Generalized estimation of missing observations in nonlinear time series model using state space representation

Abstract

The aim of the study was to formulate a Time Series Model to be used in obtaining optimal estimates of missing observations. State space models and Kalman filter were used to handle irregularly spaced data. A non-Bayesian approach where the missing values were treated as fixed parameters. Simulated AR (1) data and corresponding estimated missing values were generated using a computer programme. Values were withheld and then estimated as though they were missing. The results revealed that simple exposition of state space representation for commonly used Time Series Models can be formulated.