In this article, 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.

中文翻译：

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Oracle 在协整回归中对结构中断的有效估计

在本文中，我们提出了一种自适应组套索程序来有效地估计协整回归中的结构断裂。众所周知，组套索估计器在结构断裂设置中不是同时估计一致和模型选择一致的。因此，我们使用不同数量的断点候选者的第一步组套索估计来为第二个自适应组套索估计产生权重。我们证明参数变化是由组套索一致估计的，并表明估计的中断次数大于真实数量，但仍然足够接近它。然后，我们使用这些结果并证明如果权重是从我们的第一步估计中获得的，则自适应组套索具有预言机属性。仿真结果表明，所提出的估计器提供了预期的结果。长期美国货币需求函数的经济应用证明了这种方法的实际重要性。