It is well known in the literature that obtaining the parameter estimates for the Smooth Transition Autoregressive-Generalized Autoregressive Conditional Heteroskedasticity (STAR-GARCH) can be problematic due to computational difficulties. Conventional optimization algorithms do not seem to...

This paper proposes a scheme that speeds up the convergence of Markov Chain Monte Carlo (MCMC) algorithms in the context of limited-dependent variable models. The algorithm reduces autocorrelations more than the recently proposed Parameter Expansion Data Augumentation (PX-DA) algorithm. In...