Likelihood-ratio-based confidence sets for the timing of structural breaks
基于似然比的结构突变时点置信集AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确讨论时间序列回归中的均值与方差结构突变及突变日期推断，直接对应结构变化和时间序列。
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基于似然比的结构突变时点置信集
- 研究问题
- 研究如何为时间序列回归中的结构突变时点构造置信集，并在允许均值和方差多次突变时改善时点推断的精度。
- 方法与思路
- 作者通过反演突变日期的似然比检验构造置信集，分析临界值和预期长度，并开展模拟及美国产出与消费应用。
- 材料陈述的贡献
- 摘要称所提置信集较比较方法更精确，模拟中保持较准确覆盖率；应用中的大缓和及增长变化时点区间更短。
- 阅读关注点
- 应核对误差和回归变量条件、多重突变的处理、覆盖率与长度比较，以及引入协整关系后时点结论的敏感性。
- 阅读边界
- 仅据所给OpenAlex版本摘要，无法核实覆盖率和区间长度的具体数值及适用范围；不推断其他版本或刊本变化。
原文摘要
We propose the use of likelihood-ratio-based confidence sets for the timing of structural breaks in parameters from time series regression models. The confidence sets are valid for the broad setting of a system of multivariate linear regression equations under fairly general assumptions about the error and regressors, and allowing for multiple breaks in mean and variance parameters. In our asymptotic analysis, we determine the critical values for a likelihood ratio test of a break date and the expected length of a confidence set constructed by inverting the likelihood ratio test. Notably, the likelihood-ratio-based confidence sets are more precise than other confidence sets considered in the literature. Monte Carlo analysis confirms their greater precision in finite samples, including in terms of maintaining accurate coverage even when the sample size or magnitude of a break is small. An application to postwar U.S. real gross domestic product and consumption leads to a shorter 95% confidence set for the timing of the “Great Moderation” in the mid-1980s than previously found in the literature. Furthermore, when taking co-integration between output and consumption into account, confidence sets for structural break dates become even shorter and suggest a “productivity growth slowdown” in the early 1970s and an additional large, abrupt decline in long-run growth in the mid-1990s.