jackknife: Implementing the jackknife method of estimating standard...

In yukai-yang/jackknife: Jackknife Method of Estimating Standard Errors

Description

Implementing the jackknife method of estimating standard errors of some linear statistics

Usage

jackknife(vx, tfunc, dfunc, im, vw, ...)

Arguments

vx	a vector of observations
tfunc	the function which takes the rho argument and returns the statistic
dfunc	the delta function which takes a tuple or window of the observations and returns a vector of deltas
im	the tuple or window size
vw	the weights for deleting or downweighting the blocks
...	other arguments for both tfunc and dfunc if any

Details

The function takes several arguments including functions and returns a list containing the results.

Value

a list containing the results

The object is a list containing the following components:

T	the value of the statistic
jack	a vector or matrix containing the jackknifed statistics
sig2	the jackknifed estimate of the variance of the statistic

Author(s)

Yukai Yang, [e-mail]

Examples

vw1 = 1
vw2 = c(0.25,0.75,1,0.75,0.25)

# the AR(1) model
iN = 100
vx <- rnorm(iN)

# for first order autocorrelation
# dfunc vec 2, y_t and y_t-1
dfunc <- function(yy, my, vy) return(prod(yy - my)/vy)

tfunc <- function(rho, my, vy) return(rho)

# results
ret = jackknife(as.vector(vx), tfunc, dfunc, im=2, vw=vw1, my=mean(vx), vy=var(vx))
ret

ret = jackknife(as.vector(vx), tfunc, dfunc, im=2, vw=vw2, my=mean(vx), vy=var(vx))
ret

yukai-yang/jackknife documentation built on Nov. 5, 2019, 1:21 p.m.