Effect of Missing Observations on Buys-Ballot Estimates of Time Series Components

Main Article Content

Kelechukwu C. N. Dozie
Eleazar C. Nwogu
Maxwell A. Ijomah


This study discusses the effects of missing observations on Buys-Ballot estimate when trend-cycle component of time series is linear. The method adopted in this study is Decomposing Without the Missing Value (DWMV) which is used to estimate missing observations in time series decomposition when data are arranged in a Buys-Ballot table. The model structure used is multiplicative. Results show that the trend parameters with and without missing observations have insignificant effect while there are significant differences in the seasonal indices only at the season points where missing observations occurred in the Buys-Ballot table.

Time series decomposition, missing observation, trend parameter, seasonal effect, multiplicative model, buys-ballot table.

Article Details

How to Cite
N. Dozie, K. C., C. Nwogu, E., & A. Ijomah, M. (2020). Effect of Missing Observations on Buys-Ballot Estimates of Time Series Components. Asian Journal of Probability and Statistics, 6(3), 13-24. https://doi.org/10.9734/ajpas/2020/v6i330161
Original Research Article


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