Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/79587 
Year of Publication: 
2012
Series/Report no.: 
SFB 649 Discussion Paper No. 2012-045
Publisher: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
Abstract: 
We give an overview over smooth back tting type estimators in additive models. Moreover we illustrate their wide applicability in models closely related to additive models such as nonparametric regression with dependent error variables where the errors can be transformed to white noise by a linear transformation, nonparametric regression with repeatedly measured data, nonparametric panels with fixed effects, simultaneous nonparametric equation models, and non- and semiparametric autoregression and GARCH-models. We also discuss extensions to varying coeffcient models, additive models with missing observations, and the case of nonstationary covariates.
Subjects: 
smooth backfi tting
additive models
JEL: 
C14
C30
Document Type: 
Working Paper

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