So testing for spatial dependence on the residuals by means of the lm.LMtests (option LMerr, the only that works with residuals) is wrong, isn't it? I had read in some forum that this was a posible way to test it...
In my case the Moran test and the LM tests (both LMerr and LMlag, and also their robust versions) are strongly rejected (p-values between 4.307e-06 and 2.2e-16). As the rejection is stronger for the spatial error model, my suspicion was that this could be the best model to capture the spatial dependence (in fact the log-likelihood is bigger for the spatial error model, and the AIC lower). However, how can I know whether the spatial error model is a good option if I cannot test the absence of spatial dependence in the residuals? And how can I know, as you suspect, whether I have a misspecification problem? Moreover, I also estimated the Durbin model, and in this case the LM test on the residuals suggests no spatial dependence (for the spatial lag model I get the opposite conclusion), but due to the nature of my regression I don't think that this model is suitable (the regressors are characteristics of houses such as size, number of rooms, etc). Thanks a lot for your time. Best Javi -----Mensaje original----- De: Roger Bivand [mailto:[email protected]] Enviado el: domingo, 13 de agosto de 2017 12:45 Para: Javier García CC: [email protected] Asunto: Re: [R-sig-Geo] How to test spatial dependence in errorsarlm On Sun, 13 Aug 2017, Javier García wrote: > Hello everybody: > > > > I have estimated a spatial error model and now I would like to test > whether that model has really ?deleted? the spatial dependence. For > the spatial lag model and for the Durbin model the function lagsarlm > gives the LM test for residual autocorrelation test value, but the function errorsarlm does not. > Does anyone know how to do it in R? > As you should be aware from the literature, the only LM test that has been written (the maths) is a test for residual error autocorrelation for spatial lag models. Doing it in R will not help until someone (you?) does the maths. Computing a value is easy, but knowing what to infer from it is hard. By definition, if your model is well-specified, the residual autocorrelation is fully captured by its coefficient. I suspect that your model suffers from mis-specification problems. Roger > > > Thanks a lot in advance. > > > > Javi > > > > > > > JAVIER GARCÍA > > > > Departamento de Economía Aplicada III (Econometría y Estadística) > > Facultad de Economía y Empresa (Sección Sarriko) > Avda. Lehendakari Aguirre 83 > > 48015 BILBAO > T.: +34 601 7126 F.: +34 601 3754 > <http://www.ehu.es/> www.ehu.es > > http://www.unibertsitate-hedakuntza.ehu.es/p268-content/es/contenidos/inform > acion/manual_id_corp/es_manual/images/firma_email_upv_euskampus_bilingue.gif > > > > > > -- Roger Bivand Department of Economics, Norwegian School of Economics, Helleveien 30, N-5045 Bergen, Norway. voice: +47 55 95 93 55; e-mail: [email protected] Editor-in-Chief of The R Journal, https://journal.r-project.org/index.html http://orcid.org/0000-0003-2392-6140 https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
