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arXiv · 2211.11876

Structural Modelling of Dynamic Networks and Identifying Maximum Likelihood

Christian Gourieroux·Joann Jasiak
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Abstract

This paper considers nonlinear dynamic models where the main parameter of interest is a nonnegative matrix characterizing the network (contagion) effects. This network matrix is usually constrained either by assuming a limited number of nonzero elements (sparsity), or by considering a reduced rank approach for nonnegative matrix factorization (NMF). We follow the latter approach and develop a new probabilistic NMF method. We introduce a new Identifying Maximum Likelihood (IML) method for consistent estimation of the identified set of admissible NMF's and derive its asymptotic distribution. Moreover, we propose a maximum likelihood estimator of the parameter matrix for a given non-negative rank, derive its asymptotic distribution and the associated efficiency bound.

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Christian Gourieroux, Joann Jasiak. 2022-11-21. Structural Modelling of Dynamic Networks and Identifying Maximum Likelihood. https://arxiv.org/abs/2211.11876

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