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Penalized Adaptive Method in Forecasting with Large Information Set and Structure Change

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  • Xinjue Li
  • Lenka Zbonakova
  • Wolfgang Karl Härdle

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 coecient loadings identi ed in the longest stable subsamples over time align with the true structure changes observed throughout the market.

Suggested Citation

  • Xinjue Li & Lenka Zbonakova & Wolfgang Karl Härdle, 2017. "Penalized Adaptive Method in Forecasting with Large Information Set and Structure Change," SFB 649 Discussion Papers SFB649DP2017-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2017-023
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    SCAD penalty; propagation-separation; adaptive window choice; multiplier bootstrap; bond risk premia;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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