Fitting State-space Model for Long-term Prediction of the Log-likelihood of Nonstationary Time Series Models

Abstract

The goodness of the long-term prediction in the state-space model was evaluated using the squared long-term prediction error. In order to estimate the model parameters suitable for long-term prediction, we devised a modified log-likelihood corresponding to the long-term prediction error variance. Trend models and seasonally adjusted models with and without AR component are examined as examples.