Forecast Comparison of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Self Exciting Threshold Autoregressive (SETAR) Models
American Journal of Theoretical and Applied Statistics
Volume 6, Issue 6, November 2017, Pages: 278-283
Received: Aug. 13, 2017;
Accepted: Aug. 31, 2017;
Published: Nov. 21, 2017
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Akintunde Mutairu Oyewale, Department of Statistics, School of Applied Sciences, The Federal Polytechnic, Ede, Nigeria
Olalude Gbenga Adelekan, Department of Statistics, School of Applied Sciences, The Federal Polytechnic, Ede, Nigeria
Oseghale Osezuwa Innocient, Department of Statistics, College of Natural Sciences, Joseph Ayo Babalola University, Ikeji-Arakeji, Nigeria
Financial and Economic time series literatures have shown that financial and economic time series data exhibit non-linearity in their behavior. In order to be mindful of such behavior as applied to Nigeria inflation rates, this study therefore, applies a two stages non-linear self-exciting threshold autoregressive model (SETAR) to Nigeria inflation rates. The results obtained for both in-sample and out-of-sample forecast performances for SETAR model were compared with results of linear seasonal autoregressive integrated moving average (SARIMA). On the basis of in-sample forecast performance of linear SARIMA and non-linear SETAR, using performance measure indices like MAE and RMSE, the results obtained indicated that non-linear SETAR model performed better than linear SARIMA. So also for the out-of-sample forecast performance using multi-step ahead forecast performance, the results also indicated that non-linear SETAR out performed linear SARIMA.
Akintunde Mutairu Oyewale,
Olalude Gbenga Adelekan,
Oseghale Osezuwa Innocient,
Forecast Comparison of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Self Exciting Threshold Autoregressive (SETAR) Models, American Journal of Theoretical and Applied Statistics.
Vol. 6, No. 6,
2017, pp. 278-283.
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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