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SUMMARY:A Diagnostic Criterion For Approximate Factor Structure
DTSTART:20160412T121500
DTEND:20160412T131500
DTSTAMP:20260408T103707Z
UID:24bea2bde3e7806c3793f47ef95eccb8e681b4a7576e16020aca147f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Patrick GAGLIARDINI (USI\, University of Lugano)\nWe build a s
 imple diagnostic criterion for approximate factor structure in large cross
 -sectional equity datasets. Given a model for asset returns with observabl
 e  factors\, the criterion checks whether the error terms are weakly cros
 s-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 o
 mitted common factors. We also explain how to compute p-values via sample 
 conditioning and randomization. The panel data model accommodates both tim
 e-invariant and time-varying factor structures. The theory applies to gene
 ric random coefficient panel models under large  crosssection and time-se
 ries dimensions. The empirical analysis runs on monthly returns for about 
 ten thousand US stocks from January 1968 to December 2011 for several time
 -varying specifications. Among several multi-factor time-invariant models 
 proposed in the literature\, we cannot select a model with zero factors in
  the errors. On the opposite\, we conclude for no omitted factor structure
  in the errors for several time-varying specifications.
LOCATION:UNIL\, Extranef\, room 126 https://planete.unil.ch/plan/?local=EX
 T-126
STATUS:CONFIRMED
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