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Brillinger, M. et. al (2008). Time series data Analysis and Theory Expanded Edition, Mc-Graw-Hill Inc. New York.

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Article

Forecast Performance of Multiplicative Seasonal Arima Model: an Application to Naira/Us Dollar Exchange Rate

1Department of Statistics, University of Ibadan, Nigeria


American Journal of Applied Mathematics and Statistics. 2014, Vol. 2 No. 3, 172-178
DOI: 10.12691/ajams-2-3-14
Copyright © 2014 Science and Education Publishing

Cite this paper:
ONASANYA OLANREWAJU. K, OLUSEGUN ALFRED OLAKUNLE, ADENIJI OYEBIMPE EMMANUEL. Forecast Performance of Multiplicative Seasonal Arima Model: an Application to Naira/Us Dollar Exchange Rate. American Journal of Applied Mathematics and Statistics. 2014; 2(3):172-178. doi: 10.12691/ajams-2-3-14.

Correspondence to: ONASANYA  OLANREWAJU. K, Department of Statistics, University of Ibadan, Nigeria. Email: onasanyaolarewaju1980@gmail.com

Abstract

Seasonality accounts majorly for quarterly and monthly movements in macro-economics and time series and in modelling such patterns to have a forecasting robustness, precision and reliability have been steadily increased in recent years but no consensus has been reached as to which model yields the appropriate description. However, this research seeks to address the robustness and accuracy of seasonal ARIMA model of Naira/Dollar exchange rate using a monthly data for the period of Jan 1994 to July 2013. The series was subjected to a time plot revealing seasonal patterns and upward trend. Both behaviours were taken into account by moving average and seasonal moving average, where little or fewer months of data were used to estimate the local level and trend and also few seasons of data were averaged over to estimate the seasonal patterns. The proposed model shows predictive power, robustness and precision which was revealed by the normality of data and residual analysis. The estimated forecast values from our proposed model are more realistic and closely reflect in the current economic reality in two countries as indicated by the forecast measurement criteria.

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