Sparse Networks Through Regularised Regressions

Mauro Bernardi () and Michele Costola ()
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Mauro Bernardi: University of Padova, Department of Statistical Sciences
Michele Costola: House of Finance, Goethe University Frankfurt am Main, Research Center SAFE

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 125-128 from Springer

Abstract: Abstract We propose a Bayesian approach to the problem of variable selection and shrinkage in high dimensional sparse regression models where the regularisation method is an extension of a previous LASSO. The model allows us to include a large number of institutions which improves the identification of the relationship and maintains at the same time the flexibility of the univariate framework. Furthermore, we obtain a weighted directed network since the adjacency matrix is built “row by row” using for each institutions the posterior inclusion probabilities of the other institutions in the system.

Keywords: Financial networks; Sparsity; Bayesian inference (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_23

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DOI: 10.1007/978-3-319-89824-7_23

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