Title: Extracting statistical factors when betas are time-varying
Authors: Hao Ma - USI Lugano and SFI (Switzerland) [presenting]
Patrick Gagliardini - University of Lugano (Switzerland)
Abstract: The focus is on identification and inference on the unobservable conditional factor space and its dimension in large unbalanced panels of asset returns. The model specification is essentially nonparametric regarding the way the loadings vary in time as functions of common shocks and individual characteristics. The number of active factors can also be time-varying as an effect of the changing macroeconomic environment. The method relies on recent proposals deploying instrumental variables in large panels with unobservable factors. It accommodates for a large dimension of the vector generating the conditioning information set by machine learning techniques. In an empirical application we infer the conditional factor space in the panel of monthly returns of individual stocks in the CRSP dataset between February 1971 and December 2017.