期刊
REVIEW OF ECONOMIC STUDIES
卷 83, 期 4, 页码 1511-1543出版社
OXFORD UNIV PRESS
DOI: 10.1093/restud/rdw005
关键词
Great Recession; High-dimensional model; Large data sets; LASSO; Latent factor model; Model selection; Shrinkage estimation; Structural break
类别
资金
- National Science Foundation [SES 1061725]
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.
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