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A diagnostic criterion for approximate factor structure
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DOI：10.1016/j.jeconom.2019.06.001.png)
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Abstract
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We build a simple diagnostic criterion for approximate factor structure in large panel datasets. Given observable factors, the criterion checks whether the errors are weakly cross-sectionally correlated or share at least one unobservable common factor (interactive effects). A general version allows to determine the number of omitted common factors also for time-varying structures. The empirical analysis runs on ten thousand US stocks from January 1968 to December 2011. For monthly returns, we select time invariant specifications with at least four financial factors, and a scaled three-factor specification. For quarterly returns, we cannot select macroeconomic models without the market factor. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Large panel
Approximate factor model
Asset pricing
Model selection
Interactive fixed effects
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