Open AccessMixing ARMA Models with EGARCH Models and Using it in Modeling and Analyzing the Time Series of Temperature
Author(s) -
Abduljabbar Ali Mudhir
Publication year - 2021
Publication title -
iraqi journal of science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.152
H-Index - 4
eISSN - 2312-1637
pISSN - 0067-2904
DOI - 10.24996/ijs.2021.62.7.19
Subject(s) - heteroscedasticity , mixing (physics) , series (stratigraphy) , autoregressive–moving average model , variance (accounting) , mathematics , time series , statistics , convergence (economics) , econometrics , autoregressive model , paleontology , physics , accounting , quantum mechanics , economics , business , biology , economic growth
In this article our goal is mixing ARMA models with EGARCH models and composing a mixed model ARMA(R,M)-EGARCH(Q,P) with two steps, the first step includes modeling the data series by using EGARCH model alone interspersed with steps of detecting the heteroscedasticity effect and estimating the model's parameters and check the adequacy of the model. Also we are predicting the conditional variance and verifying it's convergence to the unconditional variance value. The second step includes mixing ARMA with EGARCH and using the mixed (composite) model in modeling time series data and predict future values then asses the prediction ability of the proposed model by using prediction error criterions.