Characterizing correlation matrices that admit a clustered factor representation

C-Tier
Journal: Economics Letters
Year: 2023
Volume: 233
Issue: C

Authors (2)

Score contribution per author:

0.503 = (α=2.01 / 2 authors) × 0.5x C-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

The Clustered Factor (CF) model is commonly used to parametrize block correlation matrices. We show that the CF model imposes additional superfluous restrictions. This can be avoided by a different parametrization, based on the logarithmic block correlation matrix.

Technical Details

RePEc Handle
repec:eee:ecolet:v:233:y:2023:i:c:s0165176523004597
Journal Field
General
Author Count
2
Added to Database
2026-01-25