I'm trying to work out how to apply the method of moments to estimate the parameters of the skew-t distribution.
From slide 12 of the following presentation: http://www.eief.it/files/2008/11/monti-8-maggio.pdf or page 17 of Azzalini and Capitanio 2012 available here: http://azzalini.stat.unipd.it/SN/se-ext.pdf the first four moments are:
$$ \mu = \xi + \omega \delta b_v \\ \sigma^2 = \omega^2\left\{\frac{\nu}{\nu - 2} - \delta^2b_v^2\right\}\\ \gamma_1 = \delta b_v \left\{ \frac{\nu(3-\delta^2)}{\nu-3}-\frac{3\nu}{\nu-2} + 2\delta^2b_v^2\right\}\left\{\frac{\nu}{\nu-2}-\delta^2b_v^2\right\}^{-3/2} \\ \gamma_2 = \left\{\frac{3\nu^2}{(\nu-2)(\nu-4)}-\frac{4\delta^2b_v^2\nu(3-\delta^2)}{\nu-3}+\frac{6\delta^2b_v^2\nu}{\nu-2}-3\delta^4b_v^4\right\}\left\{\frac{\nu}{\nu-2}-\delta^2b_v^2\right\}^{-2}-3 $$
where
$$ b_v = \left(\frac{\nu}{\pi}\right)^{1/2}\frac{\Gamma(\nu/2+1/2)}{\Gamma(\nu/2)} \\ \delta = \frac{\alpha}{(1+\alpha^2)^{1/2}} $$
and $\xi$, $\omega$, $\alpha$ are the location, scale and skewness parameters and $\nu$ the degrees of freedom.
For the skew-normal distribution i.e. the special case of $\nu=\infty$, it's easy to proceed as described by the Wikipedia article on that distribution (https://en.wikipedia.org/wiki/Skew_normal_distribution#Estimation) and relate the sample skewness to the skewness parameter; once the skewness parameter is estimated then the others follow.
However, I'm finding it challenging to rearrange the above equations to derive the skewness parameter (via $\delta$) either using the skewness or kurtosis. My intuition is that this is not possible due to the skewness depending not just on the skewness parameter but also the degrees of freedom. Is this correct?