Fixed-effect regressions on network data

Koen Jochmans and Martin Weidner

No 32/16, CeMMAP working papers from Institute for Fiscal Studies

Abstract: 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

Date: 2016-08-08
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https://www.cemmap.ac.uk/wp-content/uploads/2020/08/CWP3216.pdf (application/pdf)

Related works:
Journal Article: Fixed‐Effect Regressions on Network Data (2019)
Working Paper: Fixed-Effect Regressions on Network Data (2019)
Working Paper: Fixed-Effect Regressions on Network Data (2019)
Working Paper: Fixed-effect regressions on network data (2019)
Working Paper: Fixed-effect regressions on network data (2018)
Working Paper: Fixed-effect regressions on network data (2017)
Working Paper: Fixed-effect regressions on network data (2017)
Working Paper: Fixed-effect regressions on network data (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:32/16

DOI: 10.1920/wp.cem.2016.3216

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