International Journal of Life Science and Engineering
Articles Information
International Journal of Life Science and Engineering, Vol.1, No.1, Mar. 2015, Pub. Date: Apr. 2, 2015
A Forecasting Model for Monthly Nigeria Treasury Bill Rates by Box-Jenkins Techniques
Pages: 20-25 Views: 3555 Downloads: 1439
[01] Ette Harrison Etuk, Department of Mathematics/Computer Science, Rivers State University of Science and Technology, Port Harcourt, Nigeria.
[02] Azubuike Samuel Agbam, Department of Mathematics/Computer Science, Rivers State University of Science and Technology, Port Harcourt, Nigeria.
[03] Bartholomew Anuriobi Uchendu, Department of Mathematics/Statistics, Federal Polytechic, Nekede, Imo State, Nigeria.
A realisation, TBR, of Nigeria Treasury Bill Rates from January 2006 to December 2014 is analysed by seasonal ARIMA methods. The time plot of the realisation reveals an overall downward trend from 2006 to 2009 followed by an overall upward trend up to 2013. Twelve-monthly differencing of TBR yields the series SDTBR which has an overall upward trend. Non-seasonal differencing of SDTBR yields the series DSDTBR with an overall horizontal trend and no clear seasonality. By the Augmented Dickey-Fuller Unit Root Test both TBR and SDTBR are adjudged non-stationary whereas DSDTBR is adjudged stationary. The correlogram of DSDTBR has a negative significant spike in the autocorrelation function at lag 12, an indication of seasonality of period 12 months and the presence of a seasonal moving average component of order one. By a novel proposal credited to Suhartono, initially the (0, 1, 1)x(0, 1, 1)12 SARIMA model is fitted. The non-significance of the lag 13 coefficient suggests the additive SARIMA model with significant coefficients at lags 1 and 12. This model is fitted and has been shown to be the more adequate model and may be used to forecast future Nigeria Treasury Bill Rates.
Nigeria Treasury Bill Monthly Rates, SARIMA Models, Additive Models, Time Series, ARIMA Models
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