[R-sig-Geo] "spdep": check whether a spatial model fully controls for spatial correlation

[Roger Bivand]

On Sat, 21 Mar 2020, Gary Dong wrote:

> Dear all,
>
> I have estimated a spatial error model via the "spdep" package. The 
> spatial weights are determined based on the inverse distance between an 
> observation and its 50 nearest neighbors (knearneigh, k=50). Now I 
> wonder if my spatial error model has FULLY controlled for spatial 
> autocorrelation in the data. Is there a way to test it? I know I can use 
> lm.morantest() to test spatial autocorrelation in residuals from an 
> estimated OLS model. But I do not know if there is a similar test for a 
> spatial error model. Any advice is greatly appreciated.

You will know that there is a Lagrange Multiplier test for spatial lag 
model residuals. There is however no test for the residuals of a spatial 
error model. IDW will not help either - the choice of W as a fixed graph 
stipulates that you definitely know that it is the way observations relate 
to each other. Even PCNM/MESF (spatial filtering with the eigenvectors of 
a centred weights matrix) still assumes that the weights matrix is known.

For spatial error models, you should always report the Hausman test. 
You can only accept that SEM is not misspecified if it confirms that the 
SEM and OLS coefficients are close. Unobserved covariates are a typical 
cause of trouble; adding WX (the SDEM, D for Durbin) may help. But if your 
phenomena exhibit different scaling in the footprints of their spatial 
processes, testing (if a test existed) with the same W wouldn't expose the 
problem.

Probably SLX and SDEM are worth exploring, and for SEM and SDEM, reporting 
the Hausman test.

There is a literature starting to appear on adaptive spatial weights, some 
functionality is in CARBayes.

Hope this helps,

Roger

>
> Best
> Gary
>
>
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-- 
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; e-mail: Roger.Bivand using nhh.no
https://orcid.org/0000-0003-2392-6140
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