Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities

• 成果类型: Article
• 署名作者: Cheng, Xu; Liao, Zhipeng; Schorfheide, Frank
• 署名单位: University of Pennsylvania; University of California System; University of California Los Angeles; National Bureau of Economic Research
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdw005
• 发表日期: 2016
• 页码: 1511-1543
• 关键词: approximate factor models; principal components; number; panel

摘要

In large-scale panel data models with latent factors the number of factors and their loadings may change over time. Treating the break date as unknown, this article proposes an adaptive group-LASSO estimator that consistently determines the numbers of pre- and post-break factors and the stability of factor loadings if the number of factors is constant. We develop a cross-validation procedure to fine-tune the data-dependent LASSO penalties and show that after the number of factors has been determined, a conventional least-squares approach can be used to estimate the break date consistently. The method performs well in Monte Carlo simulations. In an empirical application, we study the change in factor loadings and the emergence of new factors in a panel of U.S. macroeconomic and financial time series during the Great Recession.