Additive Models
Jeffrey S. Racine, Liangjun Su, Aman Ullah, Enno Mammen, Byeong U. Park, Melanie Schienle
Abstract
Jeffrey S. Racine, Liangjun Su, Aman Ullah, Enno Mammen, Byeong U. Park, Melanie Schienle
Abstract
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.
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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.
Key concepts: Additive model, Nonparametric statistics, Nonparametric regression, Estimator, Covariate, Econometrics, Generalized additive model, Semiparametric regression