[Statlist] Research Webinar in Statistics *FRIDAY 24 SEPTEMBER 2021* GSEM, University of Geneva

[gsem-support-instituts]

Dear All,

We are pleased to invite you to our next Research Webinar.

Looking forward to seeing you


Organizers :                                                                                   
E. Cantoni - S. Engelke - D. La Vecchia - E. Ronchetti
S. Sperlich - F. Trojani - M.-P. Victoria-Feser


FRIDAY 24 SEPTEMBER 2021 at 15:15
ONLINE
Zoom research webinar: https://unige.zoom.us/j/92924332087?pwd=U1U1NFk4dTFCRHBMeWYrSDBQcXBiQT09
Meeting ID: 929 2433 2087
Passcode: 399192


Identification through Sparsity in Factor Models: the l1-Rotation Criterion
Simon FREYALDENHOVEN (https://simonfreyaldenhoven.github.io/), Federal Reserve Bank of Philadelphia, USA

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.

Visit the website: https://www.unige.ch/gsem/en/research/seminars/rcs/