International Economic Review

Volume 64, Issue 4, November 2023, Pages 1347-1395

A Panel Clustering Approach to Analyzing Bubble Behavior

Yanbo Liu ^a, Peter C.B. Phillips^b, Jun Yu^c

A Shandong University, China

b Yale University, USA

c Singapore Management University, Singapore

This study provides new mechanisms for identifying and estimating explosive bubbles in mixed-root panel autoregressions with a latent group structure. A post clustering 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.