vavra.test: The Psaradakis and Vavra test for normality

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/vavra_test.R

Description

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

Usage

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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:

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

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

nortsTest documentation built on June 17, 2021, 5:06 p.m.