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Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data

 

作者: Robert Kohn,   CraigF. Ansley,  

 

期刊: Journal of the American Statistical Association  （Taylor Available online 1986）
卷期: Volume 81, issue 395  

页码: 751-761

 

ISSN:0162-1459

 

年代: 1986

 

DOI:10.1080/01621459.1986.10478332

 

出版商: Taylor & Francis Group

 

关键词: Diffuse initial conditions;Kalman filter;Maximum likelihood;Smoothing;State space

 

数据来源: Taylor

 

摘要:

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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