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Inspired by the question of identifying the start time τ of financial bubbles, we address the calibration of time series in which the inception of the latest regime of interest is unknown. By taking into account the tendency of a given model to overfit data, we introduce the Lagrange...
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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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