A0429
Title: Approximate likelihood estimation of dynamic latent variable models for count data
Authors: Silvia Cagnone - University of Bologna (Italy) [presenting]
Silvia Bianconcini - University of Bologna (Italy)
Abstract: When dynamic latent variable models are specified for discrete and or mixed observations, problems related to the integration of the likelihood function arise since analytical solutions do not exist. Our recently developed dimension-wise quadrature is applied to deal with these intractable likelihoods. A comparison is made with one of the most often used remedies discussed in the literature, which is the pairwise likelihood method. Both a real data application and a simulation study show the superior performance of the dimension-wise quadrature with respect to the pairwise likelihood in estimating the parameters of the latent autoregressive process.
