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

Kelechukwu C. N. Dozie

Department of Statistics, Imo State University, Owerri, Imo State, Nigeria.

Eleazar C. Nwogu

Department of Statistics, Federal University of Technology, Owerri, Imo State, Nigeria.

Maxwell A. Ijomah *

Department of Mathematics and Statistics, University of Port Harcourt, Port Harcourt, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

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.

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


How to Cite

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

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