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Last data update: 2014.03.03

R: Test of Normailty - Robust Jarque Bera Test

rjb.test

R Documentation

Test of Normailty - Robust Jarque Bera Test

Description

This function performs the robust and classical Jarque-Bera tests of normality.

Usage

rjb.test(x, option = c("RJB", "JB"), 
         crit.values = c("chisq.approximation", "empirical"), N = 0)

Arguments

`x`	a numeric vector of data values.
`option`	The choice of the test must be "RJB" (default) or "JB".
`crit.values`	a character string specifying how the critical values should be obtained, i.e. approximated by the chisq-distribution (default) or empirically.
`N`	number of Monte Carlo simulations for the empirical critical values

Details

The test is based on a joint statistic using skewness and kurtosis coefficients. The Robust Jarque-Bera (RJB) is the robust version of the Jarque-Bera (JB) test of normality. In particular, RJB utilizes the robust standard deviation (namely the Average Absolute Deviation from the Median (MAAD)) to estimate sample kurtosis and skewness (default option). 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)).

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.

Note

Modified from 'jarque.bera.test' (in 'tseries' package).

Author(s)

W. Wallace Hui, Yulia R. Gel, Joseph L. Gastwirth, Weiwen Miao

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

## Normally distributed data
x = rnorm(100)
rjb.test(x)

## Sample Output
##
##        Robust Jarque Bera Test
##
## data:  x
## X-squared = 0.962, df = 2, p-value = 0.6182


## Using zuni data
data(zuni)
rjb.test(zuni[,"Revenue"])

##        Robust Jarque Bera Test
##
## data:  zuni[, "Revenue"] 
## X-squared = 54595.63, df = 2, p-value < 2.2e-16