[R-sig-Geo] SLX model for splm package in R
Tobias Rüttenauer
ruettenauer at sowi.uni-kl.de
Sun Dec 31 09:51:53 CET 2017
Dear Daniel,
yes, that is basically the same answer to the problem. With time-series SLX you need to create a pseries and then apply slag. A second solution (using a data.frame) would be to apply lag.listw to each cross-section. Using my example below, this would be:
# Reorder data by cross-sections, then time
oo<-order(Produc$year, Produc$state)
Produc<-Produc[oo,]
# Build indices
indic<-seq(1,length(unique(Produc$year)))
inde<-as.numeric(rep(indic, each=length(unique(Produc$state))))
# Construct WX
Produc$Wpcap <- unlist(tapply(as.vector(Produc$pcap), inde, function(u) lag.listw(usa.lw, u, NAOK=T), simplify=TRUE))
Best,
Tobi
Von: Daniel Furlan Amaral [mailto:dfamaral at usp.br]
Gesendet: 29 December 2017 17:27
An: Tobias Rüttenauer <ruettenauer at sowi.uni-kl.de>
Betreff: Re: [R-sig-Geo] SLX model for splm package in R
Dear Tobias,
Thank you for the answer. Coincidently, I published my answer on Stack Overflow today and answered my own question. I think we came up with the same solution. Please see by the link what you think:
https://stackoverflow.com/questions/46004670/slx-model-spatial-econometrics-with-panel-in-r-data-using-splm-package-and-sla
Best regards,
Daniel
2017-12-22 8:18 GMT-02:00 Tobias Rüttenauer <ruettenauer at sowi.uni-kl.de <mailto:ruettenauer at sowi.uni-kl.de> >:
Dear all,
though this is an old issue by now (but still one of the first google
results on the topic), here is some code answering one of the questions:
should you demean the spatial lag or lag the demeaned variable.
As far as I understand the following results, it does not matter. Both ways
produce identical results, which both conform to the LSDV approach.
Please find the example code below:
library(spdep)
library(splm)
library(texreg)
### Demean function
dm<-function(x, id){
res<-x-ave(x,id,FUN=function(x) mean(x, na.rm=T))
res
}
data(Produc, package="plm")
data(usaww)
usa.lw<-mat2listw(usaww)
Produc.pd<-pdata.frame(Produc, index=c("state", "year"))
Produc.pd$Wpcap<-slag(Produc.pd$pcap, usa.lw)
Produc.pd$Wpc<-slag(Produc.pd$pc, usa.lw)
Produc.pd$Wunemp<-slag(Produc.pd$unemp, usa.lw)
#### LSDV ####
lsdv.mod<-lm(gsp~pcap+pc+unemp + Wpcap+Wpc+Wunemp
+state,
data=Produc.pd)
summary(lsdv.mod)
#### Demean after lag ####
dal.mod<-plm(gsp~pcap+pc+unemp + Wpcap+Wpc+Wunemp,
data=Produc.pd, effect="individual", model="within")
summary(dal.mod)
# Manual demeaning
Produc.pd[,4:ncol(Produc.pd)]<-apply(Produc.pd[,4:ncol(Produc.pd)], 2,
function(x) dm(x, Produc.pd$state))
dal2.mod<-lm(gsp~pcap+pc+unemp + Wpcap+Wpc+Wunemp,
data=Produc.pd)
summary(dal2.mod)
#### Lag after demean ####
# Replace slags by demeaned slags
Produc.pd$Wpcap<-slag(Produc.pd$pcap, usa.lw)
Produc.pd$Wpc<-slag(Produc.pd$pc, usa.lw)
Produc.pd$Wunemp<-slag(Produc.pd$unemp, usa.lw)
lad.mod<-lm(gsp~pcap+pc+unemp + Wpcap+Wpc+Wunemp,
data=Produc.pd)
summary(lad.mod)
screenreg(l=list(lsdv.mod, dal.mod, dal2.mod, lad.mod),
omit.coef = "state", digits=6)
#### Test if both ways produce identical vectors ####
Produc.pd<-pdata.frame(Produc, index=c("state", "year"))
x<-Produc.pd$gsp
# lag after demean
dx<-dm(x, Produc.pd$state)
wdx<-slag(dx, usa.lw)
# deaman after lag
wx<-slag(x, usa.lw)
dwx<-dm(wx, Produc.pd$state)
# Test for equality
all.equal(wdx, dwx)
Best,
Tobi
Tobias Rüttenauer
TU Kaiserslautern
Erwin-Schrödinger-Straße 57
67663 Kaiserslautern
ruettenauer at sowi.uni-kl.de <mailto:ruettenauer at sowi.uni-kl.de>
Tel.: 0631 205 5785
> -----Ursprüngliche Nachricht-----
> Von: R-sig-Geo [mailto:r-sig-geo-bounces at r-project.org <mailto:r-sig-geo-bounces at r-project.org> ] Im Auftrag von
> Roger Bivand
> Gesendet: 30 August 2016 08:44
> An: dfamaral at usp.br <mailto:dfamaral at usp.br>
> Cc: r-sig-geo at r-project.org <mailto:r-sig-geo at r-project.org>
> Betreff: Re: [R-sig-Geo] SLX model for splm package in R
>
> On Tue, 30 Aug 2016, dfamaral at usp.br <mailto:dfamaral at usp.br> wrote:
>
> > 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
> >
>
> 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.
>
> > 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.
>
> 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
>
> >
> > Daniel.
> >
> > ----- Mensagem original -----
> >
> >
> > De: "Roger Bivand" <Roger.Bivand at nhh.no <mailto:Roger.Bivand at nhh.no> >
> > Para: "Burcu Ozuduru" <bozuduru at gmail.com <mailto:bozuduru at gmail.com> >
> > Cc: dfamaral at usp.br <mailto:dfamaral at usp.br> , r-sig-geo at r-project.org <mailto: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:
> >
> >> 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,TPBt
> >> A10000,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.
> >
> > 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
> >
> >>
> >> 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 <mailto: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 <mailto:Roger.Bivand at nhh.no> > wrote:
> >>
> >>> On Sun, 28 Aug 2016, dfamaral at usp.br <mailto:dfamaral at usp.br> wrote:
> >>>
> >>> 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])
> >>>>
> >>>
> >>> class(X) is not "pseries", is it?
> >>>
> >>> 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')"
> >>>>
> >>>
> >>> slag.default() takes a vector, slag.pseries() a "pseries" object.
> >>>
> >>>
> >>>> Is there any way to implement this SLX model for panel series?
> >>>>
> >>>
> >>> Did you look at spdep::create_WX?
> >>>
> >>>
> >>>> Regards,
> >>>>
> >>>> Daniel.
> >>>>
> >>>> [[alternative HTML version deleted]]
> >>>>
> >>>>
> >>> Please do not post HTML - like insects, HTML carries infectious
payloads
> >>> and wastes bandwidth and server processing capacity.
> >>>
> >>> _______________________________________________
> >>>> R-sig-Geo mailing list
> >>>> R-sig-Geo at r-project.org <mailto:R-sig-Geo at r-project.org>
> >>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >>>>
> >>>>
> >>> --
> >>> Roger Bivand
> >>> Department of Economics, Norwegian School of Economics,
> >>> Helleveien 30, N-5045 Bergen, Norway.
> >>> voice: +47 55 95 93 55 <tel:%2B47%2055%2095%2093%2055> ; fax +47 55 95 91 00 <tel:%2B47%2055%2095%2091%2000>
> >>> e-mail: Roger.Bivand at nhh.no <mailto:Roger.Bivand at nhh.no>
> >>> http://orcid.org/0000-0003-2392-6140
> >>> https://scholar.google.no/citations?user=AWeghB0AAAAJ <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en> &hl=en
> >>> http://depsy.org/person/434412
> >>>
> >>> _______________________________________________
> >>> R-sig-Geo mailing list
> >>> R-sig-Geo at r-project.org <mailto:R-sig-Geo at r-project.org>
> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >>>
> >>
> >
> >
>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55 <tel:%2B47%2055%2095%2093%2055> ; fax +47 55 95 91 00 <tel:%2B47%2055%2095%2091%2000>
> e-mail: Roger.Bivand at nhh.no <mailto:Roger.Bivand at nhh.no>
> http://orcid.org/0000-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0AAAAJ <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en> &hl=en
> http://depsy.org/person/434412
>
> _______________________________________________
> R-sig-Geo mailing list
> R-sig-Geo at r-project.org <mailto:R-sig-Geo at r-project.org>
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
[[alternative HTML version deleted]]
More information about the R-sig-Geo
mailing list