英文文献：Oracle Efficient Estimation and Forecasting with the Adaptive LASSO and the Adaptive Group LASSO in Vector Autoregressions-利用自适应套索和自适应群套索对矢量自回归进行了有效的估计和预测
英文文献作者：Anders Bredahl Kock,Laurent A.F. Callot
英文文献摘要：
We show that the adaptive Lasso (aLasso) and the adaptive group Lasso (agLasso) are oracle efficient in stationary vector autoregressions where the number of parameters per equation is smaller than the number of observations. In particular, this means that the parameters are estimated consistently at root T rate, that the truly zero parameters are classiffied as such asymptotically and that the non-zero parameters are estimated as efficiently as if only the relevant variables had been included in the model from the outset. The group adaptive Lasso differs from the adaptive Lasso by dividing the covariates into groups whose members are all relevant or all irrelevant. Both estimators have the property that they perform variable selection and estimation in one step. We evaluate the forecasting accuracy of these estimators for a large set of macroeconomic variables. The Lasso is found to be the most precise procedure overall. The adaptive and the adaptive group Lasso are less stable but mostly perform at par with the common factor models.

我们表明自适应Lasso (aLasso)和自适应组Lasso (agLasso)是有效的平稳向量自回归，其中每个方程的参数数小于观测数。特别地，这意味着参数以根T率被一致地估计，真正的零参数被如此渐进地分类，非零参数被有效地估计，仿佛只有相关的变量从一开始就被包含在模型中。群体自适应套索不同于自适应套索，它将协变量划分为成员均相关或均不相关的组。这两种估计器都具有一次性进行变量选择和估计的特点。我们评估这些估计器对大量宏观经济变量的预测精度。套索被认为是最精确的程序。自适应组和自适应组Lasso不太稳定，但大部分表现与共同因素模型相同。