[R-sig-Geo] Question impacts lagged independent variables lagsarlm

[Jorge Cárcamo]

Prof. Bivand,

Many thanks for your clarification. I think that I should read Läpple &
Kelley again.

One more question, I assume that if I create independent lag variables
with: data$w_xxx <- lag.listw(sy15,data$xxx) and add them into the model I
can get the impacts of these lag variables; however, if I add them into the
model, do I need to change the type="lag" instead of "mixed" when running
lagsarlm command?

Many thanks for your attention.

Jorge

*Ing. Jorge Alfredo Cárcamo, M. Sc., Ph. D. (c)*
Agriculture economics
Georg-August-Universität Göttingen

On Sun, Jun 5, 2016 at 2:17 PM, Roger Bivand <Roger.Bivand at nhh.no> wrote:

> On Sat, 4 Jun 2016, Jorge Cárcamo wrote:
>
> Dear all,
>>
>> I am working with a lagsarlm "mixed" model I executed:
>> library(spdep)
>> library(coda)
>> dsts15 <- nbdists(nbs15, data.xy)
>> idw15 <- lapply(dsts15, function(x) 1/(x))
>> sy15 <- nb2listw(nbs15, glist=idw15, style="W")
>> mod.sdm.15<-lagsarlm(AR32 ~ SD46 + Totaland + PC18 + PC22 + sra + sla +
>> saa
>> + owue + yearrain + yearfdi, data=data, listw=sy15, type="mixed",
>> tol.solve=1.0e-12)
>> summary(mod.sdm.15)
>>
>> And got following results (abridged table):
>>
>>               Estimate Std. Error z value Pr(>|z|)
>> (Intercept)   1.4255019  0.4824828  2.9545 0.003132
>> SD46          0.0149057  0.0116146  1.2834 0.199365
>> Totaland     -0.0217556  0.0072475 -3.0018 0.002684
>> ...
>> yearrain      0.0557373  0.0485612  1.1478 0.251062
>> yearfdi      -0.0109979  0.0069536 -1.5816 0.113741
>> lag.SD46      0.0262273  0.0278045  0.9433 0.345539
>> lag.Totaland  0.0060992  0.0141515  0.4310 0.666474
>> ...
>> lag.yearrain -0.2083118  0.0772751 -2.6957 0.007024
>> lag.yearfdi   0.0111460  0.0081562  1.3666 0.171761
>> Rho: -0.54702, LR test value: 10.052, p-value: 0.0015215
>>
>> Immediatly, I exectued impacts(mod.sdm.15, R=1000), to get the impacts:
>>
>>               Direct     Indirect         Total
>> SD46      0.013136761  0.013451739  0.0265884995
>> Totaland -0.023517332  0.013397014 -0.0101203177
>> ...
>> yearrain  0.079110119 -0.177734631 -0.0986245121
>> yearfdi  -0.012677636  0.012773412  0.0000957761
>>
>> Through simulation I manage to get credible intervals for direct, indirect
>> and total impacts (HPDinterval(impacts, choice="XXX")).
>>
>
> As you must know from the references on the help page for impacts methods,
> these are the combined impacts of the variables:
>
> S_r(W) = (I - \rho W)^{-1} (\beta_r I - \gamma_r W)
>
> where the direct impacts are sum(S_r(W))/n, etc. The \gamma_r are the
> coefficients on W x_r.
>
>
>> However, how can I get the impacts of the lagged variables? I have seen
>> some publications such as: Läpple, D., & Kelley, H. (2014). Spatial
>> dependence in the adoption of organic drystock farming in Ireland. That
>> report a posterior mean and credible intervals for the lagged variables.
>>
>
> Given the above, either you are misreading Läpple & Kelley (I do not have
> access), or both you and they are wrong. There are by definition on
> separable impacts for the lagged X variables.
>
> Hope this clarifies,
>
> Roger
>
>
>> All suggestions are welcome.
>>
>> Best regards,
>>
>> *Ing. Jorge Alfredo Cárcamo, M. Sc., Ph. D. (c)*
>> Agriculture economics
>> Georg-August-Universität Göttingen
>>
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>>
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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; fax +47 55 95 91 00
> e-mail: Roger.Bivand at nhh.no
> http://orcid.org/0000-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
> http://depsy.org/person/434412

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