聚类分析
蒙特卡罗方法
计量经济学
一致性(知识库)
爆炸物
面板数据
股票市场
计算机科学
气泡
库存(枪支)
数学
统计
工程类
人工智能
马
化学
有机化学
并行计算
古生物学
生物
机械工程
作者
Yanbo Liu,Peter C.B. Phillips,Jun Yu
摘要
Abstract This study provides new mechanisms for identifying and estimating explosive bubbles in mixed‐root panel autoregressions with a latent group structure. A postclustering approach is employed that combines k ‐means clustering with right‐tailed panel‐data testing. Uniform consistency of the k ‐means algorithm is established. Pivotal null limit distributions of the tests are introduced. A new method is proposed to consistently estimate the number of groups. Monte Carlo simulations show that the proposed methods perform well in finite samples; and empirical applications of the proposed methods identify bubbles in the U.S. and Chinese housing markets and the U.S. stock market.
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