[R-sig-Geo] Computational problems with errorsarlm

Javier García javier.garcia at ehu.eus
Thu Aug 3 02:19:31 CEST 2017

Hello everybody:


I am trying to estimate a spatial error model, but I am facing to several


1)  Running errorsarlm function the following message appears:


Warning messages:

1: In errorsarlm(y ~ z1 + z2 + z3 + z4 + z5 + z6 + z7 + z8 +  :

  inversion of asymptotic covariance matrix failed for tol.solve = 1e-10 

  número de condición recíproco = 3.80991e-16 - using numerical Hessian.

2: In sqrt(fdHess[1, 1]) : Se han producido NaNs



Getting the following results:


Approximate (numerical Hessian) standard error: NaN

    z-value: NaN, p-value: NA

Wald statistic: NaN, p-value: NA



This can be easily “solved” changing tol.solve from 1.0e-10 to, for example,
1.0e-20. Doing this I get  the following results


Asymptotic standard error: 14.053

    z-value: -44.177, p-value: < 2.22e-16

Wald statistic: 1951.6, p-value: < 2.22e-16


2)  However, I have a more serious problema: the estimate of lambda does not
make any sense


Lambda: -620.82, LR test value: 333.5, p-value: < 2.22e-16


Any idea about what it is happening? I am using a big dataset with 2800
observations (houses), 14 variables, and the spatial weight matrix has been
constructed “by hand” with the inverse of the inter-areas distances .
Moreover, several observations belong to the same area (in total we have
only 10 areas). As the intra-area distance is unknown but cannot be
considered zero, I calculate it as 1/(0.1*dist_min), being dist_min the
distance between the corresponding area and the nearest one (idea borrowed
from Pattanayak and Butry (2005) “Spatial complementarity of forest and
farms: accounting for ecosystem services”, American Journal of Agricultural
Economics). Could be due to my particular spatial weight matrix? Any









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