Additive Models
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Chapter/Article |
| Language: | English |
| Published: |
Aug 2014
|
| In: |
The Oxford handbook of applied nonparametric and semiparametric econometrics and statistics
Year: 2014, Pages: ? |
| DOI: | 10.1093/oxfordhb/9780199857944.013.007 |
| Subjects: | |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1093/oxfordhb/9780199857944.013.007 Verlag, Volltext: http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199857944.001.0001/oxfordhb-9780199857944-e-007 
|
| Author Notes: | Enno Mammen, Byeong U. Park, Melanie Schienle |
| Summary: | 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. |
|---|---|
| Item Description: | Gesehen am 22.01.2018 |
| Physical Description: | Online Resource |
| ISBN: | 9780199351039 |
| DOI: | 10.1093/oxfordhb/9780199857944.013.007 |