nortsTest (version 1.1.2)

vavra.sample: Vávra test's sieve Bootstrap sample for Anderson Darling statistic

Description

Generates a sieve bootstrap sample of the Anderson-Darling statistic test.

Usage

vavra.sample(y, normality = c("ad","lobato","jb","cvm","shapiro","epps"),
                    reps = 1000, h = 100, seed = NULL, c = 1, lambda = c(1,2))

Value

A numeric array with the Anderson Darling sieve bootstrap sample

Arguments

y

a numeric vector or an object of the ts class containing a stationary time series.

normality

A character string naming the desired test for checking normality. Valid values are "epps" for the Epps, "lobato" for Lobato and Velasco's, "jb" for the Jarque and Bera, "ad" for Anderson Darling test,"cvm" for the Cramer Von Mises' test, and "shapiro" for the Shapiro's test. The default value is "ad" test.

reps

an integer with the total bootstrap repetitions.

h

an integer with the first burn-in sieve bootstrap replicates.

seed

An optional seed to use.

c

a positive real value used as argument for the Lobato's test.

lambda

a numeric vector used as argument for the Epps's test.

Author

Asael Alonzo Matamoros.

Details

The Vávra test approximates the empirical distribution function of the Anderson-Darlings statistic, using a sieve bootstrap approximation. The test was proposed by Psaradakis, Z. & Vávra, M (20.17).

This function is the equivalent of xarsieve of Psaradakis, Z. & Vávra, M (20.17).

References

Psaradakis, Z. and Vávra, M. (2020) Normality tests for dependent data: large-sample and bootstrap approaches. Communications in Statistics-Simulation and Computation 49 (2). ISSN 0361-0918.

Psaradakis, Z. & Vávra, M. (2017). A distance test of normality for a wide class of stationary process. Journal of Econometrics and Statistics. 2, 50-60.

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

See Also

epps.statistic, lobato.statistic

Examples

# Generating an stationary arma process
y = arima.sim(100,model = list(ar = 0.3))
adbs = vavra.sample(y)
mean(adbs)