Fixed-effect regressions on network data

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP32/16

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models ; Large Data Sets: Modeling and Analysis
• Subject: fixed effects ; graph ; Laplacian ; network data ; variance bound

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Jochmans, Koen ; Weidner, Martin
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2016

Further information

• DOI: doi:10.1920/wp.cem.2016.3216
• Handle: http://hdl.handle.net/10419/149778
• Last update: 10.03.2025, 11:44 AM CET