similar to: plm(...,"within","twoways") extremely slow on unbalanced panel

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 R users What would be the best way to approach estimating a panel regression with twoways random effects, on unbalanced data? Unfortunately, the "plm" package has no implementation of twoways random effects for unbalanced data. Currently I'm considering two approaches: - extend "plm" to cover this type of panel regression. (For the authors, cc'ed:) Would

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

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 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 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 =

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

Hi R-helpers, I have two questions I hope you could help me with them: In the plm package how can I calculate the R2 within, R2 between and R2 overall? Is there any special reason to not display these values? When using first differences do I need to have some special care with dummies (both year dummies and industry dummies)? (A friend who works with Stata told me that there is

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

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

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 have posted this problem before, but thought I try to explain it a bit better. I'm using the function plm to create a fixed effects model for panel data, my method is therefor "within" my effect is "twoways". My Data contains unbalanced Panels due to missing Values, but contains 309 observation for 11 variables (incl. response), with no missing Values. These 309

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 everyone, I’m working with a panel of firm/years observations. My panel not only is unbalanced but also have some gap’s in years. For example, firm 1 has 1999, 2000, 2001, 2004, 2005, firm 2 has 2000, 2001, 2003, 2005, and so on. I’m using the plm package and what I’m asking is how can I handle with this gap’s ? Thank you very much, Cecília Carmo Universidade de Aveiro

Dear R-user, a few weeks ago I consulted the list-serve with a similar question. However, my task changed a little but sufficiently to get lost again. So I would appreciate any help on the following issue. I use the plm package and work with firm-level data in a panel. I would like to eliminate all firms that do not fulfill the requirement of having an observation in every variable used for at

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

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

I am a student who is doing empirical work for his thesis and trying to switch to R. I am familiar with Stata, and at the moment I am trying to replicate some of my previous work. I have a large unbalanced panel data set, observations for different countries between 1970 and 2007. My dependent variable is an overdispersed count. So far I have used fixed-effects negative binomial regression,

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 everyone, I am doing panel data analysis using the 'plm' package. However, I have noticed that the plm() function reports a different value of R-squared from the R-squared of the lm() function with time-demeaned data. To be clear, I have tried to compute the within model both manually (run an OLS regression with time-demeaned data using lm()) and by using plm(). The two methods give me