similar to: Adjusting for autocorrelation in a panel model

Dear R users, I have an unbalanced panel with an average of I=100 individuals and a total of T=1370 time intervals, i.e. T>>I. So far, I have been using the plm package. I wish to estimate a FE model like: res<-plm(x~c+v, data=pdata_frame, effect="twoways", model="within", na.action=na.omit) ?where c varies over i and t, and v represents an exogenous impact on x

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

Taking the Grunfeld data, which is built-in in R, for example, (1)How can I construct a dataset (or dataframe) that consists of the data of all firms in 1951? (2)How can I calculate the average capital in each form over the period 1951-1954? What I can imagine is to categorize the data by firm, and then select the data between 1951 and 1954 for each firm, but how can I do it? Thanks, Miao

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

Dear List, I am using the output of a ML estimation on a random effects model with first-order autocorrelation to make a further conditional test. My model is much like this (which reproduces the method on the famous Grunfeld data, for the econometricians out there it is Table 5.2 in Baltagi): library(Ecdat) library(nlme) data(Grunfeld)

Hello all Getting a segfault when I try to start wow.exe Code: dmk at hermes:~/.wine/drive_c/Program Files/World of Warcraft$ wine Wow.exe -opengl Segmentation fault dmk at hermes:~$ wine --version wine-0.9.61 This is a fresh install of xubuntu and wine, i'm not all that ubuntu savvy, so i could be missing some obvious stuff. Athlon64 xubuntu 8.04 (2.6.24-16-generic) nVidia 8800GTS

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 =

Hi everyone and thank you for the help you could give me. My data is in a spreadsheet. The 1st column identifies the firm (with the fiscal number), the columns 2 to 11 have the variable value for 11 years. I have many variables (files like this). Each file has about 40.000 firms (rows). I transformed all the files in txt files. The data is a panel data, like this: firm revenu2007 revenue2006

Hello, I have estimated the coefficients for my model using the 'pggls' function from the 'plm' package. Now I want to see the relative influence of those X's. How can some please tell me how to standardize those my results in R? Thank you! -- View this message in context: http://r.789695.n4.nabble.com/Standardize-GLS-coefficients-in-R-tp4671371.html Sent from the R help

On 07/02/2015 05:43 PM, David Keaton wrote: > On 07/02/2015 05:30 PM, Philip Reames wrote: >> >> >> On 07/02/2015 04:44 PM, David Keaton wrote: >>> On 07/02/2015 03:17 AM, Kuperstein, Michael M wrote: >>>> You want to redefine ["won't break the program"], by specifying a new >>>> abstract machine, which is >>>> more

Hi, my data set is data.frame(id, yr, y, l, e, k). I would like to estimate Lee and Schmidts (1993, OUP) model in R. My colleague wrote SAS code as follows: ** procedures for creating dummy variables are omitted ** ** di# and dt# are dummy variables for industry and time ** data a2; merge a1 a2 a; by id yr; proc sysnlin maxit=100 outest=beta2; endogenous y; exogenous l e k

On 07/02/2015 04:44 PM, David Keaton wrote: > On 07/02/2015 03:17 AM, Kuperstein, Michael M wrote: >> You want to redefine ["won't break the program"], by specifying a new >> abstract machine, which is >> more conservative than standard C/C++. The proper way to do that would, >> I believe, be to work towards setting up a working group within the >>

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

Greetings! Being a Perl hacker for some time, and wanting to leverage what R provides, I've been trying to work with Statistics::R and RSPerl. The former has a race condition that breeds some unreliability and the latter seems to have issues all around, and neither has been updated in some time. Are these projects are abandoned, or is there some effort currently being undertaken to

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

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)

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 =

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

I apologize if this is redundant. I've been Googling, searching the archive and reading the help all morning and I am not getting closer to my goal. I have a series of data( xi, yi). It is not evenly sampled and it is messy (meaning that there is a lot of scatter in the data). I want to fit a normal distribution (i.e. a gaussian) to the data in order to find the center. (The data

Hi, my data set is data.frame(id, yr, y, l, e, k). I would like to estimate Lee and Schmidts (1993, OUP) model in R. My colleague wrote SAS code as follows: ** procedures for creating dummy variables are omitted ** ** di# and dt# are dummy variables for industry and time ** data a2; merge a1 a2 a; by id yr; proc sysnlin maxit=100 outest=beta2; endogenous y; exogenous l e k