Time series in spatial regression model (spautolm)

Von: Roger Bivand [mailto:Roger.Bivand at nhh.no]
Gesendet: Samstag, 10. Oktober 2015 15:11

You cross-posted to this list and r-help - never do that - it 
splinters any replies. I have chosen to ignore your r-help posting and 
hope that others will do the same.

On Fri, 9 Oct 2015, Tobias R?ttenauer wrote:

Dear r-sig-geo team,

I started working with spatial analysis some month ago, so I'm quite 
new (and unknowing ) in this field. However, my aim is to connect 
time series analysis with spatial analysis, what seems to be quite 
difficult (to

me).

Why are you not using the splm package, which provides most of the 
models you might ever need to fit? Your manipulation of the weights 
matrix by years is a Kronecker product - used in that package. Study 
the plm package first, run your model aspatially with that, and move on to

Roger

The dataset I am working with a spatial polygons data frame, 
containing 402 spatial polygons for the years 2007-2011.

In a first step, I want to estimate a SAR model which accounts only 
for the spatial autoregressive error term within a year. So what I 
am basically trying is to construct a weights list object, 
containing only weights for neighbors in the same year (excluding 
the linkages to the "self" spatial unit (in different years) and 
neighboring units in different years). So what I tried was to expand 
the original weights matrix by duplicating the original matrix on 
the main diagonal while filling all the other linkages (e.g. 
linkages between 2007 spatial units and 2008 spatial units) with zero by

# Queens links:

data.nb<-poly2nb(data_subset.spdf)

# Remove temporal links
data2.nb<-aggregate(data.nb, data_subset.spdf$id, remove.self = 
TRUE)

# Get matrix
tmp.mat<-nb2mat(data2.nb)

# Expand matrix
zero1.mat<-matrix(0, 402, 402)
zero2.mat<-matrix(0, 402, 402)
zero3.mat<-matrix(0, 402, 402)
zero4.mat<-matrix(0, 402, 402)
zero5.mat<-matrix(0, 402, 402)

tmp2.mat<-tmp.mat
tmp3.mat<-tmp.mat
tmp4.mat<-tmp.mat
tmp5.mat<-tmp.mat

row.names(zero1.mat)<-paste("2007",

as.numeric(row.names(tmp.mat)),

sep="_") row.names(zero2.mat)<-paste("2008",
as.numeric(row.names(tmp.mat)), sep="_") 
row.names(zero3.mat)<-paste("2009",

as.numeric(row.names(tmp.mat)),

sep="_") row.names(zero4.mat)<-paste("2010",
as.numeric(row.names(tmp.mat)), sep="_") 
row.names(zero5.mat)<-paste("2011",

as.numeric(row.names(tmp.mat)),

sep="_")

row.names(tmp.mat)<-paste("2007", as.numeric(row.names(tmp2.mat)),
sep="_") row.names(tmp2.mat)<-paste("2008",
as.numeric(row.names(tmp2.mat)), sep="_") 
row.names(tmp3.mat)<-paste("2009",

as.numeric(row.names(tmp3.mat)),

sep="_") row.names(tmp4.mat)<-paste("2010",
as.numeric(row.names(tmp4.mat)), sep="_") 
row.names(tmp5.mat)<-paste("2011",

as.numeric(row.names(tmp5.mat)),

sep="_")

tmp1<-rbind(tmp.mat, zero2.mat, zero3.mat, zero4.mat, zero5.mat) 
tmp2<-rbind(zero1.mat, tmp2.mat, zero3.mat, zero4.mat, zero5.mat) 
tmp3<-rbind(zero1.mat, zero2.mat, tmp3.mat, zero4.mat, zero5.mat) 
tmp4<-rbind(zero1.mat, zero2.mat, zero3.mat, tmp4.mat, zero5.mat) 
tmp5<-rbind(zero1.mat, zero2.mat, zero3.mat, zero4.mat, tmp5.mat)

nb.mat<-cbind(tmp1, tmp2, tmp3, tmp4, tmp5)

data_sub.lw<-mat2listw(data.matrix(nb.mat))

any(is.na(nb.mat))

[1] FALSE

So I get a weights list object including 2010 observations (5 years 
with 402 observations, which fits the spatial polygon data frame 
with
2010 observations), but after running a spautolm model, I get the 
following error
message:

spreg.mod<-spautolm(sqrt(fortzuege_gem) ~ v1 + v2,

+                 data=data_subset.spdf, listw=data_sub.lw,
weights=area_sqkm,
+                 zero.policy=TRUE)
Error in subset.listw(listw, subset, zero.policy = zero.policy) :
 Not yet able to subset general weights lists

Elsewhere, it is mentioned that this error messages occurs if the 
weights matrix contains missing values, but that's not the case here.
I assume that there is some mistake in creating the weights matrix. 
Do you

have any ideas?

Another thing I was trying to estimate is a SAR model with unit 
fixed effects, just by including the id dummies into the equation 
(for the test I include the total list weights objects, containing all

data_total.nb<-poly2nb(data_subset.spdf)
data_total.lw<-nb2listw(data.nb, style="W")

spreg_false.mod<-spautolm(sqrt(fortzuege_gem) ~ id  + v1 + v2,

+                     data=data_subset.spdf, listw=data_total.lw,
weights=area_sqkm,
+                     zero.policy=TRUE)
Error in solve.default(crossprod(X, as.matrix(IlW %*% X)), tol =
tol.solve)
:
 system is computationally singular: reciprocal condition number =
1.01026e-16

So I assume there occurs some conflict, between using ID dummies and 
a weights matrix in one model? Is there any way to solve this problem?
This may be a stupid question for some who is (totally) aware of the 
mathematics behind the model.

I would be really happy about any help!

Thank you in advance and best wishes, Tobi

--
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