Assessment of the performance of the Bayesian Forecasting for Vector ARMA Processes

Emad E. A. Soliman; Sherif S. Ali

doi:10.64060/JASR.v2i1.2

Authors

Emad E. A. Soliman Department of Statistics, King Abdulaziz University, Jeddah 21589, Saudi Arabia Author https://orcid.org/0000-0001-5001-9330
Sherif S. Ali Department of Statistics, King Abdulaziz University, Jeddah 21589, Saudi Arabia Author https://orcid.org/0000-0002-1295-232X

DOI:

https://doi.org/10.64060/JASR.v2i1.2

Keywords:

Vector autoregressive moving average processes;, Bayesian forecasting, Prediction

Abstract

Multivariate time series are widely observed in numerous domains. In economics, for example, you can monitor the annual savings in conjunction with the real interest rate. Such variables are jointly analyzed to understand the dynamic interactions that exist between them, thus improving the precision of forecasts. Enhanced forecasting is achievable when the series are examined together, especially when one series holds information about another one. An approximate Bayesian analytical method to estimate and forecast vector autoregressive moving average (Vector ARMA) processes was introduced by Shaarawy (1989). A basic goal for the research in hand is the numerical assessment of the proposed approach in tackling forecasting difficulties associated with Vector ARMA processes through a comprehensive simulation study. Furthermore, the research checks how the performance of the suggested method fluctuates when varying parameter values and time series lengths. The findings of the numerical study demonstrated that the methodology was effective in accurately forecasting future observations for Vector ARMA processes across various values of the parameter and different time series lengths.

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