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Revisiting the Many Instruments Problem using Random Matrix Theory
We use recent results from the theory of random matrices to improve instrumental variables estimation with many instruments. In settings where the first-stage parameters are dense, we show that Ridge lowers the implicit price of a bias adjustment. This comes along with improved (finite-sample) properties in the second stage regression. Our theoretical results nest existing results on bias approximation and bias adjustment. Moreover, it extends them to settings with more instruments than observations.
Date:
8 November 2024, 14:15
Venue:
Manor Road Building, Manor Road OX1 3UQ
Venue Details:
Seminar Room C
Speaker:
Helmut Farbmacher (University of Munich)
Organising department:
Department of Economics
Part of:
Nuffield Econometrics Seminar
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Edward Valenzano
