similar to: lme with corAR1 errors - can't find AR coefficient in output

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

Hello R Developers, This is my first foray into using c-code with R, so please forgive my foolishness. I had a look at the archives and did not find anything on this, so hopefully I am not doubling up. I have tried to use R cmd to create an object file from the odesolve dynload example. I am using windows and have just installed rtools, and have the latest version of stable R (2..7.2). This is

I have been trying to make use of the odesolve library on my university's Linux grid - currently R version 2.0.1 is installed and the system runs 64-bit Scientific Linux based on Redhat. I cannot seem to get lsoda working when I define the model as a shared C library. For example, the following snippet uses the mymod.c example bundled with the package: ### START rm(list=ls())

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

On Sun, Mar 18, 2012 at 09:52:12PM -0700, Gregory Szorc wrote: > The automatic generation of the Python ctypes interfaces using the Clang > Python bindings is pretty friggin cool! A nice side effect is that everything is added to the interface. So it is easy to add a small proxy over the lib that shows which parts of the llvm-c API that is exercised by the tests. (have that in my bindings)

I've been working on a wrapper for LLVM's C++ API to use from Objective-C for a scripting language I'm working on. I currently have an issue with passing arguments to a function that takes a struct argument. typedef struct _test_struct { int x; int y; } test_struct; id testLLVMStructFuncCall(test_struct x) { NSLog(@"%d %d",x.x,x.y); return N(x.x + x.y); }

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)

Several bugs (no solutions, yet). These might be well known. 1) If one does, e.g., mymod <- lm(y ~ x); formula(mymod) then one does not get back the formula (one gets, Error: invalid formula) 2) if x is of mode numeric, then the model formula mymod <- lm(y ~ x + x^2) is not processed as S would do it. The model is fit ignoring the x^2 term, however mymod$call includes the x^2 term.

Several bugs (no solutions, yet). These might be well known. 1) If one does, e.g., mymod <- lm(y ~ x); formula(mymod) then one does not get back the formula (one gets, Error: invalid formula) 2) if x is of mode numeric, then the model formula mymod <- lm(y ~ x + x^2) is not processed as S would do it. The model is fit ignoring the x^2 term, however mymod$call includes the x^2 term.

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

Hello. Not an easy question at all, and it has little to do with software, alas! Veeeeeery loosely speaking: if the homogeneity hypothesis is rejected, then, depending on data availability, you may still be able to treat the data like a panel by: a) ignoring the results of the poolability test b) allowing the coefficients to vary. Of course, a) requires some courage while b) requires more

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 =

The cautions people have given about starting values are worth heeding. That nlxb() does well in many cases is useful, but not foolproof. And John Fox has shown that the problem can be tackled very simply too. Best, JN On 2023-08-19 18:42, Paul Bernal wrote: > Thank you so much Dr. Nash, I truly appreciate your kind and valuable contribution. > > Cheers, > Paul > > El El

It is frustrating to see the labels I want in the dimensions of a list but not be able to extract those labels into titles for plots generated from component objects. If someone could set me straight, I would appreciate it. For your amusement, I have provided an example of the Byzantine code I am currently using to avoid loops: # Simulate ANOVA type test data sex<-c(rep(1,8),rep(0,8))

Dear listserv members, In Croissant (2008) “Panel Data Econometrics in R: The plm Package” the authors seem to indicate that the nlme package for R cannot correctly handle unbalanced panel data: “Moreover, economic panel datasets often happen to be unbalanced (i.e., they have a different number of observations between groups), which case needs some adaptation to the methods and is not

Thank you so much Dr. Nash, I truly appreciate your kind and valuable contribution. Cheers, Paul El El s?b, 19 de ago. de 2023 a la(s) 3:35 p. m., J C Nash < profjcnash at gmail.com> escribi?: > Why bother. nlsr can find a solution from very crude start. > > Mixture <- c(17, 14, 5, 1, 11, 2, 16, 7, 19, 23, 20, 6, 13, 21, 3, 18, 15, > 26, 8, 22) > x1 <- c(69.98, 72.5,

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 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

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

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 =