ABC for bivariate betas

Crakel and Flegal just arXived a short paper running ABC for doing inference on the parameters of two families of bivariate betas. And I could not but read it thru. And wonder why ABC was that necessary to handle the model. The said bivariate betas are defined from $V_1=(U_1+U_5+U_7)/(U_3+U_6+U_8)\,,$ $V_2=(U_2+U_5+U_8)/(U_4+U_6+U_7)$

when $U_i\sim \text{Ga}(\delta_i,1)$

and $X_1=V_1/(1+V_1)\,,\ X_2=V_2/(1+V_2)$

This makes each term in the pair Beta and the two components dependent. This construct was proposed by Arnold and Ng (2011). (The five-parameter version cancels the gammas for i=3,4,5.)

Since the pdf of the joint distribution is not available in closed form, Crakel and Flegal zoom on ABC-MCMC as the method of choice and discuss simulation experiments. (The choice of the tolerance ε as an absolute rather than relative value, ε=0.2,0.0.6,0.8, puzzles me, esp. since the distance between the summary statistics is not scaled.) I however wonder why other approaches are impossible. (Or why it is necessary to use this distribution to model correlated betas. Unless I am confused copulas were invented to this effect.) First, this is a latent variable model, so latent variables could be introduced inside an MCMC scheme. A wee bit costly but feasible. Second, several moments of those distributions are known so a empirical likelihood approach could be considered.

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