International Journal of Mathematics and Statistics Studies (IJMSS)

EA Journals

Modeling Power Exponential Error Innovations with Autoregressive Process


The regular gussian assumption of the error terms is employed in dynamic time series models when the underlying data are not normally distributed, this often results in incorrect parameter estimations and forecast error. As a result, this paper developed maximum likelihood method of estimation of parameters of an autoregressive model of order 2 [AR (2)] with power-exponential innovations. The performance of the parameters of AR (2) in comparison to normal error innovations was evaluated using the Akaike information criterion (AIC) and forecast performance metrics (RMSE and MAE). Both real data sets and simulated data with different sample sizes were used to validate the models. The results revealed that, it is more appropriate and efficient to model non-normal time series data using AR (2) exponential power error innovations.


Keywords: Innovations, Maximum Likelihood Estimation, autoregressive process, power exponential.

cc logo

This work by European American Journals is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License


Recent Publications

Email ID:
Impact Factor: 7.80
Print ISSN: 2053-2229
Online ISSN: 2053-2210

Author Guidelines
Submit Papers
Review Status


Scroll to Top

Don't miss any Call For Paper update from EA Journals

Fill up the form below and get notified everytime we call for new submissions for our journals.