| Article ID: | iaor20012573 |
| Country: | United Kingdom |
| Volume: | 18 |
| Issue: | 6 |
| Start Page Number: | 411 |
| End Page Number: | 419 |
| Publication Date: | Nov 1999 |
| Journal: | International Journal of Forecasting |
| Authors: | Penzer Jeremy, Shea Brian |
| Keywords: | ARIMA processes |
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.