LASSO order selection for sparse autoregression: a bootstrap approach

ABSTRACT

Autoregressive models are widely employed for predictions and other inferences in many scientific fields. While the determination of their order is in general a difficult and critical step, this task becomes more complicated and crucial when the time series under investigation is realization of a stochastic process characterized by sparsity. In this paper we present a method for order determination of a stationary AR model with a sparse structure, given a set of observations, based upon a bootstrapped version of MAICE procedure [Akaike H. Prediction and entropy. Springer; 1998], in conjunction with a LASSO-type constraining procedure for lag suppression of insignificant lags. Empirical results will be obtained via Monte Carlo simulations. The quality of our method is assessed by comparison with the commonly adopted cross-validation approach and the non bootstrap counterpart of the presented procedure.

KEYWORDS:

• AR processes
• bootstrap
• AIC
• order selection
• moving block bootstrap

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Part of the computation has been performed on the server ‘Pythagoras’, maintained by the Department of Mathematics of the University of California San Diego.