Cemmap Working Paper (CWP32/16)

Fixed-effect regressions on network data

Date: 08 August 2016
Publisher: IFS
JEL classification: C23, C55
DOI: 10.1920/wp.cem.2016.3216

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