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Another Look at the Time Series Modelling of Monthly Internally Generated Revenue of MBAITOLI LGA of Nigeria

Author

Listed:
  • Ette Harrison Etuk
  • Azubuike Samuel Agbam
  • Pius Ubari Sibeate
  • Felicia Ette Etuk

Abstract

Monthly internally generated revenue of Mbaitoli Local Government Area of Imo State has been modelled by traditional time series techniques. In this work it shall be modelled by the more modern and effective seasonal autoregressive integrated moving average techniques. The realization which is analyzed spans from 2001 to 2006. The time plot of the series shows a picture of a horizontal trend and a 12-monthly seasonality. An inspection of it reveals a seasonal nature, yearly minimums all being within the first half of the year and the maximums all between the last four months of the year. This confirms the annual-period-seasonality hypothesis. The correlogram of the series also confirms the seasonality hypothesis. Even though the autocorrelations are non-significant, they follow a sinusoidal pattern of 12-month periodicity. Twelve-monthly differencing of the series yields a series with a generally horizontal trend and a correlogram which indicates seasonality of 12 month periodicity. There is also an indication of the presence of a seasonal moving average component of order one and a seasonal autoregressive component of order one. The SARIMA models which are suggestive by this autocorrelation structure are of orders: (0, 0, 0)x(1,1,1)12 and (0,0,0)x(0,1,1)12 . A comparison reveals that the former model is less adequate than the latter. Hence, the adoption of a SARIMA(0,0,0)x(0,1,1)12 model. This might be used for the forecasting of monthly internally generated revenue of the Local Government Area for administrative and management purposes.

Suggested Citation

  • Ette Harrison Etuk & Azubuike Samuel Agbam & Pius Ubari Sibeate & Felicia Ette Etuk, 2014. "Another Look at the Time Series Modelling of Monthly Internally Generated Revenue of MBAITOLI LGA of Nigeria," International Journal of Management Sciences, Research Academy of Social Sciences, vol. 3(10), pages 838-846.
    • Handle: RePEc:rss:jnljms:v3i10p10
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