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Volume 6, Issue 4, July 2017, Page: 191-197
Identification and Modeling of Outliers in a Discrete - Time Stochastic Series
Imoh Udo Moffat, Department of Mathematics and Statistics, University of Uyo, Uyo, Nigeria
Emmanuel Alphonsus Akpan, Department of Mathematics and Statistics, University of Uyo, Uyo, Nigeria
Received: Feb. 27, 2017;       Accepted: Apr. 8, 2017;       Published: Jul. 5, 2017
DOI: 10.11648/j.ajtas.20170604.14      View  2074      Downloads  87
Abstract
This study was prompted by the fact that the presence of outliers in discrete-time stochastic series may result in model misspecification, biases in parameter estimation and in addition, it is difficult to identify some outliers due to masking effects. However, the iterative approach which involves joint estimation of outliers effects and model parameters appears to be a panacea for masking effects. Considering the dataset on credit to private sector in Nigeria from 1981 to 2014, we found that ARIMA (1, 1, 1) model fitted well to the series without considering the presence of outliers. Using the iterative procedure method to reduce masking effects, the following outliers, IO (t = 24), AO (t = 33) and TC (t = 22) were identified. Adjusting the series for outliers and iterating further, ARIMA (2, 0, 1) model alongside AO (t = 33) and TC (t = 22) outliers was found to fit the series better than ARIMA (1, 1, 1) model. The implication is that in the presence of outliers, ARIMA (1, 1, 1) model was misspecified, the order of integration was wrong and by extension, autocorrelation and partial autocorrelation functions were misleading, and the estimated parameters were biased.
Keywords
ARIMA Model, Discrete - Time Stochastic Series, Masking Effects, Outlier Effects, Outlier Types
To cite this article
Imoh Udo Moffat, Emmanuel Alphonsus Akpan, Identification and Modeling of Outliers in a Discrete - Time Stochastic Series, American Journal of Theoretical and Applied Statistics. Vol. 6, No. 4, 2017, pp. 191-197. doi: 10.11648/j.ajtas.20170604.14
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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