Arbeitspapier
Variance Estimation in a Random Coefficients Model
This papers describes an estimator for a standard state-space model with coefficients generated by a random walk that is statistically superior to the Kalman filter as applied to this particular class of models. Two closely related estimators for the variances are introduced: A maximum likelihood estimator and a moments estimator that builds on the idea that some moments are equalized to their expectations. These estimators perform quite similar in many cases. In some cases, however, the moments estimator is preferable both to the proposed likelihood estimator and the Kalman filter, as implemented in the program package Eviews.
- Sprache
-
Englisch
- Erschienen in
-
Series: Munich Discussion Paper ; No. 2006-12
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Model Construction and Estimation
Model Evaluation, Validation, and Selection
- Thema
-
time-varying coefficients
adaptive estimation
random walk
Kalman filter
state-space model
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Schlicht, Ekkehart
Ludsteck, Johannes
- Ereignis
-
Veröffentlichung
- (wer)
-
Ludwig-Maximilians-Universität München, Volkswirtschaftliche Fakultät
- (wo)
-
München
- (wann)
-
2006
- DOI
-
doi:10.5282/ubm/epub.904
- Handle
- URN
-
urn:nbn:de:bvb:19-epub-904-9
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Schlicht, Ekkehart
- Ludsteck, Johannes
- Ludwig-Maximilians-Universität München, Volkswirtschaftliche Fakultät
Entstanden
- 2006
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