2023/09/04 by Ludovic Stephan, Stephan, L. S. Sanna
Decision Sciences · Economics, Econometrics and Finance · #Econometrics (econ.EM) #FOS: Economics and business #Innovation Diffusion and Forecasting #Innovation Policy and R&D
paper · pdf · doi:10.48550/arxiv.2309.01489
openalex publication_date 2023/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
According to standard econometric theory, Maximum Likelihood estimation (MLE) is the efficient estimation choice, however, it is not always a feasible one. In network diffusion models with unobserved signal propagation, MLE requires integrating out a large number of latent variables, which quickly becomes computationally infeasible even for moderate network sizes and time horizons. Limiting the model time horizon on the other hand entails loss of important information while approximation techniques entail a (small) error that. Searching for a viable alternative is thus potentially highly beneficial. This paper proposes two estimators specifically tailored to the network diffusion model of partially observed adoption and unobserved network diffusion.