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http://dx.doi.org/10.5351/KJAS.2019.32.6.909

Model selection for unstable AR process via the adaptive LASSO  

Na, Okyoung (Department of Applied Statistics, Kyonggi University)
Publication Information
The Korean Journal of Applied Statistics / v.32, no.6, 2019 , pp. 909-922 More about this Journal
Abstract
In this paper, we study the adaptive least absolute shrinkage and selection operator (LASSO) for the unstable autoregressive (AR) model. To identify the existence of the unit root, we apply the adaptive LASSO to the augmented Dickey-Fuller regression model, not the original AR model. We illustrate our method with simulations and a real data analysis. Simulation results show that the adaptive LASSO obtained by minimizing the Bayesian information criterion selects the order of the autoregressive model as well as the degree of differencing with high accuracy.
Keywords
AR model; unit-root; order selection; adaptive LASSO;
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