Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/224120 
Autor:innen: 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
Munich Discussion Paper No. 2019-3
Verlag: 
Ludwig-Maximilians-Universität München, Volkswirtschaftliche Fakultät, München
Zusammenfassung: 
This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. A penalized least squares estimation is linked to the GLS (Aitken) estimates of the corresponding linear model with time-invariant parameters. The VC estimator is a moments estimator that does not require the disturbances be Gaussian, but if they are, its 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 VC filter is two-sided and uses more of the available information for estimating intermediate states. Further, the VC filter has a clear descriptive interpretation.
Schlagwörter: 
time-series analysis
linear model
state-space estimation
time-varying coefficients
moments estimation
Kalman filtering
penalized least squares
JEL: 
C2
C22
C51
C52
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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