Abstract and Keywords
This chapter gives an overview over smooth backfitting-type estimators in additive models. Moreover, it illustrates 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. This chapter also discusses extensions to varying coefficient models, additive models with missing observations, and the case of nonstationary covariates.
Keywords: generalized additive models, smooth backfitting estimator, Nadaraya–Watson smoother, local linear smoother, semiparametric GARCH models, varying coefficient models, simultaneous nonparametric equation models, nonstationary observations, noisy integral equ
Access to the complete content on Oxford Handbooks Online requires a subscription or purchase. Public users are able to search the site and view the abstracts and keywords for each book and chapter without a subscription.
If you have purchased a print title that contains an access token, please see the token for information about how to register your code.