Computational problems with errorsarlm

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Computational problems with errorsarlm

Javier García

Hello everybody:

 

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

 

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 alternative?

 

 

Cheers

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
www.ehu.es

http://www.unibertsitate-hedakuntza.ehu.es/p268-content/es/contenidos/informacion/manual_id_corp/es_manual/images/firma_email_upv_euskampus_bilingue.gif

 

 


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Re: Computational problems with errorsarlm

Roger Bivand
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Definitely your weights matrix. The matrix must be known and fixed. You should not try to use errorsarlm with only very few spatially identified grouped observations. Only use a multilevel approach, such as that in the HSAR package, see articles referenced there, or in an online  article in Spatial Statistics by Zhe Sha and coauthors at:
Roger Bivand
Norwegian School of Economics
Bergen, Norway



Fra: Javier García
Sendt: torsdag 3. august, 02.19
Emne: [R-sig-Geo] Computational problems with errorsarlm
Hello everybody:
 
I am trying to estimate a spatial error model, but I am facing to several problems
 
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 alternative?
 
 
Cheers
Javi
 
JAVIER GARCÍA

 

Departamento de Economía Aplicada III (Econometría y Estadística)

Facultad de Economía y Empresa (Sección Sarriko)

Avda. Lehendakari Aguirre

http://www.unibertsitate-hedakuntza.ehu.es/p268-content/es/contenidos/informacion/manual_id_corp/es_manual/images/firma_email_upv_euskampus_bilingue.gif


83

48015 BILBAO

T.: +34 601 7126
F.: +34 601 3754

 
 



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Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N-5045 Bergen, Norway
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