JarqueBeraTest: (Robust) Jarque Bera Test

Description

This function performs the Jarque-Bera tests of normality either the robust or the classical way.

Usage

JarqueBeraTest(x, robust = TRUE, method = c("chisq", "mc"), N = 0, na.rm = FALSE)

Arguments

a numeric vector of data values.

robust

defines, whether the robust version should be used. Default is TRUE.

method

a character string out of chisq or mc, specifying how the critical values should be obtained. Default is approximated by the chisq-distribution or empirically via Monte Carlo.

number of Monte Carlo simulations for the empirical critical values

na.rm

defines if NAs should be omitted. Default is FALSE.

Value

A list with class htest containing the following components:

Details

The test is based on a joint statistic using skewness and kurtosis coefficients. The robust Jarque-Bera (RJB) version of utilizes the robust standard deviation (namely the mean absolute deviation from the median, as provided e. g. by MeanAD(x, FUN=median)) to estimate sample kurtosis and skewness. For more details see Gel and Gastwirth (2006).

Users can also choose to perform the classical Jarque-Bera test (see Jarque, C. and Bera, A (1980)).

References

Gastwirth, J. L.(1982) Statistical Properties of A Measure of Tax Assessment Uniformity, Journal of Statistical Planning and Inference 6, 1-12.

Gel, Y. R. and Gastwirth, J. L. (2008) A robust modification of the Jarque-Bera test of normality, Economics Letters 99, 30-32.

Jarque, C. and Bera, A. (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals, Economics Letters 6, 255-259.

Examples

Run this code

x <- rnorm(100)    # null hypothesis
JarqueBeraTest(x)

x <- runif(100)    # alternative hypothesis
JarqueBeraTest(x, robust=TRUE)

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