high dimensional by construction and sparse by assumption, is estimated using the Lasso. We apply this method to the …

autoregressive models. We propose using Lasso-type estimators to reduce the dimensionality to a manageable one and provide strong …

) and studies the properties of the Lasso and adaptive Lasso as estimators of this model. The parameters of the model are … finite sample properties of the Lasso by deriving upper bounds on the estimation and prediction errors that are valid with … of non zero increments grows slower than √T . By simulation experiments we investigate the properties of the Lasso and …

high dimensional by construction and sparse by assumption, is estimated using the Lasso. We apply this method to the …

autoregressive models. We propose using Lasso-type estimators to reduce the dimensionality to a manageable one and provide strong …

) and studies the properties of the Lasso and adaptive Lasso as estimators of this model. The parameters of the model are … finite sample properties of the Lasso by deriving upper bounds on the estimation and prediction errors that are valid with … of non zero increments grows slower than √T . By simulation experiments we investigate the properties of the Lasso and …

parameters with the Lasso and the adaptive Lasso. The parsimonious random walk allows the parameters to be modelled non … randomly. We characterize the finite sample properties of the Lasso by deriving upper bounds on the estimation and prediction … probability tending to one. We also provide conditions under which the adaptive Lasso is able to achieve perfectmodel selection …

parameters with the Lasso and the adaptive Lasso. The parsimonious random walk allows the parameters to be modelled non … randomly.We characterize the finite sample properties of the Lasso by deriving upper bounds on the estimation and prediction … probability tending to one.We also provide conditions under which the adaptive Lasso is able to achieve perfectmodel selection. We …

autoregressive models. We propose using Lasso-type estimators to reduce the dimensionality to a manageable one and provide strong …