Volume 2, Issue 2, March 2013, Page: 21-28
Generalized Estimation of Missing Observations in Nonlinear Time Series Model Using State Space Representation
Biwott K. Daniel, Maseno University,Department of Statistics and Actuarial Science, Kenya
Odongo O. Leo, Kenyatta University,Department of Statistics, Kenya
Received: Mar. 9, 2013;       Published: Apr. 2, 2013
DOI: 10.11648/j.ajtas.20130202.13      View  2593      Downloads  90
Abstract
The aim of the study was to formulate a Time Series Model to be used in obtaining optimal estimates of miss-ing observations. State space models and Kalman filter were used to handle irregularly spaced data. A non-Bayesian ap-proach 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.
Keywords
Model, Linear, Non-Linear, Simulated, Non-Bayesian
To cite this article
Biwott K. Daniel, Odongo O. Leo, Generalized Estimation of Missing Observations in Nonlinear Time Series Model Using State Space Representation, American Journal of Theoretical and Applied Statistics. Vol. 2, No. 2, 2013, pp. 21-28. doi: 10.11648/j.ajtas.20130202.13
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