Arbeitspapier
Penalized Adaptive Forecasting with Large Information Sets and Structural Changes
In the present paper we propose a new method, the Penalized Adaptive Method (PAM), for a data driven detection of structural changes in sparse linear models. The method is able to allocate the longest homogeneous intervals over the data sample and simultaneously choose the most proper variables with the help of penalized regression models. The method is simple yet exible and can be safely applied in high-dimensional cases with dierent sources of parameter changes. Comparing with the adaptive method in linear models, its combination with dimension reduction yields a method which properly selects signicant variables and detects structural breaks while steadily reduces the forecast error in high-dimensional data.
- Language
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Englisch
- Bibliographic citation
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Series: IRTG 1792 Discussion Paper ; No. 2018-039
- Classification
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Wirtschaft
Hypothesis Testing: General
Estimation: General
Econometric Modeling: General
Money and Interest Rates: Forecasting and Simulation: Models and Applications
Asset Pricing; Trading Volume; Bond Interest Rates
- Subject
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SCAD penalty
propagation-separation
adaptive window choice
multiplier bootstrap
- Event
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Geistige Schöpfung
- (who)
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Zbonakova, Lenka
Li, Xinjue
Härdle, Wolfgang Karl
- Event
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Veröffentlichung
- (who)
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Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (where)
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Berlin
- (when)
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2018
- Handle
- Last update
-
02.08.0484, 12:44 PM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the 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
- Zbonakova, Lenka
- Li, Xinjue
- Härdle, Wolfgang Karl
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Time of origin
- 2018
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Model confidence sets for forecasting models
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