Arbeitspapier
Nonparametric regression for locally stationary time series
In this paper, we study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models are a natural extension of time series models with time-varying coefficients. We introduce a kernel-based method to estimate the time-varying regression function and provide asymptotic theory for our estimates. Moreover, we show that the main conditions of the theory are satis ed for a large class of nonlinear autoregressive processes with a time-varying regression function. Finally, we examine structured models where the regression function splits up into time-varying additive components. As will be seen, estimation in these models does not su er from the curse of dimensionality. We complement the technical analysis of the paper by an application to financial data.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP22/12
- Classification
-
Wirtschaft
- Subject
-
local stationarity
nonparametric regression
smooth backfitting
- Event
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Geistige Schöpfung
- (who)
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Vogt, Michael
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2012
- DOI
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doi:10.1920/wp.cem.2012.2212
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Vogt, Michael
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2012
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