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Network-Constrained Covariate Coefficient and Connection Sign Estimation

Author

Listed:
  • Jonas Striaukas
  • Martin Schumacher
  • Harald Binder
  • Matthias Weber

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.

Suggested Citation

  • Jonas Striaukas & Martin Schumacher & Harald Binder & Matthias Weber, 2020. "Network-Constrained Covariate Coefficient and Connection Sign Estimation," Working Papers on Finance 2001, University of St. Gallen, School of Finance.
    • Handle: RePEc:usg:sfwpfi:2020:01
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    Keywords

    Network regression; network penalty; connection sign estimation; regularized regression;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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