Facilitating an expectation-maximization (EM) algorithm to solve an integrated choice and latent variable (ICLV) model with fully correlated latent variables

It is well known that estimating the parameters of an integrated choice and latent variable (ICLV) model is not a trivial undertaking. The log-likelihood of an ICLV model cannot be evaluated analytically, and can only be evaluated by a simulation that requires large numbers of sample draws. While conducting simulation-based model estimations, researchers often encounter an estimation failure. Sohn (2017) suggests a novel estimation method to circumvent the problem by using an expectation-maximization algorithm (EM). However, a drawback of this method continues to be the requirement of a huge amount of computer memory to deal with an augmented covariance matrix. In the present study, this problem was overcome by connecting each latent variable in a structural equation to all individual specific variables. This restriction did not hamper the utility of an ICLV model during empirical experimentation. The main contribution of this study is to introduce a simple method devised to solve large-scale ICLV models.