Dear Thomas,
I cannot really answer because this is not a reproducible example; but your
traceback() output already gives a hint: try changing the random.method to
something different from default. In fact, as the singular matrix problem
happens during estimation of variance components, using a different method may
circumvent it.
Also please update.packages, and notice that plm.data() is deprecated
(actually, it is defunct in the development version): please use pdata.frame()
instead.
Best wishes,
Giovanni
Giovanni Millo, PhD
Research Dept.,
Assicurazioni Generali SpA
Via Machiavelli 3,
34132 Trieste (Italy)
tel. +39 040 671184
fax +39 040 671160
---------------- original message --------------
Message: 14
Date: Fri, 14 Mar 2014 05:25:42 -0700 (PDT)
From: tahaus <tah...@web.de>
To: r-help@r-project.org
Subject: [R] Random effects model with PLM: "System is computationally
singular"-Error?
Message-ID: <1394799942831-4686819.p...@n4.nabble.com>
Content-Type: text/plain; charset=us-ascii
Dear readers,
I am currently trying to estimate some panel data models in R using PLM
package. This includes the estimation of basic pooled, fixed effects and
random effects models. Therefore I make use of this code:
Now here's the problem:
Now here's the problem: I can without any problem estimate all models except
for the random effects model. After entering the "random"-formula, R
produces the following error:
First guesses:
- linear combinations in x?
A first guess would be that there are exact linear dependencies of the
exogenous variables in x. The data is balance sheet data and I would like to
explain the standard deviation (y) of a specific balance sheet position by
other balance sheet positions (or the ratio of the position and the balance
sheet sum). Of course, the variables in x are related to each other. For
example some of the ratios are calculated by dividing by the mean which is
also a separate independant variable. And the dependant variable, which is
the standard deviation, is also calculated by using this mean. But again:
There should be no EXACT correlation. But: If I exclude some of my exogenous
variables, the problem disappears, but I have to include them actually.
- problems with unbalanced panel data or NAs?
The data is unbalanced and there are NAs. Fixed effects output says: n=16,
T=18-40, N=455. Probably the unbalanced data or the NAs are the reason for
the error?
Traceback-Code:
Is there anybody who can give me a hint what this error does actually mean
and especially: how to solve the problem? How do I have to correct the code
in order to get results?
Thanks a lot!
Thomas
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