Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/215316 
Autor:innen: 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 12920
Verlag: 
Institute of Labor Economics (IZA), Bonn
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 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 VC filter is two sided and therefore 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
C32
C51
C52
Dokumentart: 
Working Paper

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