similar to: Count function calls

Dear all I am trying to fit Variable Coefficients Models on Unbalanced Panel Data. I managed to fit such models on balanced panel data (the example from the "plm" vignette), but I failed to do so on my real, unbalanced panel data. I can reproduce the error on a modified example from the vignette: > require(plm) > data("Hedonic") > Hed <- pvcm(mv ~ crim + zn + indus

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

Dear r-helpers, I am working with plm package. I am trying to fit a fixed effects (or a 'within') model of the form y_it = a_i + b_i*t + e_it, i.e. a model with an individual-specific intercept and an individual- specific slope. Does plm support this directly? Thanks in advance! Otto Kassi

Hello everybody, I have a problem with the the function PVCM in the plm package: I have an unbalanced panel with over 200 companies and 1 to 14 years for each company. I already excluded all NAs: > dataset<-na.omit(dataset) Now I'm trying to fit a variable coefficients model: > model_vc <- pvcm(v1 ~ lag(v2,1), model="within", data=dataset) This results in the

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

Hi all, I am quite new in the world of parallelization and I wonder if there is a way to increase the speed of creation of a parallel socket cluster. The time spend to include threads increase exponentially with the number of thread considered and I use of computer with two 8 cores CPU and thus showing a total of 32 threads in windows 7. Currently, I use the default parameters (type =

Dear R community, I do know, that an R function is constructing a copy of any object passed as argument into a function. I program on a larger S4 project for a package, and I arrived at a point where I have to think a little harder on implementation style (especially to spare users complex object handling). I have a function foo(), taking as input arguments two S4 objects of different class

Dear Sir/ Madam, I am writing about the panel data for my bachelor degree. I would really appreciate if You could help dealing with R functions. I am trying to estimate the panel data lm model with plm function. When i include 3dummy variables into the regression it dont appear in the sumarry of the model, but when i estimate a simple lm model it appears. Why is it so? What should i do to

Is there anyway with some experience in using armadillo in R C++ extensions? My problem is the following: I programmed a function in a header looking like #include <armadillo> inline arma::vec foo(input) { ... do something return an arma::vec object } compiling this via R CMD INSTALL packagename (PKG_CXXFLAGS = -I/folder/of/armadillo and armadillo_bits in my package) I get the

Hi all, I a semi-beginner with R and I am working with the plm package to examine a longitudinal dataset. Each individual in this dataset has a longitudinal weight for the probability that he or she remains in the sample. Unfortunately, I have not found an argument to use weights in the plm function? I tried ?weights=? like in standard lm or in nlme or lm4 but it does not work. I asked the

Hi,R 2.15.2 plm() function on Windows 7 when i perform a plm regression, i can't manage to obtain the intercept, but I need it.it gives me just the beta coefficient. my formula: fixed <- plm(deltaS ~ L1.deltaS + L2.deltaS, data=Mody_R, index=c("country_id", "date"), model="within") my output: Coefficients : Estimate Std. Error t-value Pr(>|t|)

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 all, I have estimated a random panel model using plm function. I have a question about the vector of resduals obtained with the object $residuals. example: data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, model="random", data = Produc, index = c("state","year")) res<-zz$residuals #

Hi all, what indicates the parameter theta in the summary of a random effect panel model estimated with the plm function? example: data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, model="random", data = Produc, index = c("state","year")) summary(zz) Effects: var std.dev

 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

dear readers---I struggled with how to do nice fixed-effects regressions in large economic samples for a while. Eventually, I realized that nlme is not really what I needed (too complex), and all I really wanted is the plm package. so, I thought I would share a quick example. ################ sample code to show fixed-effects models? in R # create a sample panel data set with firms and years

hey guys I have a panel data set that i want to perform some regressions on. I am using the /plm/ package. I defined a model in the following way: PWBw.pool <- plm(*PWB* ~ log(*I_EQON*) + log(*RD*) + ... + *PAGRI*, data = pfem, na.action=na.exclude, model="pooling") When i run this it gives the following error (the error remains when i use other model = "" specifications

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 >

Hello, I am trying to conduct a Hausman test to choose between FE estimators and RE estimators. When I try to run: library(plm) fixed <- plm(ROS ~ DiffClosenessC +ZZiele + AggSK + nRedundantStrecken + Degree + KantenGew + BetweennessC + SitzKappazitaet, data=Panel,index=c("id","time"),model="within") summary(fixed) fixef(fixed) random <-plm(ROS ~

Dear R Experts--- Sorry for all the questions yesterday and today. I am trying to use Yves Croissant's pgmm function in the plm package with Blundell-Bond moments. I have read the Blundell-Bond paper, and want to run the simplest model first, d[i,t] = a*d[i,t-1] + fixed[i] + u[i,t] . no third conditioning variables yet. the full set of moment conditions recommended for system-GMM,