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recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of … context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then …
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We propose the dynamic network effect (DNE) model for the study of high-dimensional multivariate time series data … of latent stochastic network effects. The parameter-driven, nonlinear state-space model requires simulation …-section dimension is large and the network is dense. An empirical application on the spread of the COVID-19 pandemic through …
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In this paper, we study the time-varying network vector autoregression (TV-NVAR) model. Constituting an ultra …
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Quantile and least-absolute deviations (LAD) methods are popular robust statistical methods but have not generally been applied to state filtering and sequential parameter learning. This paper introduces robust state space models whose error structure coincides with quantile estimation...
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