Subset Selection for Vector Autoregressive Processes Using Lasso

doi:10.1016/j.csda.2007.12.004

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/69242

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题名:	Subset Selection for Vector Autoregressive Processes Using Lasso
作者:	Hsu, Nan-jung;Hung, Hung-lin;Chang, Ya-mei
贡献者:	淡江大學統計學系
日期:	2008-03-15
上传时间:	2011-10-23 16:42:47 (UTC+8)
出版者:	Elsevier
摘要:	A subset selection method is proposed for vector autoregressive (VAR) processes using the Lasso [Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B 58, 267–288] technique. Simply speaking, Lasso is a shrinkage method in a regression setup which selects the model and estimates the parameters simultaneously. Compared to the conventional information-based methods such as AIC and BIC, the Lasso approach avoids computationally intensive and exhaustive search. On the other hand, compared to the existing subset selection methods with parameter constraints such as the top-down and bottom-up strategies, the Lasso method is computationally efficient and its result is robust to the order of series included in the autoregressive model. We derive the asymptotic theorem for the Lasso estimator under VAR processes. Simulation results demonstrate that the Lasso method performs better than several conventional subset selection methods for small samples in terms of prediction mean squared errors and estimation errors under various settings. The methodology is applied to modeling U.S. macroeconomic data for illustration.
關聯:	Computational Statistics and Data Analysis 52(7), pp.3645-3657
DOI:	10.1016/j.csda.2007.12.004
显示于类别:	[統計學系暨研究所] 期刊論文

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