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A diagnostic criterion for approximate factor structure

Gagliardini, Patrick
Ossola, Elisa
Published in Journal of Econometrics. 2019, vol. 212, p. 503-521
Abstract We build a simple diagnostic criterion for approximate factor structure in large cross-sectional equity datasets. Given a model for asset returns with observable factors, the criterion checks whether the error terms are weakly cross-sectionally correlated or share at least one unobservable common factor. It only requires computing the largest eigenvalue of the empirical cross-sectional covariance matrix of the residuals of a large unbalanced panel. A general version of this criterion allows us to determine the number of omitted common factors. The panel data model accommodates both time-invariant and time-varying factor structures. The theory applies to random coefficient panel models with interactive fixed effects under large cross-section and time-series dimensions. The empirical analysis runs on monthly and quarterly returns for about ten thousand US stocks from January 1968 to December 2011 for several time-invariant and time-varying specifications. For monthly returns, we can choose either among time-invariant specifications with at least four financial factors, or a scaled three-factor time-varying specification. For quarterly returns, we cannot select macroeconomic models without the market factor.
Keywords Large panelApproximate factor modelAsset pricingModel selectionInteractive fixed effects
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Research group Geneva Finance Research Institute (GFRI)
(ISO format)
GAGLIARDINI, Patrick, OSSOLA, Elisa, SCAILLET, Olivier. A diagnostic criterion for approximate factor structure. In: Journal of Econometrics, 2019, vol. 212, p. 503-521. doi: 10.1016/j.jeconom.2019.06.001 https://archive-ouverte.unige.ch/unige:86121

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Deposited on : 2016-08-16

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