Dear helpers, I wrote a code which estimates a multi-equation model with generalized least squares (GLS). I can use GLS because I know the covariance matrix of the residuals a priori. However, it is a bit slow and I wonder if anybody would be able to point out a way to make it faster (it is part of a bigger code and needs to run several times).
Any suggestion would be greatly appreciated. Carlo *************************************** Carlo Fezzi Senior Research Associate Centre for Social and Economic Research on the Global Environment (CSERGE), School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ United Kingdom. email: c.fe...@uea.ac.uk *************************************** Here is an example with 3 equations and 2 exogenous variables: ----- start code ------ N <- 1000 # number of observations library(MASS) ## parameters ## # eq. 1 b10 <- 7; b11 <- 2; b12 <- -1 # eq. 2 b20 <- 5; b21 <- -2; b22 <- 1 # eq.3 b30 <- 1; b31 <- 5; b32 <- 2 # exogenous variables x1 <- runif(min=-10,max=10,N) x2 <- runif(min=-5,max=5,N) # residual covariance matrix sigma <- matrix(c(2,1,0.7,1,1.5,0.5,0.7,0.5,2),3,3) # residuals r <- mvrnorm(N,mu=rep(0,3), Sigma=sigma) # endogenous variables y1 <- b10 + b11 * x1 + b12*x2 + r[,1] y2 <- b20 + b21 * x1 + b22*x2 + r[,2] y3 <- b30 + b31 * x1 + b32*x2 + r[,3] y <- cbind(y1,y2,y3) # matrix of endogenous x <- cbind(1,x1, x2) # matrix of exogenous #### MODEL ESTIMATION ### # build the big X matrix needed for GLS estimation: X <- kronecker(diag(1,3),x) Y <- c(y) # stack the y in a vector # residual covariance matrix for each observation covar <- kronecker(sigma,diag(1,N)) # GLS betas covariance matrix inv.sigma <- solve(covar) betav <- solve(t(X)%*%inv.sigma%*%X) # GLS mean parameter estimates betam <- betav%*%t(X)%*%inv.sigma%*%Y ----- end of code ---- ______________________________________________ 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.
