A PANEL CLUSTERING APPROACH TO ANALYZING BUBBLE BEHAVIOR

• 成果类型: Article
• 署名作者: Liu, Yanbo; Phillips, Peter C. B.; Yu, Jun
• 署名单位: Shandong University; Yale University; University of Auckland; Singapore Management University; Singapore Management University; University of Macau; Yale University
• 刊物名称: INTERNATIONAL ECONOMIC REVIEW
• ISSN/ISSBN: 0020-6598
• DOI: 10.1111/iere.12647
• 发表日期: 2023
• 页码: 1347-1395
• 关键词: unit-root tests; limit theory; cross-section; data models; autoregressive roots; inference; return; exuberance; dependence; regression

摘要

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.