Science and Education Publishing American Journal of Applied Mathematics and Statistics 2328-7292 3 5 2015 10 21 Imputation of Missing Values for Pure Bilinear Time Series Models with Normally Distributed Innovations 199 205 EN Poti Owili Abaja Mathematics and Computer Science Department, Laikipia University, Nyahururu, Kenya Dankit Nassiuma Luke Orawo AJAMS2015354 10.12691/ajams-3-5-4 2015 5 2 2015 8 9 2015 10 19 In this study, estimates of missing values for bilinear time series models with normally distributed innovations were derived by minimizing the h-steps-ahead dispersion error. For comparison purposes, missing value estimates based on artificial neural network (ANN) and exponential smoothing (EXP) techniques were also obtained. Simulated data was used in the study. 100 samples of size 500 each were generated for different pure bilinear time series models using the R-statistical software. In each sample, artificial missing observations were created at data positions 48, 293 and 496 and estimated using these methods. The performance criteria used to ascertain the efficiency of these estimates were the mean absolute deviation (MAD) and mean squared error (MSE). The study found that optimal linear estimates were the most efficient estimates for estimating missing values. Further, the optimal linear estimates were equivalent to one step-ahead forecast of the missing value. The study recommends OLE estimates for estimating missing values for pure bilinear time series data with normally distributed innovations.