library(dse1) library(vars) ## A-model Apoly <- array(c(1.0, -0.5, 0.3, 0.8, 0.2, 0.1, -0.7, -0.2, 0.7, 1, 0.5, -0.3) , c(3, 2, 2)) ## Setting covariance to identity-matrix B <- diag(2) ## Generating the VAR(2) model svarA <- ARMA(A = Apoly, B = B) ## Simulating 500 observations svarsim <- simulate(svarA, sampleT = 500, rng = list(seed = c(123456))) ## Obtaining the generated series svardat <- matrix(svarsim$output, nrow = 500, ncol = 2) colnames(svardat) <- c("y1", "y2") ## Estimating the VAR varest <- VAR(svardat, p = 2, type = "none") ## Setting up matrices for A-model Amat <- diag(2) Amat[2, 1] <- NA Amat[1, 2] <- NA ## Estimating the SVAR A-type by direct maximisation ## of the log-likelihood args(SVAR) svar.A <- SVAR(varest, estmethod = "direct", Amat = Amat, hessian = TRUE)