normality: Normality, multivariate skewness and kurtosis test

In vars: VAR Modelling

Normality, multivariate skewness and kurtosis test

Description

This function computes univariate and multivariate Jarque-Bera tests and multivariate skewness and kurtosis tests for the residuals of a VAR(p) or of a VECM in levels.

Usage

normality.test(x, multivariate.only = TRUE)

Arguments

x	Object of class ‘varest’; generated by VAR(), or an object of class ‘vec2var’; generated by vec2var().
multivariate.only	If TRUE (the default), only multivariate test statistics are computed.

Details

Multivariate and univariate versions of the Jarque-Bera test are applied to the residuals of a VAR. The multivariate version of this test is computed by using the residuals that are standardized by a Choleski decomposition of the variance-covariance matrix for the centered residuals. Please note, that in this case the test result is dependant upon the ordering of the variables.

Value

A list of class ‘varcheck’ with the following elements is returned:

resid	A matrix of the residuals.
jb.uni	A list of elements with class attribute ‘htest’ containing the univariate Jarque-Bera tests. This element is only returned if multivariate.only = FALSE is set.
jb.mul	A list of elements with class attribute ‘htest’.

containing the mutlivariate Jarque-Bera test, the multivariate Skewness and Kurtosis tests.

Note

This function was named normality in earlier versions of package vars; it is now deprecated. See vars-deprecated too.

Author(s)

Bernhard Pfaff

References

Hamilton, J. (1994), Time Series Analysis, Princeton University Press, Princeton.

Jarque, C. M. and A. K. Bera (1987), A test for normality of observations and regression residuals, International Statistical Review, 55: 163-172.

Lütkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.

See Also

VAR, vec2var, plot

Examples

data(Canada)
var.2c <- VAR(Canada, p = 2, type = "const")
normality.test(var.2c)

vars documentation built on March 31, 2023, 10:30 p.m.