Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data
- 1 September 1986
- journal article
- research article
- Published by JSTOR in Journal of the American Statistical Association
- Vol. 81 (395) , 751
- https://doi.org/10.2307/2289007
Abstract
We show how to define and then compute efficiently the marginal likelihood of an ARIMA model with missing observations. The computation is carried out by using the univariate version of the modified Kalman filter introduced by Ansley and Kohn (1985a), which allows a partially diffuse initial state vector. We also show how to predict and interpolate missing observations and obtain the mean squared error of the estimate.Keywords
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