Model Selection in Kernel Ridge Regression
Peter Exterkate ()
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely applicable, and we recommend their use instead of the popular polynomial kernels in general settings, in which no information on the data-generating process is available.
Keywords: Nonlinear forecasting; shrinkage estimation; kernel methods; high dimensionality (search for similar items in EconPapers)
JEL-codes: C51 C53 C63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2012-10
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