Arbeitspapier
Inference on a semiparametric model with global power law and local nonparametric trends
This paper studies a model with both a parametric global trend and a nonparametric local trend. This model may be of interest in a number of applications in economics, finance, ecology, and geology. The model nests the parametric global trend model considered in Phillips (2007) and Robinson (2012), and the nonparametric local trend model. We first propose two hypothesis tests to detect whether either of the special cases are appropriate. For the case where both null hypotheses are rejected, we propose an estimation method to capture both aspects of the time trend. We establish consistency and some distribution theory in the presence of a large sample. Moreover, we examine the proposed hypothesis tests and estimation methods through both simulated and real data examples. Finally, we discuss some potential extensions and issues when modelling time effects.
- Language
-
Englisch
- Bibliographic citation
-
Series: cemmap working paper ; No. CWP05/18
- Classification
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Climate; Natural Disasters and Their Management; Global Warming
- Subject
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Global Mean Sea Level
Nonparametric Kernel Estimation
Nonstationarity
- Event
-
Geistige Schöpfung
- (who)
-
Gao, Jiti
Linton, Oliver
Peng, Bin
- 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)
-
2018
- DOI
-
doi:10.1920/wp.cem.2018.0518
- Handle
- Last update
-
10.03.2025, 11:43 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
- Gao, Jiti
- Linton, Oliver
- Peng, Bin
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2018
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