[R-sig-Geo] Spatial regression

[Roger Bivand]

On Mon, 8 Jun 2009, Adrian Toti wrote:

> 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?
>

Yes, doing likelihood ratio tests between the nesting spatial Durbin model 
and either of the nested spatial error (test on Common Factor) or lag 
(test on coefficients on lagged X) models should be OK. However, 
lagsarlm() does not (yet) support weights, I'm afraid.

Roger

>
>
> Thanks again.
>
>
>
> Adrian
>
>
>
>
> On Sat, Jun 6, 2009 at 1:24 PM, Roger Bivand <Roger.Bivand at 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 at nhh.no
>>
>>
>

-- 
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 at nhh.no