Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/224120 
Year of Publication: 
2019
Series/Report no.: 
Munich Discussion Paper No. 2019-3
Publisher: 
Ludwig-Maximilians-Universität München, Volkswirtschaftliche Fakultät, München
Abstract: 
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.
Subjects: 
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 of the first edition: 
Document Type: 
Working Paper

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