sieve.bootstrap: Generates a sieve bootstrap sample.

In nortsTest: Assessing Normality of Stationary Process

Generates a sieve bootstrap sample.

Description

The function generates a sieve bootstrap sample for a univariate linear stochastic process.

Usage

sieve.bootstrap(y,reps = 1000,pmax = NULL,h = 100,seed = NULL)

Arguments

y	a numeric vector or an object of the ts class containing a stationary time series.
reps	an integer with the total bootstrap repetitions.
pmax	an integer with the max considered lags for the generated ar(p) process. By default, pmax = NULL.
h	an integer with the first burn-in sieve bootstrap replicates.
seed	An optional seed to use.

Details

simulates bootstrap samples for the stochastic process y, using a stationary auto-regressive model of order "pmax", AR(pmax). If pmax = NULL (default), the function estimates the process maximum lags using an AIC as a model selection criteria.

Value

A matrix or reps row and n columns, with the sieve bootstrap sample and n the time series length.

Author(s)

Asael Alonzo Matamoros.

References

Bulmann, P. (1997). Sieve Bootstrap for time series. Bernoulli. 3(2), 123 -148.

See Also

lobato.test, epps.test.

Examples

# Generating an stationary arma process
y = arima.sim(100,model = list(ar = 0.3))
M = sieve.bootstrap(y)

nortsTest documentation built on May 29, 2024, 10:05 a.m.