Autoregressive Lag-Order Selection Using Conditional Saddlepoint Approximations

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Auteur(s)

Butler, Ronald

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Beschreibung

A new method for determining the lag order of the autoregressive polynomial in regression models with autocorrelated normal disturbances is proposed. It is based on a sequential testing procedure using conditional saddlepoint approximations and permits the desire for parsimony to be explicitly incorporated, unlike penalty-based model selection methods. Extensive simulation results indicate that the new method is usually competitive with, and often better than, common model selection methods.

Langue

English

Datum

2017

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