摘要翻译：
在Casini和Perron(2018a)最近提出的用于结构变化模型推理的连续记录渐近框架的基础上，我们提出了一个基于Laplace的（准Bayes）过程来构造结构变化日期的估计和置信度集。它是通过集成而不是基于优化的方法来定义的。对最小二乘准则函数的变换进行了估计，以便得到一个适当的分布，称为准后验分布。对于给定的损失函数选择，Laplace型估计量是期望风险的最小值，其期望取在准后验下。除了提供比通常的最小二乘估计更精确的平均绝对误差(MAE)和更低的均方根误差(RMSE)外，准后验分布还可以利用最高密度区域的概念构造渐近有效的推断。结果表明，与传统的大跨度方法相比，基于拉普拉斯的推理过程具有较低的MAE和RMSE,并且无论断口大小，置信集都在经验复盖率和置信集平均长度之间取得了最佳平衡。
---
英文标题：
《Continuous Record Laplace-based Inference about the Break Date in
  Structural Change Models》
---
作者：
Alessandro Casini and Pierre Perron
---
最新提交年份：
2020
---
分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
--
一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
--

---
英文摘要：
  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.
---
PDF链接：
https://arxiv.org/pdf/1804.00232