Working Paper

Fixed-effect regressions on network data


Koen Jochmans, Martin Weidner

Published Date

8 August 2016


Working Paper (CWP32/16)

This paper studies inference on fixed eff ects in a linear regression model estimated from network data. We derive bounds on the variance of the fixed-e ffect estimator that uncover the importance of the smallest non-zero eigenvalue of the (normalized) Laplacian of the network and of the degree structure of the network. The eigenvalue is a measure of connectivity, with smaller values indicating less-connected networks. These bounds yield conditions for consistent estimation and convergence rates, and allow to evaluate the accuracy of first-order approximations to the variance of the fixed-eff ect estimator.

Supplement for CWP32/16

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Fixed-effect regressions on network data
Koen Jochmans, Martin Weidner