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SLX model for splm package in R

7 messages · dfamaral at usp.br, Burcu Ozuduru, Roger Bivand

Original
dfamaral at usp.br
# 
Dear colleagues, 

Does anyone know how to implement Spatial Lag of X (SLX) model for panel data using splm package? I tried the following to create a WX matrix and then apply Fixed and Random Effects: 

tb <- read.csv2("panel.csv", header = TRUE, sep = ";", dec = ".") 
time <- length(unique(tb$year)) 
X <- as.matrix(tb[ ,3:20]) 
WX <- slag(X, listw, maxlag = 1) 

I see that slag is applicable for vectors of pseries, but this command led to the error message: "Error in UseMethod("slag"): no applicable method for 'slag' applied to an object of class "c('matrix', 'double', 'numeric')" 

Is there any way to implement this SLX model for panel series? 

Regards, 

Daniel.
Roger Bivand
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On Sun, 28 Aug 2016, dfamaral at usp.br wrote:

            

      
      
      
    

        
class(X) is not "pseries", is it?

            

      
      
      
    

        
slag.default() takes a vector, slag.pseries() a "pseries" object.

            

      
      
      
    

        
Did you look at spdep::create_WX?

            

      
      
      
    

        
Please do not post HTML - like insects, HTML carries infectious payloads 
and wastes bandwidth and server processing capacity.

        

      
      
    

        

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      dfamaral at usp.br
    

    

      
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Dear Professor and colleagues,

Thank you for the response. X is a matrix and tb is a data.frame:

            

      
      
      
    

        
[1] "matrix"

            

      
      
      
    

        
[1] "data.frame"

I do not know how to apply slag to tb or X.

I also looked at spdep::create_WX and I understand its purpose is for "spatial simultaneous autoregressive error models", but in my case I just want a model such as:

y = beta1*X + beta2*WX + e

So, if anyone can give any help, I thank you again.

Daniel.

----- Mensagem original -----

            

      
      
      
    

        

        

      
      
    

        

            

      
      
      
    

  
  


  


      

    

    

      
      

        


  

        

      Burcu Ozuduru
    

    

      
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Hi,

This is a great question because I faced the same problem a while back.
What I did is to obtain the spatial weight matrix as (have spdep
installed).

S47matrix<-W1%*%S47

and then put it in my data frame and equation. Btw, above W1 is a spatial
weights matrix I obtained from a neighbor list
(W1=as.matrix(as_dgRMatrix_listw(ccListw))) and S47 is the dependent
variable.

m2<-data.frame(S41,S46,S47,S65,S68,S47matrix,MAD10000,NQPDA10000,TPBtA10000,DivA10000,Lnk10000)
summary(m2<-zeroinfl(S47~MAD10000+NQPDA10000+S47matrix, dist="negbin"))

I also wanted to ask whether I was on the right track. Professor Bivand, if
this is a common problem could you direct us to a relevant page -if
available and if at all possible- please? Your advice would be sincerely
appreciated.

Kind Regards-

Thanks-

Burcu

---

Burcu H. Ozuduru,
Gazi University, Faculty of Architecture,
Department of City and Regional Planning
Maltepe-Ankara Turkey
t: +90-312-5823701
e: bozuduru at gazi.edu.tr
http://websitem.gazi.edu.tr/bozuduru

        
On Mon, Aug 29, 2016 at 12:42 AM, Roger Bivand <Roger.Bivand at nhh.no> wrote:

        

            

      
      
      
    

        

      
      
    

        

  
  


  


      

    

    

      
      

        


  

        

      Roger Bivand
    

    

      
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This is far too ad-hoc a discussion. For Spatial panel SLX, please read 
carefully:

http://dx.doi.org/10.1016/j.spasta.2014.02.002
DOI 10.1007/s10109-015-0217-3
DOI: 10.1111/jors.12188

for a view of the state of play. One question - should you demean the X 
variables before taking spatial lags? This does not apply to cross-section 
models for which spdep::create_WX() was written, for obvious reasons. A 
second question - how do you handle the calculation of total impacts in 
this setting? In cross-section models, this is reasonably easy, by linear 
combination. But in spatial panel SLX models, is it easy? What does the 
literature suggest?

        
On Mon, 29 Aug 2016, Burcu Ozuduru wrote:

        

            

      
      
      
    

        
This is an inappropriate question for this thread (SLX spatial panel 
models). You must refer to the literature, and understand what you are 
doing:

zeroinfl(S47~MAD10000+NQPDA10000+S47matrix, dist="negbin")

is completely wrong, as S47matrix is Wy, the spatial lag of the dependent 
variable, and this spatial lag model of response S47 cannot be estimated 
using pscl::zeroinfl (do say which package provides the functions you 
mention). Your only recourse is to write your own Bayesian model. It is 
even extremely likely that this model doesn't make any substantive sense, 
unless you have a good understanding about why S47 at i (for all i) should 
be simultaneously caused by the full set of values of S47. A model in the 
error might make sense (a zero inflated negbin model with an additive mrf 
random effect (maybe R2BayesX 10.18637/jss.v014.i11).

An SLX zero inflated negbin model could maybe apply, in which case 
pscl::zeroinfl might be applicable, but then you'd be including the lags 
of MAD10000 and NQPDA10000, not the response, and inference on total 
impacts of these variables could be challenging rather than obvious (MCMC 
summaries of the summed samples of the coefficients would work, but here 
no MCMC I think - R2BayesX might help).

Most of these issues are far more complex than students or their 
supervisors understand, do ask an experienced applied statistician (I'm 
not an applied statistician, don't rely on my suggestions, you yourself 
are responsible for your results).

Hope this clarifies,

Roger

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      dfamaral at usp.br
    

    

      
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Dear all, 

Impacts in SLX model are calculated directly by parameters estimation: 

http://onlinelibrary.wiley.com/doi/10.1111/jors.12188/abstract 

Beta1 gives direct impact and Beta 2 the indirect: 

y = beta1*X + beta2*WX + e

I understand W must multiply T times the X matrix (T is the lenght of time series for a panel data). And variables are demeaned after WX is calculated.

Daniel.

----- Mensagem original -----


De: "Roger Bivand" <Roger.Bivand at nhh.no> 
Para: "Burcu Ozuduru" <bozuduru at gmail.com> 
Cc: dfamaral at usp.br, r-sig-geo at r-project.org 
Enviadas: Segunda-feira, 29 de Agosto de 2016 4:13:26 
Assunto: Re: [R-sig-Geo] SLX model for splm package in R 

This is far too ad-hoc a discussion. For Spatial panel SLX, please read 
carefully: 

http://dx.doi.org/10.1016/j.spasta.2014.02.002 
DOI 10.1007/s10109-015-0217-3 
DOI: 10.1111/jors.12188 

for a view of the state of play. One question - should you demean the X 
variables before taking spatial lags? This does not apply to cross-section 
models for which spdep::create_WX() was written, for obvious reasons. A 
second question - how do you handle the calculation of total impacts in 
this setting? In cross-section models, this is reasonably easy, by linear 
combination. But in spatial panel SLX models, is it easy? What does the 
literature suggest?

        
On Mon, 29 Aug 2016, Burcu Ozuduru wrote:

        

            

      
      
      
    

        
This is an inappropriate question for this thread (SLX spatial panel 
models). You must refer to the literature, and understand what you are 
doing: 

zeroinfl(S47~MAD10000+NQPDA10000+S47matrix, dist="negbin") 

is completely wrong, as S47matrix is Wy, the spatial lag of the dependent 
variable, and this spatial lag model of response S47 cannot be estimated 
using pscl::zeroinfl (do say which package provides the functions you 
mention). Your only recourse is to write your own Bayesian model. It is 
even extremely likely that this model doesn't make any substantive sense, 
unless you have a good understanding about why S47 at i (for all i) should 
be simultaneously caused by the full set of values of S47. A model in the 
error might make sense (a zero inflated negbin model with an additive mrf 
random effect (maybe R2BayesX 10.18637/jss.v014.i11). 

An SLX zero inflated negbin model could maybe apply, in which case 
pscl::zeroinfl might be applicable, but then you'd be including the lags 
of MAD10000 and NQPDA10000, not the response, and inference on total 
impacts of these variables could be challenging rather than obvious (MCMC 
summaries of the summed samples of the coefficients would work, but here 
no MCMC I think - R2BayesX might help). 

Most of these issues are far more complex than students or their 
supervisors understand, do ask an experienced applied statistician (I'm 
not an applied statistician, don't rely on my suggestions, you yourself 
are responsible for your results). 

Hope this clarifies, 

Roger

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Roger Bivand
    

    

      
      #
    

  


  

      
On Tue, 30 Aug 2016, dfamaral at usp.br wrote:

        

            

      
      
      
    

        
Yes, but you need the total impact beta1+beta2, but more work to infer 
from that sum, which is what you need to assess the impact of X.

            

      
      
      
    

        
Kronecker product of sparse matrices. Please contribute a patch for 
create_WX if desirable.

Please motivate the demean-after-lag concusion. Will this not affect the 
direct link between X and WX?

Roger