Penalized adaptive method in forecasting with large information set and structure change

Xinjue Li, Lenka Zbonakova and Wolfgang Härdle

No 2017-023, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: In the present paper we propose a new method, the Penalized Adaptive Method (PAM), for a data driven detection of structure 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 help of penalized regression models. The method is simple yet exible and can be safely applied in high-dimensional cases with di erent sources of parameter changes. Comparing with the adaptive method in linear models, its combination with dimension reduction yields a method which selects proper signi cant variables and detects structure breaks while steadily reduces the forecast error in high-dimensional data. When applying PAM to bond risk premia modelling, the locally selected variables and their estimated coefficient loadings identified the longest stable subsamples over time align with the true structure changes observed throughout the market.

Keywords: SCAD penalty; propagation-separation; adaptive window choice; multiplier bootstrap; bond risk premia (search for similar items in EconPapers)
JEL-codes: C13 C20 E37 (search for similar items in EconPapers)
Date: 2017
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