Asymptotic Normality of the Encompassing Test Associated to the linear Parametric Modelling and the Kernel Method for α-Mixing Processes

This paper contributes on model selection between parametric and nonparametric methods through the use of encompassing test. We provide asymptotic normality of encompassing statistic associated to the encompassing hypothesis for parametric and nonparametric regres-
sion methods. We develop various results on this test for more general processes satisfying several dependence structures.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.