论文标题
在协整回归中的结构断裂有效估计
Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions
论文作者
论文摘要
在本文中，我们提出了一种自适应组套索程序，以有效地估计协调回归中的结构断裂。众所周知，组拉索估计器并非同时估计一致，并且在结构断裂设置中的模型选择一致。因此，我们使用第一步组的套件估计数量分散的断点候选物来产生重量，以进行第二次自适应组套索估计。我们证明，参数变化是通过组套索始终如一地估计的，并表明估计断裂的数量大于真实数量，但仍然足够接近它。然后，我们使用这些结果，并证明如果从我们的第一步估计中获得权重，则自适应组拉索具有甲骨文特性。仿真结果表明，所提出的估计器可提供预期的结果。长期美国货币需求功能的经济应用表明了这种方法的实际重要性。
In this paper, we propose an adaptive group lasso procedure to efficiently estimate structural breaks in cointegrating regressions. It is well-known that the group lasso estimator is not simultaneously estimation consistent and model selection consistent in structural break settings. Hence, we use a first step group lasso estimation of a diverging number of breakpoint candidates to produce weights for a second adaptive group lasso estimation. We prove that parameter changes are estimated consistently by group lasso and show that the number of estimated breaks is greater than the true number but still sufficiently close to it. Then, we use these results and prove that the adaptive group lasso has oracle properties if weights are obtained from our first step estimation. Simulation results show that the proposed estimator delivers the expected results. An economic application to the long-run US money demand function demonstrates the practical importance of this methodology.
