Abstract
A univariate structural time series model based on the traditional
decomposition into trend, seasonal and irregular components is defined. A
number of methods of computing maximum likelihood estimators are then
considered. These include direct maximization of various time domain
likelihood function. The asymptotic properties of the estimators are given
and a comparison between the various methods in terms of computational
efficiency and accuracy is made. The methods are then extended to models
with explanatory variables.
decomposition into trend, seasonal and irregular components is defined. A
number of methods of computing maximum likelihood estimators are then
considered. These include direct maximization of various time domain
likelihood function. The asymptotic properties of the estimators are given
and a comparison between the various methods in terms of computational
efficiency and accuracy is made. The methods are then extended to models
with explanatory variables.
| Original language | English |
|---|---|
| Pages (from-to) | 89-108 |
| Number of pages | 20 |
| Journal | Journal of Forecasting |
| Volume | 9 |
| Publication status | Published - 1990 |