similar to: Quarterly data in PLM package

Does anyone know how to read SPSS .por (ie Portable) files? The foreign package only deals with SPSS .sav files and not with those with a "por" extension. Thanks, José Mr José Luis Iparraguirre D'Elia Senior Research Economist Economic Research Institute of Northern Ireland Floral Buildings 2 - 14 East Bridge Street Belfast BT1 3NQ Northern Ireland United Kingdom

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 >

Dear all, i just wonder if there´s a way to use a two column time index field in plm package. the manual says the following concerning data indexing: a character vector of length two containing the names of the individual and the time index, What would y´all do with a quarterly dataset that contains one column for the period and one for the year. Do I have to convert it to one single date

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 #

splm package is a r implemention of spatial panel data models. and the loglik paremeter is most important infomation for splm methods. but i found the loglik always been null ,it's craze to get right estimation in splm with null loglik. Any one knows the splm package and can get the right loglik ? please help me. thanks -- View this message in context:

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

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

 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

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

Dear all, Say I have the following dataset: > DF x y z [1] 1 1 1 [2] 2 2 2 [3] 3 3 NA [4] 4 NA 4 [5] NA 5 5 And I want to omit all the rows which have NA, but only in columns X and Y, so that I get: x y z 1 1 1 2 2 2 3 3 NA If I use na.omit(DF), I would delete the row for which z=NA, obtaining thus x y z 1 1 1 2 2 2

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

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

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

Dear R users, I am using plm packege in R to build my model, but from the result I can't quite figure out what it is... Can anyone tell me why? Am I missing something? R Results: *> ar1<-plm(formula=ADOP~lag(ADOP,1)+PE+WOR, + data=well, effect="time",model="within") > summary(ar1)* Oneway (time) effect Within Model Call: plm(formula = ADOP ~ lag(ADOP, 1) + PE

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 users, Consider the first two columns of a data frame like this: > z[,1:2] x y 1 1 1 2 2 2 3 3 3 4 1 4 Imagine that y represents the times that the value x happens in a population. But z is not exactly a frequency table, because in z we have x=1 twice. So, the x=1 in the first line and the x=1 in the fourth are not the same, differing according to a third variable in

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, 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 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 am working on unbalanced panel data and I can readily fit a "pooling" model using plm(), but not a "within" or "random" model. Reproducing the examples in vignette("plm") and in the AER package I encountered no such issues. ##unfortunately I cannot disclose the data, and it is too big anyway > dim(ibes.kld.exp.p[x.subs , ]) [1] 13189 34