Normality, multivariate skewness and kurtosis test
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.
normality.test(x, multivariate.only = TRUE)
x |
Object of class ‘ |
multivariate.only |
If |
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.
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
‘ |
jb.mul |
A list of elements with class attribute
‘ |
containing the mutlivariate Jarque-Bera test, the multivariate Skewness and Kurtosis tests.
This function was named normality
in earlier versions of package
vars; it is now deprecated. See vars-deprecated
too.
Bernhard Pfaff
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.
data(Canada) var.2c <- VAR(Canada, p = 2, type = "const") normality.test(var.2c)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.