Penalized Adaptive Forecasting with Large Information Sets and Structural Changes

Lenka Zbonakova, Xinjue Li and Wolfgang Härdle

No 2018-039, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Abstract: 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.

Keywords: SCAD penalty; propagation-separation; adaptive window choice; multiplier bootstrap (search for similar items in EconPapers)
JEL-codes: C12 C13 C50 E47 G12 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2018039

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