This paper proposes new methods of estimating missing values in time series data while comparing them with existing methods. The new methods are based on the row, column and overall averages of time series data arranged in a Buys-Ballot table with m rows and s columns. The methods assume that 1) only one value is missing at a time, 2) the trending curve may be linear, quadratic or exponential and 3) the decomposition method is either Additive or Multiplicative. The performances of the methods are assessed by comparing accuracy measures (MAE, MAPE and RMSE) computed from the deviations of estimates of the missing values from the actual values used in simulation. Results show that, under the stated assumptions, estimates from the new method based on full decomposition of a series is the best (in terms of the accuracy measures) when compared with other two new and the existing methods.

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Difference equations are derived for third order moments and cumulants for bilinear multiplicative seasonal ARlMA (O,d,O) x (I,D,l), time series model stcdizd by lwueze and Chikezie (2005). The third order~cm~llanf~structure are '' shown to be the same as the covariacce structure. The moments ( first, second and third .) and cumulants obtained z. : used for:-(i) determining uniquely the periodicity of the series, (ii) initial estimation of the model parameters, and (iii) determining uniquely the region where the parameters lie. The initial estimates are then used to obtain. least squares estimates of the parameters iteratively.. . -

In this paper, we adopt a time series approach in modeling inflation in Nig'eria using a four-decade data (1960-1999) on consumer price index.. Logarithmic transformation was used to stabilize the variation in the data. On the whole four decades, a quadratic trend was obtained. This is also true with the individual decades except in the first where a linear trend proved better than the quadratic trend. For the four-decade, the seasonal indices in the 'first and the last quarters of the year negatively affected the price index, whereas the second and the third quarters have positive influences. This pattern is consistent with the data relating to the first and third decades. Seasonal multiplicative ARlMA (p,d,q) x (P,D,Q), models were fitted to first, the four decades and then to each of the decade's data. Forecasts using the models were obtained.

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