Dear Carlos, please refrain from posting the same question umpteen times. Please consider that code is hard to read and people might not have the time to run your simulation etc. etc..
As I told you privately in response to your message on 18/1, > Re: generating correlated effects, I tried this only once, but I > didn't get it right. Simulations using this are, e.g., Hansen (2007) > http://faculty.chicagobooth.edu/christian.hansen/research/panel_cov_t. > pdf > and Drukker (2003) > http://ideas.repec.org/a/tsj/stataj/v3y2003i2p168-177.html > I suggest you take inspiration from what they did. so the references are here for the possible benefit (?) of the list too. In the meantime I looked at your code and it looks more or less OK to me. I would have generated the correlated effects simply as x_i=something random + gamma*u_i, but your Choleski approach is much better. Sorry but a thorough check is beyond my time availability now. arima.sim() may be a good way to go, I can't tell. The simplest way is just to use loops: generating (in pseudo-R) > w <- runif(n, <parameters>) > x[1] <- <random> and then > for(i in 2:n) x[i] <- 0.5*x[i-1]+w[i] PS remember to generate a sufficiently long sequence of x's *before* those in your sample, in order to make the initial conditions irrelevant. Let me suggest Ch. 7.1 of Kleiber and Zeileis, "Applied Econometrics with R" for a nice and organized approach to (econometric) simulations in R. Other than this, it's just basic R, trying it out and seeing if it works. Best wishes, Giovanni ----------- original message ------------------- Message: 18 Date: Mon, 24 Jan 2011 14:38:43 +0000 From: car...@sapo.pt To: r-help <r-help@r-project.org> Subject: [R] How to simulate a variable Xt=Wit+0.5Wit-1 with Wit~U(0,2) Message-ID: <20110124143843.10742vvdq8rvq...@mail.sapo.pt> Content-Type: text/plain; charset=ISO-8859-15; DelSp="Yes"; format="flowed" Dear all I simulate a panel data: n <- 10 t <- 5 nt <- n*t pData <- data.frame(id = rep(paste("JohnDoe", 1:n, sep = "."), each = t),time = rep(1981:1985, n)) rho <-0.99#simulate alphai corelated with the xi print(rho) alphai <- rnorm(n,mean=0,sd=1)#alphai simulation x<- as.matrix(rnorm(nt,1))#xi simulation akro <- kronecker(alphai ,matrix(1,t,1))#kronecker of alphai cormat<-matrix(c(1,rho,rho,1),nrow=2,ncol=2)#correlation matrix cormat.chold <- chol(cormat)#choleski transformation of correlation matrix akrox <- cbind(akro,x) ax <- akrox%*%cormat.chold ai <- as.matrix(ax[,1]) pData$alphai<-as.vector(ai) xcorr <- as.matrix(ax[,2:(1+ncol(x))]) pData$xcorrei<-as.vector(xcorr) library(plm) pData<-plm.data(pData, index = c("id", "time")) pData But now i need a variable Xt=Wit+0.5Wit-1 with Wit~U(0,2), the code i Try to use is: for (i in 1:n) { p <- i*t m <- (i-1)*t+1 for (j in m:p){ xt<-arima.sim(n=nt, list(ar=c(0.5))) } } Is this the correct way to simulate the AR(1), without the assumption Wit~U(0,2)? How i simulate the variable with the assumption Wit~U(0,2), and put it in my dataframe correctly? Comments and tips are welcome, regards Carlos Br?s Statistics Portugal-INE ---------- end original message ------------- Giovanni Millo Research Dept., Assicurazioni Generali SpA Via Machiavelli 4, 34132 Trieste (Italy) tel. +39 040 671184 fax +39 040 671160 Ai sensi del D.Lgs. 196/2003 si precisa che le informazi...{{dropped:13}} ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
