A PANEL CLUSTERING APPROACH TO ANALYZING BUBBLE BEHAVIOR

Yanbo Liu, Peter Phillips and Jun Yu

International Economic Review, 2023, vol. 64, issue 4, 1347-1395

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.

Date: 2023
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https://doi.org/10.1111/iere.12647

Related works:
Working Paper: A Panel Clustering Approach to Analyzing Bubble Behavior (2022)
Working Paper: A Panel Clustering Approach to Analyzing Bubble Behavior (2022)
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