Selecting time-series hyperparameters with the artificial jackknife
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This article proposes a generalisation of the delete-d jackknife to solve hyperparameter selection problems for time series. This novel technique is compatible with dependent data since it substitutes the jackknife removal step with a fictitious deletion, wherein observed datapoints are replaced with artificial missing values. In order to emphasise this point, I called this methodology artificial delete-d jackknife. As an illustration, it is used to regulate vector autoregressions with an elastic-net penalty on the coefficients. A software implementation, ElasticNetVAR.jl, is available on GitHub.
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