Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities

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...

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Bibliographic Details
Published in:The Review of economic studies 2016-10, Vol.83 (4 (297)), p.1511-1543
Main Authors: CHENG, XU, LIAO, ZHIPENG, SCHORFHEIDE, FRANK
Format: Article
Language:English
Subjects:
Consistent estimators
Datasets
Econometric factor models
Estimators
Forecasting models
Great Recession
Macroeconomics
Monte Carlo simulation
Preliminary estimates
Recessions
Studies
Support columns
Time series
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Summary: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.
ISSN:0034-6527
1467-937X
DOI:10.1093/restud/rdw005