Dear R Users,
I have an unbalanced panel with (on average) approx. 100 individuals over
1370 time intervals (with individual time series of different lengths,
varying between 60 and 1370 time intervals). I use the following model:
res1<-plm(x~c+d+e,data=pdata_frame, effect="twoways", model="within",
na.action=na.omit))
I repeatedly get the following error (which has been discussed in the past):
Error in La.svd(x, nu, nv) : error code 1 from Lapack routine ‘dgesdd’
I found it hard to create a reproducible example. As noted by Douglas Bates,
the error might be related to the scaling of the matrix.
For variables x,c,d,and e in object pdata_frame, I find that all sd() are
reasonably similar both among the cross-sections as well as among the
variables. However, I find that extracting the demeaned data from plm(),
variables demXt$d and demXt$e (i.e. the demeaned variables) have sd()s that
are very small compared to those of dem_yt and demXt$c (approx. by factor
1e-15). I extract the demeaned data as follows:
dem_yt<-pmodel.response(res)
demXt<-model.matrix(res)
How is this possible? What is it that plm() does with my data so that the
standard deviations change?
I suspect effect="twoways" to play a central role because plm() works fine
for effect="individual". I thought about the idea that maybe, time-effects
simply do not apply here. However: In order to test my regression for
time-effects (which I detect for subsamples (by time) and for equation x~e
at high levels of significance), I need both the model with and and the
model without time effects (as otherwise, I can't compare the two models in
an F-test), right? Any alternative tests?
Another thought was that the impact of d and e changes over time (as in the
subsamples I do see such a change).
Any help is appreciated!
Best wishes,
Philipp Grueber
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____________________________________
EBS Universitaet fuer Wirtschaft und Recht
FARE Department
Wiesbaden/ Germany
http://www.ebs.edu/index.php?id=finacc&L=0
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