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This paper studies inference on fixed effects in a linear regression model estimated from network data. We derive bounds on the variance of the fixed-effect 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-effect estimator.
Authors
Research Associate University College London and University of Oxford
Martin is an IFS Research Associate, a Fellow of the Nuffield College and a Professor in the Department of Economics at the University of Oxford.
University of Cambridge
Working Paper details
- DOI
- 10.1920/wp.cem.2016.3216
- Publisher
- IFS
Suggested citation
Jochmans, K and Weidner, M. (2016). Fixed-effect regressions on network data. London: IFS. Available at: https://ifs.org.uk/publications/fixed-effect-regressions-network-data-0 (accessed: 29 March 2024).
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