A point on discrete versus continuous state-space Markov chains
Year: 2025
Type: article
Abstract: This article investigates the effects of discrete marginal distributions on copula-based Markov chains. We establish results on mixing properties and parameter estimation for a copula-based Markov chain model with Bernoulli( p p ) marginal distributions, emphasizing some distinctions between continuous and discrete state-space Markov chains. We derive parameter estimators using the maximum-likelihood estimation (MLE) method and explore alternative estimators of p p that are asymptotically equivalent to the MLE. Furthermore, we provide the asymptotic distributions of these parameter estimators. A simulation study is conducted to evaluate the performance of the various estimators for p p . Additionally, we employ the likelihood ratio test to assess independence within the sequence.
Source: Dependence Modeling
Authors: Mathias Muia, Martial Longla
Institutions: University of South Alabama, University of Mississippi
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,187
OpenAlex ID: W4413159874