Asymptotics of the Adaptive Elastic Net Estimation for Conditional Heteroscedastic Time Series Models

Autor:	Yuanyuan Liao, Lihong Wang
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Adaptive elastic net AR-ARCH models asymptotic normality iteratively reweighted algorithm sign consistency Statistics HA1-4737 Probabilities. Mathematical statistics QA273-280
Zdroj:	Revstat Statistical Journal, Vol 20, Iss 2 (2022)
Druh dokumentu:	article
ISSN:	1645-6726 2183-0371
DOI:	10.57805/revstat.v20i2.369
Popis:	In this paper we propose an iteratively reweighted adaptive elastic net estimation method for conditional heteroscedastic time series models. The sign consistency and the asymptotic normality of the estimator are investigated. Compared with the Lasso method, the elastic net is more efficient for autoregressive time series models, because it benefits not only from the selection of the Lasso but also from the grouping effect inherited from the ridge penalty. The Monte Carlo simulation studies based on an AR-ARCH model are reported to assess the finite-sample performance of the proposed elastic net method.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/fbe3d1c37f344214a40db5cb5f8a0736 Zobrazit plný text záznamu View record in DOAJ