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.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 6, Issue 4) |
| DOI | 10.11648/j.ajtas.20170604.14 |
| Page(s) | 191-197 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
ARIMA Model, Discrete - Time Stochastic Series, Masking Effects, Outlier Effects, Outlier Types
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APA Style
Imoh Udo Moffat, Emmanuel Alphonsus Akpan. (2017). Identification and Modeling of Outliers in a Discrete - Time Stochastic Series. American Journal of Theoretical and Applied Statistics, 6(4), 191-197. https://doi.org/10.11648/j.ajtas.20170604.14
ACS Style
Imoh Udo Moffat; Emmanuel Alphonsus Akpan. Identification and Modeling of Outliers in a Discrete - Time Stochastic Series. Am. J. Theor. Appl. Stat. 2017, 6(4), 191-197. doi: 10.11648/j.ajtas.20170604.14
AMA Style
Imoh Udo Moffat, Emmanuel Alphonsus Akpan. Identification and Modeling of Outliers in a Discrete - Time Stochastic Series. Am J Theor Appl Stat. 2017;6(4):191-197. doi: 10.11648/j.ajtas.20170604.14
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Download@article{10.11648/j.ajtas.20170604.14,
author = {Imoh Udo Moffat and Emmanuel Alphonsus Akpan},
title = {Identification and Modeling of Outliers in a Discrete - Time Stochastic Series},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {6},
number = {4},
pages = {191-197},
doi = {10.11648/j.ajtas.20170604.14},
url = {https://doi.org/10.11648/j.ajtas.20170604.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20170604.14},
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.},
year = {2017}
}
TY - JOUR T1 - Identification and Modeling of Outliers in a Discrete - Time Stochastic Series AU - Imoh Udo Moffat AU - Emmanuel Alphonsus Akpan Y1 - 2017/07/05 PY - 2017 N1 - https://doi.org/10.11648/j.ajtas.20170604.14 DO - 10.11648/j.ajtas.20170604.14 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 191 EP - 197 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20170604.14 AB - 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. VL - 6 IS - 4 ER -
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