This package offers an implementation of the algorithms developed by Hansen, Lunde and Nason (2011). The authors introduce the notion of a model confidence set (MCS) to account for the degree of uncertainty surrounding model selection. In analogy to confidence intervals for population parameters, the MCS methodology estimates a set of models that is expected to contain the best model(s) with a given probability.
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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