tsbootstrap generates bootstrap samples for general stationary
data and computes the bootstrap estimate of standard error and bias
if a statistic is given.tsbootstrap(x, nb = 1, statistic = NULL, m = 1, b = NULL,
type = c("stationary","block"), ...)type is "stationary", then b is the
mean block length. If type is "block", then b
is the fixed block length."stationary"
(stationary bootstrap with mean block length b) and
"block" (blockwise bootstrap with block length
statistic which are
passed unchanged each time statistic is called.statistic is NULL, then it returns a matrix or time
series with nb columns and length(x) rows containing the
bootstrap data. Each column contains one bootstrap sample. If statistic is given, then a list of class
"resample.statistic" with the following elements is returned:
statistic to each of
the simulated time series.statistic to the
original series.statistic.statistic.tsbootstrap.type is "stationary", then the stationary
bootstrap scheme with mean block length b according to Politis
and Romano (1994) is computed. For type equals "block",
the blockwise bootstrap with block length b according to
Kuensch (1989) is used. If m > 1, then the block of blocks bootstrap is computed
(see Kuensch, 1989). The basic sampling scheme is the same as for
the case m = 1, except that the bootstrap is applied to a series
y containing blocks of length m, where each block of y is
defined as $y[t] = (x[t], \dots, x[t-m+1])$. Therefore, for the block
of blocks bootstrap the first argument of statistic is given by
a n x m matrix yb, where each row of yb contains one
bootstrapped basic block observation $y[t]$ (n is the number of
observations in x).
Note, that for statistics which are functions of the empirical
m-dimensional marginal (m > 1) only this procedure
yields asymptotically valid bootstrap estimates. The
case m = 1 may only be used for symmetric statistics (i.e., for
statistics which are invariant under permutations of x).
tsboot does not implement the block of blocks
bootstrap, and, therefore, the first example in tsboot
yields inconsistent estimates.
For consistency, the (mean) block length b should grow with
n at an appropriate rate. If b is not given, then a
default growth rate of const * n^(1/3) is used. This rate is
"optimal" under certain conditions (see the references for more
details). However, in general the growth rate depends on the specific
properties of the data generation process. A default value for
const has been determined by a Monte Carlo simulation using a
Gaussian AR(1) process (AR(1)-parameter of 0.5, 500
observations). const has been chosen such that the mean square
error for the bootstrap estimate of the variance of the empirical mean
is minimized.
Note, that the computationally intensive parts are fully implemented
in C which makes tsbootstrap about 10 to 30 times faster
than tsboot.
Missing values are not allowed.
There is a special print method for objects of class
"resample.statistic" which by default uses
max(3, getOption("digits") - 3) digits to format real numbers.
D. N. Politis and J. P. Romano (1994): The Stationary Bootstrap. Journal of the American Statistical Association 89, 1303--1313.
sample,
surrogate,
tsbootn <- 500 # Generate AR(1) process
a <- 0.6
e <- rnorm(n+100)
x <- double(n+100)
x[1] <- rnorm(1)
for(i in 2:(n+100)) {
x[i] <- a * x[i-1] + e[i]
}
x <- ts(x[-(1:100)])
tsbootstrap(x, nb=500, statistic=mean)
# Asymptotic formula for the std. error of the mean
sqrt(1/(n*(1-a)^2))
acflag1 <- function(x)
{
xo <- c(x[,1], x[1,2])
xm <- mean(xo)
return(mean((x[,1]-xm)*(x[,2]-xm))/mean((xo-xm)^2))
}
tsbootstrap(x, nb=500, statistic=acflag1, m=2)
# Asymptotic formula for the std. error of the acf at lag one
sqrt(((1+a^2)-2*a^2)/n)Run the code above in your browser using DataLab