A Panel Clustering Approach to Analyzing Bubble Behavior
Liu, Y., P. C. B. Phillips and J. Yu “A Panel Clustering Approach to Analyzing Bubble Behavior,” International Economic Review, Vol. 64, No. 4, November 2023, 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.