epps.test: The Epps and Pulley Test for normality.

In nortsTest: Assessing Normality of Stationary Process

Description

Performs the Epps test for normality. The null hypothesis (H0) is that the given data follows a stationary Gaussian process.

Usage

epps.test(y)

Arguments

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

Details

The Epps test minimize the process' empirical characteristic function using a quadratic loss in terms of the process two first moments. The test was proposed by Epps, T.W. (1987) and implemented by Nieto-Reyes, A., Cuesta-Albertos, J. & Gamboa, F. (2014) using the amoebam() function of Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (2007).

Value

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

• "epps"The Epps statistic

• "df"The test degrees freedoms

• "p.value"The p value

• "alternative"The alternative hypothesis

• "method"The used method

• "data.name"The data name.

Author(s)

Asael Alonzo Matamoros and Alicia Nieto-Reyes.

References

Epps, T.W. (1987). Testing that a stationary time series is Gaussian. The Annals of Statistic. 15(4), 1683-1698.

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.

Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (2007). Numerical Recipes. The Art of Scientific Computing. Cambridge University Press.

See Also

lobato.test

Examples

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

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