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A DECOMPOSITION ANALYSIS OF DIFFUSION OVER A LARGE NETWORK

Published online by Cambridge University Press:  11 July 2022

Kyungchul Song
Kyungchul Song*
Affiliation:
University of British Columbia
*
Address correspondence to Kyungchul Song, Vancouver School of Economics, University of British Columbia, 6000 Iona Drive, Vancouver, BC V6T 1L4, Canada; e-mail: kysong@mail.ubc.ca.
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Abstract

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Diffusion over a network refers to the phenomenon of a change of state of a cross-sectional unit in one period leading to a change of state of its neighbors in the network in the next period. One may estimate or test for diffusion by estimating a cross-sectionally aggregated correlation between neighbors over time from data. However, the estimated diffusion can be misleading if the diffusion is confounded by omitted covariates. This paper focuses on the measure of diffusion proposed by He and Song (2022, Preprint, arXiv:1812.04195v4 [stat.ME]), provides a method of decomposition analysis to measure the role of the covariates on the estimated diffusion, and develops an asymptotic inference procedure for the decomposition analysis in such a situation. This paper also presents results from a Monte Carlo study on the small sample performance of the inference procedure.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 38 , Issue 6: YALE 2018 CONFERENCE IN HONOR OF PETER C. B. PHILLIPS: PART II , December 2022 , pp. 1221 - 1252
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Footnotes

I thank Mahdi Ebrahimi Kahou for his valuable comments at the beginning of this research, and Yige Duan for excellent assistance in this research, including numerous helpful comments on this work. I also thank the Co-Editor and two anonymous referees for criticisms and suggestions. I acknowledge that this research was supported by the Social Sciences and Humanities Research Council of Canada.

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