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摘要翻译：
在经典的大跨度渐近框架下，我们对Bai和Perron(1998)分析的具有多重结构变化的线性时间序列回归模型中的变点数据提出了一类广义Laplace(GL)推断方法。GL估计器是通过积分而不是基于优化的方法定义的，它依赖于最小二乘准则函数。它被解释为一个经典的（非贝叶斯）估计量，所提出的推理方法保留了频率论的解释。与现有方法相比，该方法对变点数据的不确定性提供了更好的近似。在理论方面，对于某些输入（平滑）参数，GL估计类呈现对偶极限分布；即经典的收缩型渐近分布，或贝叶斯型渐近分布。我们提出了一种基于最高密度区域的推理方法。我们证明了它具有其他流行的替代方案所没有的吸引人的理论性质，即它是BET证明的。仿真结果表明，这些理论性质转化为更好的有限样本性能。
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英文标题：
《Generalized Laplace Inference in Multiple Change-Points Models》
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作者：
Alessandro Casini and Pierre Perron
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最新提交年份：
2021
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分类信息：
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
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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。
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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。
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英文摘要：
Under the classical long-span asymptotic framework we develop a class of Generalized Laplace (GL) inference methods for the change-point dates in a linear time series regression model with multiple structural changes analyzed in, e.g., Bai and Perron (1998). The GL estimator is defined by an integration rather than optimization-based method and relies on the least-squares criterion function. It is interpreted as a classical (non-Bayesian) estimator and the inference methods proposed retain a frequentist interpretation. This approach provides a better approximation about the uncertainty in the data of the change-points relative to existing methods. On the theoretical side, depending on some input (smoothing) parameter, the class of GL estimators exhibits a dual limiting distribution; namely, the classical shrinkage asymptotic distribution, or a Bayes-type asymptotic distribution. We propose an inference method based on Highest Density Regions using the latter distribution. We show that it has attractive theoretical properties not shared by the other popular alternatives, i.e., it is bet-proof. Simulations confirm that these theoretical properties translate to better finite-sample performance.
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PDF链接：
https://arxiv.org/pdf/1803.10871
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