Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/104208
Authors: 
Schlicht, Ekkehart
Ludsteck, Johannes
Year of Publication: 
2006
Series/Report no.: 
Munich Discussion Paper 2006-12
Abstract: 
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.
Subjects: 
time-varying coefficients
adaptive estimation
random walk
Kalman filter
state-space model
JEL: 
C2
C22
C51
C52
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.