Fixed-effect regressions on network data

No CWP32/16, CeMMAP working papers from Centre for Microdata Methods and Practice, 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

Keywords: fixed effects; graph; Laplacian; network data; variance bound (search for similar items in EconPapers)
JEL-codes: C23 C55 (search for similar items in EconPapers)
Date: 2016-08-08
New Economics Papers: this item is included in nep-ecm and nep-net
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Citations: View citations in EconPapers (4)

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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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