Hello again.
This is really strange: I actually get the right numbers all the time. I tried now and got 
 
> 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. 
-1.8440000 -0.3908000  0.0388700  0.0005525  0.4153000  1.4920000 
 
Coefficients
              Estimate Std. Error z-value Pr(>|z|)    
(Intercept)   1.751448   0.180100  9.7249  < 2e-16 ***
log(wage)    -0.132986   0.054292 -2.4495  0.01431 *  
log(capital)  0.629621   0.018483 34.0648  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
Total Sum of Squares: 1853.6
Residual Sum of Squares: 386
Multiple R-squared: 0.79176

exactly as expected.
Which is your system? (please post the output of Sys.info) .... although I'd be surprised if this were system dependent!
 
You might also update the package to the last version if it isn't yet, although I can't remember bugs in this function since a very long time.
Thx for feedback
Best,
G.

________________________________

Da: Ruben de Bliek [mailto:rubendebliek@gmail.com] 
Inviato: venerdė 27 aprile 2012 16.28
A: Millo Giovanni
Cc: r-help@r-project.org
Oggetto: Re: [R] PLM package PGGLS strange behavior


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_Millo@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 <tel:%2B39%20040%20671184> 
	fax  +39 040 671160 <tel:%2B39%20040%20671160> 
	
	--------------- original message ---------------
	
	Message: 18
	Date: Thu, 26 Apr 2012 14:07:16 +0200
	From: Ruben de Bliek <rubendebliek@gmail.com>
	To: r-help@r-project.org
	Subject: [R] PLM package PGGLS strange behavior
	Message-ID:
	
	<CAMjFNLVpiDsSVemYf=cTX2fzLjQEKHOOBNctC8FC2cSRGKxD5A@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?
	
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