Abstract
This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. The method calculates the conditional expectations of the coefficients, given the observations. A penalized least squares estimation is linked to the GLS (Aitken) estimates of the corresponding linear model with time-invariant parameters. The estimates are moments estimates. They do not require the disturbances to be Gaussian, but if they are, the estimates are asymptotically equivalent to maximum likelihood estimates. In contrast to Kalman filtering, no specification of an initial state or an initial covariance matrix is required. While the Kalman filter is one sided, the filter proposed here is two sided and therefore uses more of the available information for estimating intermediate states. Further, the proposed filter has a clear descriptive interpretation.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Fakultät: | Volkswirtschaft |
| Themengebiete: | 300 Sozialwissenschaften > 330 Wirtschaft |
| ISSN: | 1226-3192 |
| Sprache: | Englisch |
| Dokumenten ID: | 101802 |
| Datum der Veröffentlichung auf Open Access LMU: | 05. Jun. 2023, 15:38 |
| Letzte Änderungen: | 17. Okt. 2023, 15:09 |
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