ONLINE - Identification through Sparsity in Factor Models: the l1-Rotation Criterion
24.09.2021 15:15 – 16:15
RESEARCH CENTER FOR STATISTICS SEMINAR / ABSTRACT
We show that sparsity in the loading matrix can solve the rotational indeterminacy in factor models, allowing a researcher to recover how individual factors relate to the observed variables. The key insight is that any rotation of a sparse loading vector will be less sparse. While a rotation criterion based on the l0-norm of the loading matrix is infeasible, we prove that a rotation criterion based on the l1-norm will consistently recover the individual loading vectors under sparsity in the loading matrix. Existing rotation criteria (e.g. the Varimax rotation, \cite{kaiser1958}) lack such theoretical guarantees.
We further show that the assumption of sparsity in the loading matrix is testable, and develop such a test. Our l1-rotation performs better than existing rotation criteria in our simulations, and we find strong evidence for the presence of local factors in two economic applications.
Lieu
Online
Organisé par
Faculté d'économie et de managementResearch Institute for Statistics and Information Science
Intervenant-e-s
Simon FREYALDENHOVEN, Federal Reserve Bank of Philadelphia, USAentrée libre
Classement
Catégorie: Séminaire