标题： 连续记录拉普拉斯基于推理的结构变化模型中的断点日期推断 显示英文标题

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

摘要： 基于Casini和Perron（2018a）最近为结构变化模型推断引入的连续记录渐近框架，我们提出了一种基于拉普拉斯的方法（准贝叶斯程序）来构建结构变化日期的估计值和置信集。 它通过积分而不是基于优化的方法定义。 为了得出一个称为准后验的适当分布，需要评估最小二乘准则函数的变换。 对于给定的损失函数选择，拉普拉斯型估计量是在准后验下期望风险最小化的最小化器。 除了提供一个比普通最小二乘法更精确（更低的平均绝对误差MAE和更低的均方根误差RMSE）的替代估计值外，准后验分布还可以使用最高密度区域的概念构造渐近有效的推断。 结果表明，基于拉普拉斯的推断程序具有更低的MAE和RMSE，并且相对于传统长跨度方法，无论断裂大小是小还是大，置信集在经验覆盖率和置信集平均长度之间达到了最佳平衡。 显示英文摘要

摘要： Building upon the continuous record asymptotic framework recently introduced by Casini and Perron (2018a) 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. It is defined by an integration rather than an optimization-based method. A transformation of the least-squares 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 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 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.