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.
Data-driven calibration of penalties for least-squares regression
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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.