similar to: panel regression with twoways random effects, on unbalanced data?

Dear R users I get different F-statistic results for a "within" model, when using "time" or "twoways" effects in plm() [1] and when manually specifying the time control dummies [2]. [1] vignette("plm") [2] http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf Two examples below: library("AER") data("Grunfeld", package =

Dear R users, I wish to manually demean a panel over time and entities. I tried to code the Wansbeek and Kapteyn (1989) transformation (from Baltagi's book Ch. 9). As a benchmark I use both the pmodel.response() and model.matrix() functions in package plm and the results from using dummy variables. As far as I understood the transformation (Ch.3), Q%*%y (with y being the dependent variable)

Hello Giovanni I made a minor modification to your function, which now allows to compute the within R-sq in Twoways Within models (see below). However I ran into an issue that I have already encountered before: whenever I try to fit Twoways Within models on my unbalanced data, the process is strangely slow and I usually terminate it either after ~15min or when my CPU hits 100C. This is similar to

Hello everyone, I have a peoblem to create the twoways effect in the plm package. when i try to create the following dsn1<-plm(lnQ~lnC+lnL+lnM+lnE+eco+RD,data=newdata,effect="twoways",model="within") i have this error: Error in rep.int(c(1, numeric(n)), n - 1L) : negative length vectors are not allowed and to be honest i have no idea what does it mean!! can someone

Hi, How could I see the source code of functions in an R package? If we type ?function_name , we will see documentations of the function_name. If we type function_name, is what returns just the source code? Could we just save it in an .R file and modify as we want? However, it seems that sometimes the source code is hidden (or stored elsewhere?) As an example, could we see the source

I have an unexpected result with the functions lag() and diff() in the plm (panel data) package when used with transform(). These plm-specific functions are supposed to generate lags and first differences within each panel. lag() does not work properly the first time (it reproduces the same series--this is a common time series pitfall), BUT then it does work properly when it is run a second

Dear R users, I have an unbalanced panel with an average of I=100 individuals and a total of T=1370 time intervals, i.e. T>>I. So far, I have been using the plm package. I wish to estimate a FE model like: res<-plm(x~c+v, data=pdata_frame, effect="twoways", model="within", na.action=na.omit) ?where c varies over i and t, and v represents an exogenous impact on x

I am using it on a big dataset. The estimation was done in hours. But summary() takes forever. Any suggestion is greatly appreciated! [[alternative HTML version deleted]]

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

Hi list, has anyone succeeded in using pgmm() on any dataset besides Arellano/Bond's EmplUK, as shown in the vignette? Whatever I try, I eventually get a runtime error because of a singular matrix at various points in pgmm.diff() (which gets called by pgmm()). For example, when estimating a "dynamic" version of the Grunfeld data: data(Grunfeld, package="Ecdat") grun

Hi, I am trying to estimate a two-way model with both individual and time fixed effects. I am using plm with "twoways" specification. plm(as.integer(yvar) ~ xvar, index = c("id", "time"), model="within", data=dataset, effect = "twoways") But I get keep getting the following message and I don't know what to do about it, because I don't

Hello, So I am afraid I am having a recurring problem that I just can't figure out. I am using the plm package to conduct a panel analysis - although I am not sure if the problem is arising as a result of the plm package or something more general. I am trying to run a fixed effects model with effects over time and individual. The model has various lags, and the problem is that these lags do

 Hi R users:  I am using the plm package for linear panel data analysis but encountered the following message when I try plm function to estimate an random model with individual effect.  data.re.ind <- plm(X.RETURN. ~ IOB + IOBS,data=E,model="random",effect = "individual")  Error in swar(object, data, effect) :  the estimated variance of the individual effect is negative

Hello to all on the list, I'm trying to estimate a fixed effects model from a large (unbalanced) panel data set. I have no problems when using only an individual effect or only a time effect, but I get an error message when I try for a "twoways" effect. Here is some of the code: paneldata27 is the entire panel data set: > dim(paneldata27) [1] 1178831 8 >

Hi, I am trying to estimate a two-way model with both individual and time fixed effects. I am using plm with "twoways" specification. plm(as.integer(yvar) ~ xvar, index = c("id", "time"), model="within", data=dataset, effect = "twoways") But I get keep getting the following message and I don't know what to do about it, because I don't

Hi, I am trying to run a panel regression using 88 observations and 9 variables. In-built Hausman Test did not work, then I found a code for auxiliary regression method for the Hausman test. The panel models are: fe=plm(gd ~ l+g+o+c+g1+h+n+r, model = "within", data = new.frame,index = c("id")) re=plm(gd ~ l+g+o+c+g1+h+n+r, model = "random", data = new.frame,index =

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,

hi, i am writing my master thesis and i am dealing with 146474 observations (panel data), i have just learned the R so i am a beginner!! i am trying to use the "plm" package and i have a duplication problem; i have written the following commands to read my data and create my model >dsn<-plm.data(ds, c("stno", "year")) ds=name of my data, stno=individual

Hello, I am using {plm} to estimate panel models. I want to estimate a model that includes fixed effects for time and individual, but has a random individual effect for the coefficient on the independent variable. That is, I would like to estimate the model: Y_it = a_i + a_t + B_i * X_it + e_it Where i denotes individuals, t denotes time, X is my independent variable, and B (beta) is the

I didn't see you got an answer posted to this question: You can't modify a pdata.frame object. Your transforms turn it back to a normal data frame and diff and lag won't work as expected. Try: Grunfeld.p <- pdata.frame(Grunfeld,c("firm","year")) tmp <- transform(Grunfeld.p, d.value = diff(Grunfeld.p$value,1)) tmp <- cbind(tmp, l.value =