Dear all, is there any package/function available which simulates a co-integrating VAR model once the model parameters are input over some arbitrary horizon? Please let me know anyone aware of that. Thanks -- View this message in context: http://n4.nabble.com/Simulation-of-VAR-tp1693295p1693295.html Sent from the R help mailing list archive at Nabble.com.
Dear Ron, have you had a look at the package dse? Here, ARMA models can be specified and simulated. The only exercise left for you, is to transform the VECM coefficients into their level-VAR values. Best, Bernhard |> -----Original Message----- |> From: r-help-bounces at r-project.org |> [mailto:r-help-bounces at r-project.org] On Behalf Of Ron_M |> Sent: Saturday, March 27, 2010 12:14 PM |> To: r-help at r-project.org |> Subject: [R] Simulation of VAR |> |> |> Dear all, is there any package/function available which simulates a |> co-integrating VAR model once the model parameters are |> input over some |> arbitrary horizon? Please let me know anyone aware of that. |> |> Thanks |> -- |> View this message in context: |> http://n4.nabble.com/Simulation-of-VAR-tp1693295p1693295.html |> Sent from the R help mailing list archive at Nabble.com. |> |> ______________________________________________ |> R-help at 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. |> ***************************************************************** Confidentiality Note: The information contained in this ...{{dropped:10}}
Yes I looked into "dse" package. Here I have implemented two approach for simulation like following : library(dse) A1 <- matrix(rnorm(16),4) A2 <- matrix(rnorm(16),4) mu <- rnorm(4) sigma <- matrix(c(0.006594712, 0.006467731, -0.000254914, 0.005939934, 0.006467731, 0.006654184, -0.000384097, 0.005658247, -0.000254914, -0.000384097, 0.000310294, 4.34141E-05, 0.005939934, 0.005658247, 4.34141E-05, 0.00574024), 4) initial.val <- matrix(c(-0.2347096, -0.1803612, -0.2780356, -0.2154427 , 3.740364, 3.757908, 3.50216 , 3.57783), 2) ##### My approach res <- matrix(NA, 4,4); res[c(1,2),] <- initial.val library(mnormt); shocks <- rmnorm(2, rep(0,4), sigma) for (i in 1:2) { res[i+2,] <- mu + A1%*%res[i+2-1,] + A2%*%res[i+2-2,] + shocks[i,] } res ##### dse approach temp1 <- matrix(t(cbind(diag(4), A1, A2)), ncol = 4, byrow = TRUE) model <- ARMA(A=array(temp1, c(3,4,4)), B=diag(4), TREND=mu) simulate(model, y0=initial.val, sampleT=2, noise=shocks) Ideally last two rows of "res" and simulate() should be exactly same. However that is not what I am getting. Can anyone please tell me whether there is any mistale in any of those approaches? Am I missing somthing? Thanks -- View this message in context: http://n4.nabble.com/Simulation-of-VAR-tp1693295p1694899.html Sent from the R help mailing list archive at Nabble.com.