CODEWITHSUNDEEP
r
Bob
Bob

Reputation: 516

Replicating a function by bootstrapping the data in R

I am estimating a GMM model using library(gmm). 

n <- 200
x1 <- rnorm(n)
x2 <- rnorm(n)
x3 <- rnorm(n)
x4 <- rnorm(n)
x5 <- rnorm(n)
x6 <- rnorm(n)

xx <- cbind(x1, x2, x3, x4, x5, x6)
fun <- function(betastar, x) {
m1 <- (x[,1] - x[,2]*betastar - x[,3] - x[,4])*x[,5]
m2 <- (x[,1] - x[,2]*betastar - x[,3] - x[,4])*x[,6]
f <- cbind(m1,m2)
return(f)
}

library(gmm)
k <-  gmm(fun, x=xx, 0, optfct="optim", lower = 0, upper = 2, method="Brent")

I want to replicate it B times by bootstrapping my sample xx (with replacement). My scope is to save the standard errors of betastar for each replication and store all of them somewhere. Is there a fast way to do that ? I know there is the library(boot) which in principle should allow me to do that, but I am having an hard time to figure out how, since for using the function gmm I need to specify another function (fun)

EDIT: What the gmm function is doing is minimizing the other function fun with respect to the parameter betastar. All the terms in gmm() define the way gmm works. What I want is to bind betastar (which is a coefficient) and its standard error in an object, for any 1:B replication. They can be recovered by the commands coef(k) and sqrt(k$vcov) I am trying the following

B <- 199  # number of bootstrapping
betak_boot <- rep(NA, 199)
se_betak_boot <- rep(NA, 199)
for (ii in 1:B){
  sample <- (replicate(ii, apply(xx, 2, sample, replace = TRUE)))
  k_cons <- gmm(fun, x=samples, 0, gradv=Dg, optfct="optim", lower = 0, upper = 2, method="Brent")
  betak_boot[ii] <- coef(k_cons)
  se_betak_boot[ii] <- sqrt(k_cons$vcov)
}

I don't know why, I get an error while applying fun, i.e. Error in x[, 1] : incorrect number of dimensions. Indeed, I don't know why sample is

dim(sample)
[1] 200   6   1

Upvotes: 3

Views: 356

Answers (1)

Toby
Toby

Reputation: 543

library(gmm)
set.seed(123)
n <- 200
x1 <- rnorm(n)
x2 <- rnorm(n)
x3 <- rnorm(n)
x4 <- rnorm(n)
x5 <- rnorm(n)
x6 <- rnorm(n)

xx <- cbind(x1, x2, x3, x4, x5, x6)
fun <- function(betastar, x) {
m1 <- (x[,1] - x[,2]*betastar - x[,3] - x[,4])*x[,5]
m2 <- (x[,1] - x[,2]*betastar - x[,3] - x[,4])*x[,6]
f <- cbind(m1,m2)
return(f)
}
ii=4
samples <- replicate(ii, apply(xx, 2, sample, replace = TRUE))

coefk <- rep(0,ii)
sdk <- rep(0,ii)

for (i in 1:ii) {
        xx <- samples[,,i]
        k <-  gmm(fun, x=xx, 0, optfct="optim", lower = 0, upper = 2, method="Brent")
        coefk[i] <- coef(k)
        sdk[i] <- sqrt(k$vcov)[1,1]
 }

Upvotes: 1

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