A Decomposition Analysis of Diffusion over a Large Network
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Diffusion over a causal 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 causal neighbors 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 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.
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