Bayesian analysis of dynamic binary data: A simulation study and application to economic index SP

TL;DR

This paper evaluates the effectiveness of Bayesian methods, specifically MCMC, for modeling dynamic binary data over time, highlighting advantages over traditional MLE in handling complex, evolving datasets.

Contribution

It provides a simulation study and real-world application demonstrating Bayesian methods' advantages in dynamic binary data analysis.

Findings

Bayesian methods outperform MLE in complex, evolving data scenarios

Posterior distributions effectively incorporate past data information

Bayesian approach offers flexible modeling of dynamic binary data

Abstract

It is proposed in the literature that in some complicated problems maximum likelihood estimates (MLE) are not suitable or even do not exist. An alternative to MLE for estimation of the parameters is the Bayesian method. The Markov chain Monte Carlo (MCMC) simulation procedure is designed to fit Bayesian models. Bayesian method like classical method (MLE) has advantages and disadvantages. One of the advantages of Bayesian method over MLE method is the ability of saving the information included in past data through the posterior distributions of the model parameters to be used for modelling future data. In this article we investigate the performance of Bayesian method in modelling dynamic binary data when the data are growing over time and individuals.