Discovering Specific Common Trends in a Large Set of Disaggregates: Statistical Procedures, their Properties and an Empirical Application*
提出一种成对比较方法，从大量分解数据中找出共享共同趋势的子集，无需假设普遍性或特殊相关性结构，并应用于美国CPI的159个成分。
Abstract Macroeconomic variables are weighted averages of a large number of components. Our objective is to model and forecast all of the N components of a macro variable. The main feature of our proposal consists of discovering subsets of components that share single common trends while neither assuming pervasiveness nor imposing special restrictions on the serial or cross‐sectional idiosyncratic correlation. We adopt a pairwise approach and study its statistical properties. Our asymptotic theory works both with fixed N and T →∞ and with [ T , N ]→∞. We show that the pairwise approach can be implemented using three alternative strategies, which take into account alternative characteristics of the data generating process. The paper includes an application to the US CPI broken down into 159 components.