Estimation, prediction, and interpolation for ARIMA models with missing data
R Kohn, CF Ansley - Journal of the American statistical Association, 1986 - Taylor & Francis
R Kohn, CF Ansley
Journal of the American statistical Association, 1986•Taylor & FrancisWe 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.
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.
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.
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