DescTools (version 0.99.14)

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

x

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.

N

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:
• statistic
  the value of the test statistic.
• parameter
  the degrees of freedom.
• p.value
  the p-value of the test.
• method
  type of test was performed.
• data.name
  a character string giving the name of the data.

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 (MeanAD) 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.

See Also

Alternative tests for normality as shapiro.test, AndersonDarlingTest, CramerVonMisesTest, LillieTest, PearsonTest, ShapiroFranciaTest qqnorm, qqline for producing a normal quantile-quantile plot

Examples

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

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

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