nortsTest (version 1.1.2)

vavra.test: The Psaradakis and Vávra test for normality.

Description

Performs the Psaradakis and Vávra distance test for normality. The null hypothesis (H0), is that the given data follows a Gaussian process.

Usage

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

Value

A list with class "h.test" containing the following components:

statistic:

the sieve bootstrap A statistic.

p.value:

the p value for the test.

alternative:

a character string describing the alternative hypothesis.

method:

a character string “Psaradakis and Vávra test”.

data.name:

a character string giving the name of the data.

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, and "cvm" for the Cramer Von Mises' 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 Psaradakis and Vávra test approximates the empirical distribution function of the Anderson Darling's statistic, using a sieve bootstrap approximation. The test was proposed by 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

lobato.test, epps.test

Examples

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