542 research outputs found
Bayesian dynamic financial networks with time-varying predictors
We propose a Bayesian nonparametric model including time-varying predictors
in dynamic network inference. The model is applied to infer the dependence
structure among financial markets during the global financial crisis,
estimating effects of verbal and material cooperation efforts. We interestingly
learn contagion effects, with increasing influence of verbal relations during
the financial crisis and opposite results during the United States housing
bubble
Nonparametric Bayes dynamic modeling of relational data
Symmetric binary matrices representing relations among entities are commonly
collected in many areas. Our focus is on dynamically evolving binary relational
matrices, with interest being in inference on the relationship structure and
prediction. We propose a nonparametric Bayesian dynamic model, which reduces
dimensionality in characterizing the binary matrix through a lower-dimensional
latent space representation, with the latent coordinates evolving in continuous
time via Gaussian processes. By using a logistic mapping function from the
probability matrix space to the latent relational space, we obtain a flexible
and computational tractable formulation. Employing P\`olya-Gamma data
augmentation, an efficient Gibbs sampler is developed for posterior
computation, with the dimension of the latent space automatically inferred. We
provide some theoretical results on flexibility of the model, and illustrate
performance via simulation experiments. We also consider an application to
co-movements in world financial markets
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