similar to: Problems when using lag() in plm package

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 =

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

Hello, I am trying to use the plm package for panel econometrics. I am just trying to get started and load my data. It seems from most of the sample documentation that I need to use the pdata.frame function to get my data loaded. However, even after installing the "plm" package, my R installation cannot find the function. I am trying to follow the example in plmEN.pdf (

Dear Sir, Thank you for accepting my request for registration on this site. I am trying to solve panel data problems using plm package , but while suing random effect model i am getting following messege saying " Warning message:In sqrt(sigma2) : NaNs produced " In some other cases i am getting message saying where TSS = NA , that I am not understanding I am sending you my code along

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

Dear Kailas Gokhale, The negative individual variance is not a problem with your code or plm. It a property of your data. Please check the posts of Giovanni Millo on this topic: [R] R: plm random effect: the estimated variance of the individual effect is negative Millo Giovanni Giovanni_Millo at Generali.com Sat Jan 5 10:10:01 CET 2013 You can find the posts in the archive by rseek.org.

Hi all, I installed all required packages for plm, but pdata.frame is not a recognized function on R to start with. I followed the first page of "introduction to plm" by Croissant&Millo, Here are the messages: * > library(plm) Loading required package: kinship Loading required package: survival Loading required package: splines Loading required package: nlme Loading required

Hi everyone. I have two questions. I’ve found some other questions and answers similar to these but they didn’t solve my problem. I’m working with a panel of firm/years observations (see my reproducible example). I’m using the plm package. My panel not only is unbalanced but also have some gap’s in years. #reproducible example

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

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

Hello everyone, I am working with plm package and I have problem with random and within models, which are giving errors which says "empty model". However, the model is not empty. In the source code for plm.fit, where the error originates it says something like (writing from the top of my head...) X <- model.matrix(formula,data, lhs=1,...) if (ncol(X) == 0) stop("empty

In the course of some work I have been doing for Revolution Analytics I have had the necessity of modifying the plm function so that it would not die halfway through fitting. In particular, I was able to more than halve the runtime (for my particular run) and improve its memory usage with three small modifications: 1.) Replacing throughout apply(X, 2, mean) with colMeans, similarly with

Hello, although I searched for a solution related to my problem I didn?t find one, yet. My skills in R aren?t very large, however. For my Diploma thesis I need to run a GMM estimation on a dynamic panel model using the "pgmm" - function in the plm-Package. The model I want to estimate is: "Y(t) = Y(t-1) + X1(t) + X2(t) + X3(t)" . There are no "normal" instruments

Hi R-helpers I want a function that performs the opposite of lag() with panel data. I have transformed my data before with pdata.frame(mydata, index=c("groupindex", “timeindex")) And then I’ve done lag(mydata, -1) but it doesn’t work. The error message was: Error in rep(1, ak) : invalid ''times'' argument Thank you in advance, Cecília Carmo

Good day fellow R users: I have routinely received the following message when attempting to estimate a GMM model for a somewhat square panel (N = 20, T = 9-27, Obs = 338) using the pgmm function in the plm package: Error in function (..., deparse.level = 1) : number of rows of matrices must match (see arg 2) So far, I am not wedded to a particular GMM model but what I have used thus far is

Hi all, I am hardly an expert, so I expect that this code is not the easiest/ most efficient way of getting where I want. Any suggestions in that direction would also be helpful. I am working on panel analysis with five imputed datasets, generated by Amelia. To do panel analysis, it seemed that the plm package was the best, providing a convenient wrapper for fixed and random effects

Dear LLVM development team, working with the LLVM gold plugin, I have encountered an unexpected behaviour when the option --allow-multiple-definition (or -z muldefs) is specified for the linker. Let's suppose the following scenario with four simple source files: ----- main.c ------ #include "unit.h" int main() { only_in_unit1(); only_in_unit2(); return

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

Hello list, I'm following the paper (http://www.jstatsoft.org/v27/i02/paper) on how to use "plm" to run panel regressions, and am having trouble with what I believe should be something very basic. When I run the command (p.9 in the paper): R> dynformula(emp~wage+capital,log=list(capital=FALSE,TRUE),lag=list(emp=2,c(2,3)),diff=list(FALSE,capital=TRUE)) I see: emp ~ wage +

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)