Finite sample prediction and interpolation for ARIMA models with missing data

被引：0

作者：

Penzer, J

Shea, B

机构：

[1] Univ London London Sch Econ & Polit Sci, Dept Stat, London WC2A 2AE, England

[2] Manchester Metropolitan Univ, Dept Comp & Math, Manchester M1 5GD, Lancs, England

来源：

JOURNAL OF FORECASTING | 1999年 / 18卷 / 06期

关键词：

autoregressive moving average; missing data; smoothing; time series;

D O I：

10.1002/(SICI)1099-131X(199911)18:6<411::AID-FOR737>3.0.CO;2-D

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

A transformation which allows Cholesky decomposition to be used to evaluate the exact likelihood function of an ARIMA model with missing data has recently been suggested. This method is extended to allow calculation of finite sample predictions of future observations. The output from the exact likelihood evaluation may also be used to estimate missing series values. Copyright (C) 1999 John Wiley & Sons, Ltd.

引用

页码：411 / 419

页数：9

共 12 条

[1]

Anderson B. D. O., 1979, OPTIMAL FILTERING

[2]

ANSLEY CF, 1983, BIOMETRIKA, V70, P275, DOI 10.1093/biomet/70.1.275

[3] ALGORITHM FOR THE EXACT LIKELIHOOD OF A MIXED AUTOREGRESSIVE-MOVING AVERAGE PROCESS [J].