Testing for the number of latent factors in short panel
The DEMS Economics & Statistics Seminar series is proud to host
Patrick Gagliardini
(University of Lugano)
Abstract: We develop inferential tools for latent factor analysis in short panels. The pseudo maximum likelihood setting under a large cross-sectional dimension n and a fixed time series dimension T relies on a diagonal T × T covariance matrix of the errors without imposing sphericity or Gaussianity. We characterize the asymptotic distributions of the latent factor and error covariance estimates as well as of an asymptotically uniformly most powerful invariant test based on the likelihood ratio statistic for tests of the number of factors. An empirical application to a large panel of U.S. stock returns separates systematic and idiosyncratic risks in short subperiods of bear vs. bull market based on the selected number of factors. We observe an uptrend in idiosyncratic volatility while the systematic risk explains a large part of the cross-sectional total variance in bear markets but is not driven by a single factor. We also find that observed factors, scaled or not, struggle spanning latent factors.
(with A.P. Fortin and O. Scaillet)
The seminar will be in person: room 13, Building U7