Thank you Millo. I was a little confused by the random versus pooling
nomenclature used in PLM, thank you for clearing that up. I still have the
issue of not receiving the proper coefficient estimates for the example in
the paper though. My output is posted below; the estimates are
substantially different from the ones posted on page 20. My R version is
2.14.2.
library(plm)
> data("EmplUK", package="plm")
> zz <- pggls(log(emp)~log(wage)+log(capital),data=EmplUK, model="random")
Warning message:
'random' argument to pggls() has been renamed as 'pooling'
> summary(zz)
Random effects model
Call:
pggls(formula = log(emp) ~ log(wage) + log(capital), data = EmplUK,
model = "random")
Unbalanced Panel: n=140, T=7-9, N=1031
Residuals
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1.80700 -0.36550 0.06181 0.03230 0.44280 1.58700
Coefficients
Estimate Std. Error z-value Pr(>|z|)
(Intercept) 2.023480 0.158468 12.7690 < 2.2e-16 ***
log(wage) -0.232329 0.048001 -4.8401 1.298e-06 ***
log(capital) 0.610484 0.017434 35.0174 < 2.2e-16 ***
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Total Sum of Squares: 1853.6
Residual Sum of Squares: 402.55
Multiple R-squared: 0.78283
2012/4/27 Millo Giovanni <giovanni_mi...@generali.com>
> Hello. No "strange behaviour" here, just a warning.
>
> There is a difference between an "error" and a "warning", and between an
> argument and a model. In this specific case, the warning is just there
> to remind you that, as stated, 'the "random" **argument** has been
> renamed to "pooling" ' (emphasis mine).
>
> Both still work, but the former is deprecated. The estimator you get is
> the same (a GGLS, or "Parks estimator"), everything works the way it
> should and you can trust the numbers that come out, provided the
> specification is clear to you and it is what you wanted (which for
> instance is not entirely clear from your email).
>
> The model underlying the General Feasible GLS estimator (GGLS) does not
> really have "random effects"; therefore, after having initially named
> the model without FEs "random" by contrast to the Fixed Effects GLS a la
> Kiefer (1980), we later considered the denomination as inappropriate and
> changed it to "pooling" which in effect it is: a pooled model with no
> proper individual effects but a general error covariance structure. You
> can get a better understanding of the specification if you read the
> cited reference carefully (or even better, Wooldridge as referenced
> therein).
>
> Lastly, it is not clear what you mean by "the right estimates": the
> numbers I get by using either 'model="pooling"' or ' model="random"' on
> the given example are exactly those in the JSS paper. If on your system
> you get anything else, I'll be grateful for a reproducible report, as
> asked for in the posting guide.
>
> PS if by chance you are just mistaking "General Feasible GLS" with
> "Random Effects by GLS", then you should use 'plm(yourformula, yourdata,
> model="random")' instead, and you'll get the standard RE model.
>
> Best wishes,
> Giovanni
>
> Giovanni Millo, PhD
> Research Dept.,
> Assicurazioni Generali SpA
> Via Machiavelli 4,
> 34132 Trieste (Italy)
> tel. +39 040 671184
> fax +39 040 671160
>
> --------------- original message ---------------
>
> Message: 18
> Date: Thu, 26 Apr 2012 14:07:16 +0200
> From: Ruben de Bliek <rubendebl...@gmail.com>
> To: r-help@r-project.org
> Subject: [R] PLM package PGGLS strange behavior
> Message-ID:
>
> <CAMjFNLVpiDsSVemYf=ctx2fzljqekhoobnctc8fc2csrgkx...@mail.gmail.com>
> Content-Type: text/plain
>
> When using the PLM package (version 1.2-8), I encounter the probem that
> calling the FGLS estimator evokes strange behavior, when choosing the
> "random" effects model. After calling the PGGLS function to estimate
> FGLS,
> PLM gives me a warning, stating that the "random" model has been
> replaced
> with the "pooling" model. I would, however, really like to estimate the
> random model instead. For me, the problem is reproducable using one of
> the
> examples from the PLM Jstatsoft article "Panel Data Econometrics in R:
> The
> plm package" (pp.19-20):
>
> data("EmplUK", package="plm")
> zz <- pggls(log(emp)~log(wage)+log(capital),data=EmplUK, model="random")
> summary(zz)
>
> Which for me results in the following warning:
>
> WARNING: Warning: 'random' argument to pggls() has been renamed as
> 'pooling'
>
> It then proceeds with estimating a pooled model. I've checked if PLM by
> any
> chance does produce the right coefficient estimates, but the numbers do
> not
> add up when compared to the estimates in the article. This problem
> perists
> for any dataset I use. Any thoughts?
>
> ------------------ end original message ---------------------
>
>
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>
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