Arbeitspapier
Non-parametric transformation regression with non-stationary data
We examine a kernel regression smoother for time series that takes account of the error correlation structure as proposed by Xiao et al. (2008). We show that this method continues to improve estimation in the case where the regressor is a unit root or near unit root process.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP16/13
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
Dependence
Efficiency
Cointegration
Non-stationarity
Non-parametric estimation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Linton, Oliver
Wang, Qiying
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2013
- DOI
-
doi:10.1920/wp.cem.2013.1613
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Linton, Oliver
- Wang, Qiying
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2013
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