We consider network autoregressive models for count data with a non-random neighborhood structure. The main methodological contribution is the development of conditions that guarantee stability and valid statistical inference for such models. We consider both cases of fixed and increasing network dimension and we show that quasi-likelihood inference provides consistent and asymptotically normally distributed estimators. The article is complemented by simulation results and a data example.
Armillotta, M., Fokianos, K. (2024). Count network autoregression. JOURNAL OF TIME SERIES ANALYSIS, 45(4), 584-612 [10.1111/jtsa.12728].
Count network autoregression
Mirko Armillotta;
2024-01-01
Abstract
We consider network autoregressive models for count data with a non-random neighborhood structure. The main methodological contribution is the development of conditions that guarantee stability and valid statistical inference for such models. We consider both cases of fixed and increasing network dimension and we show that quasi-likelihood inference provides consistent and asymptotically normally distributed estimators. The article is complemented by simulation results and a data example.| File | Dimensione | Formato | |
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Journal Time Series Analysis - 2023 - Armillotta - Count network autoregression.pdf
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