lawstat (version 3.2)

rjb.test: 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

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) 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)).

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

sj.test, rqq, jarque.bera.test (in tseries package).

Examples

# NOT RUN {
## 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


# }