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A Multiplicative Seasonal Arima Model for Nigerian Unemployment Rates

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Abstract:

Time series analysis of Nigerian Unemployment Rates is done. The data used is monthly from 1948 to 2008. The time plot reveals a slightly positive trend with no clear seasonality. A multiplicative seasonal model is suggestive given seasonality that typically tends to increase with time. Seasonal differencing once produced a series with no trend nor discernible stationarity. A non-seasonal differencing of the seasonal differences yielded a series with no trend but with a correlogram revealing stationarity of order 12, a nonseasonal autoregressive component of order 3 and a seasonal moving average component of order 1. A multiplicative seasonal autoregressive integrated moving average (ARIMA) model, (3, 1, 0)x(0, 1, 1)12, is fitted to the series. It has been shown to be adequate.

Info:

Periodical:
The Bulletin of Society for Mathematical Services and Standards (Volume 3)
Pages:
46-53
Citation:
E. H. Etuk "A Multiplicative Seasonal Arima Model for Nigerian Unemployment Rates", The Bulletin of Society for Mathematical Services and Standards, Vol. 3, pp. 46-53, 2012
Online since:
Sep 2012
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References:

Box, G. E. P. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco.

Etuk, E. H. (1987). On the Selection of Autoregressive Moving Average Models. An unpublished Ph. D. Thesis, Department of Statistics, University of Ibadan, Nigeria.

Etuk, E. H. (1998). An Autoregressive Integrated Moving Average (ARIMA) Simulation Model: A Case Study. Discovery and Innovation, Volume 10, Nos. 1 & 2: pp.23-26.

Madsen, H. (2008). Time Series Analysis, Chapman & Hall/CRC, London.

Oyetunji, O. B. (1985). Inverse Autocorrelations and Moving Average Time Series Modelling. Journal of Official Statistics, Volume : pp.315-322.

Priestley, M. B. (1981). Spectral Analysis and Time Series. Academic Press, London.

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