be coded and packaged yet (and are also in the
> spatial econometrics view of the world).
>
> Hope this helps,
>
> Roger
>
>
>>
>> Thanks for your time.
>>
>>
>>
>> Adrian
>>
>>        [[alternative HTML version deleted]]
>>
>>
>>
> --
> Roger Bivand
> Economic Geography Section, Department of Economics, Norwegian School of
> Economics and Business Administration, Helleveien 30, N-5045 Bergen,
> Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
> e-mail: Roger.Bivand@nhh.no
>
>

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