A PANEL CLUSTERING APPROACH TO ANALYZING BUBBLE BEHAVIOR
提出一种结合k均值聚类和右尾面板检验的方法，用于识别混合根面板自回归中的泡沫，并一致估计组数。蒙特卡洛模拟验证了有限样本性能，实证应用于美国和中国住房市场及美国股市的泡沫识别。
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.