International Journal of Mathematics and Statistics Studies (IJMSS)

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Modeling Power Exponential Error Innovations with Autoregressive Process

Abstract

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.

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

 

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Email ID: editor.ijmss@ea-journals.org
Impact Factor: 7.80
Print ISSN: 2053-2229
Online ISSN: 2053-2210
DOI: https://doi.org/10.37745/ijmss.13

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