epps.statistic: Estimates the Epps statistic.

In nortsTest: Assessing Normality of Stationary Process

Estimates the Epps statistic.

Description

Estimates the Epps statistic minimizing the quadratic loss of the process' characteristic function in terms of the first two moments.

Usage

epps.statistic(y, lambda = c(1,2))

Arguments

y	a numeric vector or an object of the ts class containing a stationary time series.
lambda	a numeric vector for evaluating the characteristic function. This values could be selected by the user for a better test performance. By default, the values are 'c(1,2)', another plausible option is to select random values.

Details

The Epps test minimize the process' empirical characteristic function using a quadratic loss in terms of the process two first moments. Nieto-Reyes, A., Cuesta-Albertos, J. & Gamboa, F. (2014) upgrade the test implementation by allowing the option of evaluating the characteristic function with random values.

This function is the equivalent of Sub in Nieto-Reyes, A., Cuesta-Albertos, J. & Gamboa, F. (2014). This function uses a quadratic optimization solver implemented by Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (2007).

Value

a real value with the Epps test's statistic.

Author(s)

Alicia Nieto-Reyes and Asael Alonzo Matamoros.

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.statistic

Examples

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

nortsTest documentation built on May 29, 2024, 10:05 a.m.