search for: grunfeld

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

..., BUT then it does work properly when it is run a second time. (This is demonstrated in the code below. diff() does not work at all if it is run before lag(). It works properly if it is run after lag(). The problem occurs only inside a transform statement. lag() and diff() work correctly with Grunfeld.p$l.value <- lag(Grunfeld.p$value,1) Grunfeld.p$d.value <- diff(Grunfeld.p$value,1) Here is code to reproduce the error: library(plm) data(Grunfeld) Grunfeld.p <- pdata.frame(Grunfeld,c("firm","year")) Grunfeld.p <- transform(Grunfeld.p, l.value = lag(value,1)) h...

...balanced panel, I receive results which differ (even though being close). I guess I am missing something. Every comment pointing me to the right solution would be of great value to me. Also, comments on how to increase the efficiency of my function would help! Please find an example based on the Grunfeld data below. Thank you very much! Philipp Grueber ########################## library(MASS) getQ<-function(object,t.index,i.index){ t.ind<-object[,t.index] i.ind<-object[,i.index] nam<-unique(i.ind) tim<-unique(t.ind) n<-nrow(object) N<-length(nam) T<-length(tim)...

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 d...

...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 <- pgmm(dynformula(inv ~ value + capital, lag=list(1,1,1), log=T), data=Grunfeld, gmm.inst=~log(inv), lag.gmm=list(c(2,5)), model="twosteps") Error in solve.default(suml(A2)) : system is computationally singular: reciprocal condit...

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) mymod<-lme(inv~value+capital,data=Grunfeld,random=~1|firm,correlation=co rAR1(0,~year|firm)) Embarrassing as it may be, I can find the autoregressive parameter ('Phi', if I...

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$value,1)) tmp <- cbind(tmp, l.value = lag(Grunfeld.p$value,1)) ... When everything is in shape, convert it back to a pdata.frame for further analysis: Grunfeld.p <-...

...implementing this be particularly difficult? Where should one look in the source? - perform the regressions manually as suggested in ?Econometrics in R? [1]. But what would be the correct lme() syntax for a twoways RE model? For example, library("AER") library("plm") data("Grunfeld", package = "AER") pgr <- plm.data(Grunfeld, index = c("firm", "year")) gr_re <- plm(invest ~ value + capital, data = pgr, model = "random", effect="individual") gr_lme <- lme(invest ~ value + capital, Grunfeld, random=~1|firm) summa...

...; 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 = "AER") library("plm") gr <- subset(Grunfeld, firm %in% c("General Electric", "General Motors", "IBM")) pgr <- plm.data(gr, index = c("firm", "year")) > dim(pgr) [1] 60 5 ## the first example is actu...

I am working with panel data. I am using the plm package to do this. I would like to do be able to adjust for autocorrelation, as one does with glm models and correlation structures (eg corr=corARMA(q=4)) . In particular, I want to employ MA(4) error structure. Is there a way of doing this with the plm package? (Note: I do not really want to use the pggls function for various

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

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

...nice hearing from you again. I must restate a couple of my old hints, though ;^) 1) please always put the authors c/c, as we are not guaranteed to browse through the r-help every day 2) please provide reproducible examples. As example(pooltest) keeps working fine, as do some other cases I tried (Grunfeld data etc.), I don't know what the problem is but evidently your data are peculiar in exposing it. Sure the data numerosity must be enough, but maybe you have something which doesn't vary along some dimension, and gets dropped? or some groups which are all-NA? Do the "random" and...

...Roodman's tutorial, 2.6). This looks theoretically ok. Whether this is meaningful in applied practice is an issue I will discuss with the package maintainer. IMHO it is not, apart maybe for illustrative purposes, and it might well encourage bad habits (see the discussion about (not) fitting the Grunfeld model by GMM on this list, some weeks ago). 2) fitting the simple models Simplest possible model: AR(1) with individual effects x(i,t)= a*(x(i,t-1)) + bi + c This is what Ivo asked for in the first place. As the usual example is on data from the Arellano and Bond paper, available in package ...

...Roodman's tutorial, 2.6). This looks theoretically ok. Whether this is meaningful in applied practice is an issue I will discuss with the package maintainer. IMHO it is not, apart maybe for illustrative purposes, and it might well encourage bad habits (see the discussion about (not) fitting the Grunfeld model by GMM on this list, some weeks ago). 2) fitting the simple models Simplest possible model: AR(1) with individual effects   x(i,t)= a*(x(i,t-1)) + bi + c This is what Ivo asked for in the first place. As the usual example is on data from the Arellano and Bond paper, available in package ...