Develops conditions for consistent penalized quasi-likelihood model selection in affine causal processes, shows BIC inconsistency in some cases like AR(p) with ARCH errors, and adds a portmanteau goodness-of-fit test.
Sequential Model Selection Method for Nonparametric Autoregression
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abstract
In this paper for the first time the nonparametric autoregression estimation problem for the quadratic risks is considered. To this end we develop a new adaptive sequential model selection method based on the efficient sequential kernel estimators proposed by Arkoun and Pergamenshchikov (2016). Moreover, we develop a new analytical tool for general regression models to obtain the non asymptotic sharp or- acle inequalities for both usual quadratic and robust quadratic risks. Then, we show that the constructed sequential model selection proce- dure is optimal in the sense of oracle inequalities.
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math.ST 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Consistent model selection criteria and goodness-of-fit test for affine causal processes
Develops conditions for consistent penalized quasi-likelihood model selection in affine causal processes, shows BIC inconsistency in some cases like AR(p) with ARCH errors, and adds a portmanteau goodness-of-fit test.