vavra.test: The Psaradakis and Vavra test for normality

In nortsTest: Assessing Normality of Stationary Process

Description

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

Usage

vavra.test(y,reps = 1000,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.
h	an integer with the first burn-in sieve bootstrap replicates.
seed	An optional seed to use.

Details

The Psaradakis and Vavra test approximates the empirical distribution function of the Anderson Darling's statistic, using a sieve bootstrap approximation. The test was proposed by Psaradakis, Z. & Vavra, M (20.17).

Value

a h.test class with the main results of the Epps hypothesis test. The h.test class have the following values:

• "bootstrap A"The sieve bootstrap A statistic

• "p.value"The p value

• "alternative"The alternative hypothesis

• "method"The used method

• "data.name"The data name.

Author(s)

Asael Alonzo Matamoros.

References

Psaradakis, Z. & Vavra, 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)

nortsTest documentation built on Aug. 16, 2021, 5:06 p.m.