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

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

1 | ```
lobato.test(y,c = 1)
``` |

`y` |
a numeric vector or an object of the |

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

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)*.

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

"lobato"The Lobato and Velasco's statistic

"df"The test degrees freedoms

"p.value"The p value

"alternative"The alternative hypothesis

"method"The used method

"data.name"The data name.

Asael Alonzo Matamoros and Alicia Nieto-Reyes.

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.

1 2 3 | ```
# Generating an stationary arma process
y = arima.sim(100,model = list(ar = 0.3))
lobato.test(y)
``` |

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