lobato.test {nortsTest}

The asymptotic Lobato and Velasco's Test for normality.

Description

Performs the asymptotic Lobato and Velasco's test of normality for univariate time series. Computes the p-value using the asymptotic Gamma Distribution.

Usage

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

statistic:

the Lobato and Velasco's statistic.

parameter:

the test degrees freedoms.

p.value:

the p-value for the test.

alternative:

a character string describing the alternative hypothesis.

method:

a character string “Lobato and Velasco's test”.

data.name:

a character string giving the name of the data.

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

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