lobato.test: The Lobato and Velasco's Test for normality

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

View source: R/lobato_test.R

Description

Performs the Lobato and Velasco's test for normality. The null hypothesis (H0), is that the given data follows a Gaussian process.

Usage

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lobato.test(y,c = 1)

Arguments

y

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

c

a positive real value that identifies the total amount of values used in the cumulative sum.

Details

This test proves a normality assumption in correlated data employing the skewness-kurtosis test statistic, but studentized by standard error estimates that are consistent under serial dependence of the observations. The test was proposed by Lobato, I., & Velasco, C. (2004) and implemented by Nieto-Reyes, A., Cuesta-Albertos, J. & Gamboa, F. (2014).

Value

A h.test class with the main results of the Lobato and Velasco's hypothesis test. The h.test class have the following values:

Author(s)

Asael Alonzo Matamoros and Alicia Nieto-Reyes.

References

Lobato, I., & Velasco, C. (2004). A simple test of normality in time series. Journal of econometric theory. 20(4), 671-689.

Nieto-Reyes, A., Cuesta-Albertos, J. & Gamboa, F. (2014). A random-projection based test of Gaussianity for stationary processes. Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 124-141.

See Also

lobato.statistic,epps.test

Examples

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

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