arXiv 4 Sep 2023 · Econometrics
arXiv:2309.01471 · PDF · DOI · OpenAlex · Extracted main text
Network diffusion models are applicable to many socioeconomic interactions, yet network interaction is hard to observe or measure. Whenever the diffusion process is unobserved, the number of possible realizations of the latent matrix that captures agents' diffusion statuses grows exponentially with the size of network. Due to interdependencies, the log likelihood function can not be factorized in individual components. As a consequence, exact estimation of latent diffusion models with more than one round of interaction is computationally infeasible. In the present paper, I propose a trimming estimator that enables me to establish and maximize an approximate log likelihood function that almost exactly identifies the peak of the true log likelihood function whenever no more than one third of eligible agents are subject to trimming.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Banerjee, A., A. Chandrasekhar, E. Duflo, and M. Jackson (2013, Scie… (2013) The diffusion of microfinance | 0.737 | 3 | 2 | 100% |
| * | unmatched citation key * | 0.000 | 1 | 1 | 0% |
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