标题： 基于连续记录拉普拉斯的结构变化模型中断裂日期推断 显示英文标题

标题： Continuous Record Laplace-based Inference about the Break Date in Structural Change Models

摘要： 在Casini和Perron（2017a）最近为结构变化模型中的推断引入的连续记录渐近框架基础上，我们提出了一种基于拉普拉斯的（准贝叶斯）方法，用于构建结构变化日期的估计量和置信集。 该方法依赖于一种基于积分而非优化的方法定义的拉普拉斯型估计量。 为了推导出一个适当分布，称为准后验分布，对最小二乘准则函数进行了变换。 对于给定的损失函数，拉普拉斯型估计量定义为在准后验分布下期望风险的最小化器。 除了提供一种比通常的最小二乘法更精确的估计量——具有较低的平均绝对误差（MAE）和较低的均方根误差（RMSE）之外，准后验分布可以用来利用最高密度区域的概念构建渐近有效的推断。 所提出的拉普拉斯推断方法被证明具有较低的MAE和RMSE，并且无论断裂大小是小还是大，置信集在经验覆盖概率和置信集平均长度之间达到了最佳平衡，相对于传统的长跨度方法而言。 显示英文摘要

摘要： Building upon the continuous record asymptotic framework recently introduced by Casini and Perron (2017a) for inference in structural change models, we propose a Laplace-based (Quasi-Bayes) procedure for the construction of the estimate and confidence set for the date of a structural change. The procedure relies on a Laplace-type estimator defined by an integration-based rather than an optimization-based method. A transformation of the leastsquares criterion function is evaluated in order to derive a proper distribution, referred to as the Quasi-posterior. For a given choice of a loss function, the Laplace-type estimator is defined as the minimizer of the expected risk with the expectation taken under the Quasi-posterior. Besides providing an alternative estimate that is more precise---lower mean absolute error (MAE) and lower root-mean squared error (RMSE)---than the usual least-squares one, the Quasi-posterior distribution can be used to construct asymptotically valid inference using the concept of Highest Density Region. The resulting Laplace-based inferential procedure proposed is shown to have lower MAE and RMSE, and the confidence sets strike the best balance between empirical coverage rates and average lengths of the confidence sets relative to traditional long-span methods, whether the break size is small or large.