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Shrinkage estimation of high-dimensional factor models with structural instabilities
In high-dimensional factor models, both the factor loadings and the number of factors may change over time. This paper proposes a shrinkage estimator that detects and disentangles these instabilities. The new method simultaneously and consistently estimates the number of pre- and post-break factors, which liberates researchers from sequential testing and achieves uniform control of the family-wise model selection errors over an increasing number of variables. The shrinkage estimator only requires the calculation of principal components and the solution of a convex optimization problem, which makes its computation efficient and accurate. The finite sample performance of the new method is investigated in Monte Carlo simulations. In an empirical application, we study the change in factor loadings and emergence of new factors during the Great Recession.
Cite this item
Xu Cheng & Zhipeng Liao & Frank Schorfheide, Shrinkage estimation of high-dimensional factor models with structural instabilities, Federal Reserve Bank of Philadelphia, Working Papers 14-4, 01 Dec 2013.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
Keywords: Consistent Model Selection; Factor Model; Great Recession; High-dimensional Model; Large Data Sets; LASSO; Shrinkage Estimation; Structural Break
This item with handle RePEc:fip:fedpwp:14-4
is also listed on EconPapers
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