LOGMVSKEWT - function for multivariate skew T density
Posted: Thu Mar 22, 2012 10:49 amThis computes the (log) multivariate skew-t density from Bauwens & Laurent(2005), "A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models," JBES, vol 23, pp 346-354. As this is a function (not a procedure), you will need to source the file in before you can use it.
Note that this is for a standardized density (mean 0, covariance matrix I). If your model generates mean zero data with covariance matrix <<sigma>>, you need to standardize the input vector by pre-multiplying by inv(%decomp(sigma)) and adjust the likelihood by subtracting .5 * log |sigma|.
%LOGMVSKEWT(z,xi,nu)
Parameters
Note that this is for a standardized density (mean 0, covariance matrix I). If your model generates mean zero data with covariance matrix <<sigma>>, you need to standardize the input vector by pre-multiplying by inv(%decomp(sigma)) and adjust the likelihood by subtracting .5 * log |sigma|.
logmvskewt.src- Function file
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%LOGMVSKEWT(z,xi,nu)
Parameters
| Z | a K vector, which is assumed to represent a standardized (mean 0, covariance matrix I) process | |
| xi | a K vector of (positive) skewness parameters (xi(i)=1.0 means no skewness, > 1 means skewed positive, < 1 means skewed negative) | |
| nu | degrees of freedom for underlying Student t. |