Model selection in the presence of nonstationarity
非平稳情形下的模型选择AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：题名和摘要均以平稳与非平稳时间序列成分下的模型选择为核心。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
非平稳情形下的模型选择
- 研究问题
- 存在非平稳成分时，贝叶斯模型选择规则应如何调整，对平稳与非平稳成分赋予不同权重会怎样影响简约性和检验功效？
- 方法与思路
- 作者推导非平稳环境下的模型选择准则及贝叶斯规则的三种形式，比较简约性、功效和一致性，并在四类情形中进行模拟。
- 材料陈述的贡献
- 摘要称贝叶斯准则区别对待平稳和非平稳成分，在所研究框架中具有选择一致性，且模拟总体表现优于所比较的其他准则。
- 阅读关注点
- 应核对贝叶斯规则的先验与近似、三种形式的对应关系，以及简约性、原始功效和尺寸调整功效的定义及比较设计。
- 阅读边界
- 仅据所给出版社摘要，不能把模拟优势推广至任意非平稳模型；四类应用情形、完整条件及数值改善幅度均未说明。
原文摘要
This paper studies model selection methods in the presence of nonstationarity. We focus on the Bayesian model selection rule and compare it with other criteria that are frequently used in econometric practice. First, we derive each of these criteria in the presence of nonstationarity. In particular, we study the Bayesian model selection rule in detail and derive three alternative forms of it in the presence of nonstationarity. One important feature of the Bayesian model selection criterion (BMSC) is that BMSC gives different weights to the stationary and nonstationary components of a model while the other criteria do not. This feature of BMSC is a desirable property for a model selection rule in the presence of possible nonstationarity. Second, we compare these criteria with regard to parsimony and power. We have found that BMSC shows the highest parsimony, AIC is the second, and Cp and R̄2, having the same level of parsimony, are the third. With regard to power, the order is not clearly established. However, for the size adjusted power BMSC becomes dominant as the sample size increases. Without size adjustment the order in the power is exactly the opposite to that in parsimony. Also, we find that BMSC is a consistent selection rule while the other criteria are not. Third, we consider four different cases of practical interest for which BMSC with some of the other criteria is applicable. We discuss how our BMSC can be used in these cases of practical interest. Results of an extensive Monte Carlo simulation for models in these four cases show that overall the BMSC outperforms other criteria.