Comparison between Different Likelihood BasedEstimation Methods in Latent Variable Models for Categorical Data

Latent variable models represent a useful tool in different fields of research in which the constructs of interest are not directly observable. In presence of many latent variables and/or random effects, problems related to the integration of the likelihood function can arise since analytical solutions do not exist. In literature, different remedies have been proposed to overcome these problems. Among these, the pairwise likelihood method and, more recently, the dimension-wise quadrature have been shown to produce estimators with desirable properties. We compare the performance of the two methods for a class of dynamic latent variable models for count data.