The dimension-wise quadrature estimation of dynamic latent variable models for count data

Silvia Bianconcini () and Silvia Cagnone

Computational Statistics & Data Analysis, 2023, vol. 177, issue C

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. A recently developed dimension-wise quadrature is applied to deal with these likelihoods with high-dimensional integrals. A comparison is performed with the pairwise likelihood method, one of the most often used remedies. 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.

Keywords: Latent autoregressive models; Count data; Pairwise likelihood; Approximate likelihood inference (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:177:y:2023:i:c:s0167947322001657

DOI: 10.1016/j.csda.2022.107585

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