A point on discrete versus continuous state-space Markov chains

Muia Mathias () and Longla Martial ()
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Muia Mathias: Department of Mathematics and Statistics, University of South Alabama, Mobile, Al 36688, United States of America
Longla Martial: Department of Mathematics, University of Mississippi, University, MS 38677, United States of America

Dependence Modeling, 2025, vol. 13, issue 1, 23

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.

Keywords: asymptotic normality; dependent Bernoulli trials; Frechet family of copulas; mixing for Markov chains (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:13:y:2025:i:1:p:23:n:1001

DOI: 10.1515/demo-2025-0015

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