Discrete-Time Series Based on Asymmetric Distributions

Authors

  • Fatemeh Pooyannik Department of Statistics, Marvdasht branch, Islamic Azad University, Marvdsht, Iran
  • Zahra Khodadadi Department of Statistics, Marvdasht branch, Islamic Azad University, Marvdsht, Iran

Keywords:

Autoregressive Model, Bayesian Marginal Effect, Error, Double Exponential Distribution

Abstract

The goal of this study is to introduce a flexible intervention autoregressive process that is modified based on random autoregressive coefficients and asymmetric innovations. The transfer function of the proposed process is designed to follow a dynamic step change structure. In intervention analysis, outlier or influential observations significantly impact statistical inference. Therefore, we discuss Bayesian marginal effect analysis to evaluate the influence of errors in response variables, priors, and contemporaneous disturbances concerning the Bayesian factor evaluator. By considering Markov Chain Monte Carlo samples, the proposed marginal effects and diagnostic measures can be easily obtained. Real data on new weekly COVID-19 cases from Greece, covering the period from March 1, 2020, to December 17, 2023, confirms the effectiveness of the presented methods.

Published

2025-12-29

Issue

Vol. 20

Section

Vol. 20, No. 1, (2026)

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