search for: pvcm

...nel 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 + chas + nox + rm + age + dis +rad + tax + ptratio + blacks + lstat, Hedonic, model = "within",index = "townid") Error in FUN(X[[1L]], ...) : insufficient number of observations > ##it fails for both FE and RE cases > Hed <- pvcm(mv ~ crim + zn +...

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", da...

Dear R-users, I have the following problem: I am running the function 'pvcm' from the 'plm' Panel Data package. Inside this function 'solve' is called and gives for a certain individual data series an exception because of singularity. I would like to know which individual data series causes this error. I tried to debug it, but this is truly painful, as...

...ems here: -When "Tempo" = 1 (the time index), the variable "Perc_Resg_Acum" gets 0. -I have some databases of the same kind (panel data structure) and some of then does not have any value on the variable "Promo" in the entire column. I'm modeling with the funcions pvcm() and lmList() (which are equivalent), but then, instead of giving 0 as coefficient for variable "promo", the function removes the entire column of the model and calculates the estimations. How can I do to consider the columns of zeros on the regression model and return a null coefficient...

...ex = c("firm", "year"), drop.index = TRUE, row.names = TRUE) # Two-way fixed effects model with constant beta m1 <- plm(wage ~ output,data=E,model="within",effect="twoways") summary(m1) # Random (individual) effects for both intercept and beta m2 <- pvcm(wage ~ output,data=E,model="random") summary(m2)

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

...e like Baltagi et al.'s (30 years x 47 states). This was just very loose talk to give you an idea of the issues involved: I strongly suggest you check out the literature. Turning back from philosophy to software, available methods for panels with heterogeneous slopes are the Swamy estimator in pvcm{plm} and the mixed models' methods in packages nlme and lme4. PS if you want to play with the Baltagi et al. data, > data(Cigar, package="Ecdat") > fm <- log(sales)~lag(log(sales),1)+log(price)+log(pimin)+log(ndi) ...and so on. (As you can see, the pooltest badly rejects)....