Network-Constrained Covariate Coefficient and Connection Sign Estimation
Jonas Striaukas,
Martin Schumacher (),
Harald Binder and
Matthias Weber
No 2001, Working Papers on Finance from University of St. Gallen, School of Finance
Abstract:
Often, variables are linked to each other via a network. When such a network structure is known, this knowledge can be incorporated into regularized regression settings via a network penalty term. However, when the type of interaction via the network is unknown (that is, whether connections are of an activating or a repressing type), the connection signs have to be estimated simultaneously with the covariate coefficients. This can be done with an algorithm iterating a connection sign estimation step and a covariate coefficient estimation step. We develop such an algorithm and show detailed simulation results and an application forecasting event times. The algorithm performs well in a variety of settings. We also briefly describe the R-package that we developed for this purpose, which is publicly available.
Keywords: Network regression; network penalty; connection sign estimation; regularized regression (search for similar items in EconPapers)
JEL-codes: C13 C52 C53 C55 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2020-01
New Economics Papers: this item is included in nep-net
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http://ux-tauri.unisg.ch/RePEc/usg/sfwpfi/WPF-2001.pdf (application/pdf)
Related works:
Working Paper: Network constrained covariate coefficient and connection sign estimation (2018) 
Working Paper: Network constrained covariate coefficient and connection sign estimation (2018) 
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Persistent link: https://EconPapers.repec.org/RePEc:usg:sfwpfi:2020:01
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