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Dear Hao-pang, it is impossible to really tell the problem without a reproducible example. Just guessing: this looks like you have too many regressors. In GMM, lags of variables are used as instruments, so you might have more regressors than observations. Try reducing the 'lag' argument (which, by default, uses all lags available). Of course, the first observation to make would be that

Dear R help, I use the package plm the function plm() to analyse a panel data and estimate a fixeffect model. I use the code as follow : fe <- plm(y~x+z, data, model = "within") I want to use bootstrap to estimate standard error I consult the paper plm.pdf , but I can't find any answer. Can I estimate variance with bootstrap? Thanks, Helen

Hello Cecilia, nice hearing from you again. I must restate a couple of my old hints, though ;^) 1) please always put the authors c/c, as we are not guaranteed to browse through the r-help every day 2) please provide reproducible examples. As example(pooltest) keeps working fine, as do some other cases I tried (Grunfeld data etc.), I don't know what the problem is but evidently your data are

Dear Limin, might be just about anything. Could you please provide a reproducible example? Best, Giovanni ----------------- Original message ---------------------- Message: 51 Date: Mon, 17 May 2010 10:36:03 +0800 (CST) From: ??? <dlmsos at 163.com> To: r-help at r-project.org Subject: [R] pgmm function Message-ID: <b2cba0.35fc.128a41e3684.Coremail.dlmsos at 163.com> Content-Type:

Matteo, I fully agree with David: please read the posting guide. Anyway, the error message says it all: "the estimated variance of the individual effect is negative". See e.g. the "basic panel" chapter (10 or 11) in Wooldridge's "Econometric Analysis of XS and Panel Data" to understand why this may happen. Stata's behaviour is (as far as I remember) to

Dear list, dear Cecilia and Daniel, sorry for coming in ten days late, I've been very busy lately so I came across this email only today. This is just to make some points clearer re: fixed effects and r2 in package 'plm', to both you and the list. In particular, to make you aware of some additional features. Please see my comments below, with '##'. Best, Giovanni

Hello. Just a quick follow-up, for other 'plm' users on the list: - the problem turned out to be logs of zero values hidden in the big dataset - trying log(xx) would not reveal the problem, because log(0)=-Inf is a valid result in log() while it is an invalid input to plm() --> it is always advisable to try lm(yourformula, yourdata) as a first diagnostic check when plm(yourformula,

Hello. Not an easy question at all, and it has little to do with software, alas! Veeeeeery loosely speaking: if the homogeneity hypothesis is rejected, then, depending on data availability, you may still be able to treat the data like a panel by: a) ignoring the results of the poolability test b) allowing the coefficients to vary. Of course, a) requires some courage while b) requires more

Dear Carlos, please refrain from posting the same question umpteen times. Please consider that code is hard to read and people might not have the time to run your simulation etc. etc.. As I told you privately in response to your message on 18/1, > Re: generating correlated effects, I tried this only once, but I > didn't get it right. Simulations using this are, e.g., Hansen (2007)

Dear Philipp, this is just a tentative answer because debugging is really not possible without a reproducible example (or, at a very bare minimum, the output from traceback()). Anyway, thank you for reporting this interesting numerical issue; I'll try to replicate some similar behaviour on a similarly dimensioned artificial dataset when I have some time (which might not be soon). As for now,

Hello. This is to get you started with data.frames, next time please - read the posting guide - see the documentation, especially the builtin "R data import/export" manual form the help menu ## begin R examples, paste into console ## data(mtcars) # builtin database class(mtcars) # what it is mtcars # print it out... head(mtcars) # ...better: see first rows fm <- mpg~hp+wt

Thomas, may I also suggest, from the Documentation>Contributed section of CRAN, "Econometrics in R" by Grant Farnsworth http://cran.at.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf (see the chapter on Time series) and, in case you can read Italian, "Analisi delle serie storiche con R" by Vito Ricci http://cran.at.r-project.org/doc/contrib/Ricci-ts-italian.pdf

Dear List, I am using the output of a ML estimation on a random effects model with first-order autocorrelation to make a further conditional test. My model is much like this (which reproduces the method on the famous Grunfeld data, for the econometricians out there it is Table 5.2 in Baltagi): library(Ecdat) library(nlme) data(Grunfeld)

Dear Ivo, dear list, (see: Message: 70 Date: Thu, 26 Mar 2009 21:39:19 +0000 From: ivowel at gmail.com Subject: [R] pgmm (Blundell-Bond) sample needed) I think I finally figured out how to replicate your supersimple GMM example with pgmm() so as to get the very same results as Stata. Having no other regressors in the formula initially drove me crazy. This was a case where simpler models are

Dear R help, I use the package plm the function plm() to analyse a panel data and estimate a fixeffect model. I use the code as follow : fe <- plm(y~x+z, data, model = "within") But I had a error message Error in model.frame.default(form, ...) : object is not a matrix I don't what's wrong. Beacuse others use the same codes and data,the code can

Dear List, I just installed R 2.6.1 (on Win2K) and I get a strange error in functions min() and max(): > min(1:3) Errore in .Internal(min(..., na.rm = na.rm)) : nessuna funzione interna "min" which, as you may have guessed, means 'no internal function "min" '. The same happens for max(). Maybe this is a bug in the new release, or maybe I'm missing

Dear all, I just went through the process of installing R on an eeePC 900 running Linux. As a Windows useR utterly ignorant about Linux, I'd never have done it without reading your posts and the R Wiki, so first of all: thank you! Next, taking up your thread from some weeks ago, I thought this could be useful for somebody else too, so here's what I did: 1) I followed wolfgang's

I was experiencing the same problems as detailed below. I found that my OpenLDAP Samba (samba.schema) in /etc/ldap/schema was out of date; I have now replaced it with the latest version from the Samba source (3.0.23d). The correct number of domain groups are now reported with 'net rpc info' and the groups are visible from the Windows XP dialog boxes and USRMGR. Regards, Jon

Dear List, the advent of multicore machines in the consumer segment makes me wonder whether it would, at least in principle, be possible to divide a computational task into more slave R processes running on the different cores of the same processor, more or less in the way package SNOW would do on a cluster. I am thinking of simple 'embarassingly parallel' problems, just like inverting

Dear R user I would like to run some panel regressions with R. Therefore I want to use fixed effect model. I consulted nlme package pdf. Unfortunately I couldn't find anything clear example about fixed effect model. Is there any defined function to calculate panel data under fixed effect model. I need some suggestions or examples. Thanks Best Helen Chen -- View this message in context: