Bayesian Outlier Detection for Matrix–variate Models

Monica Billio, Roberto Casarin, Fausto Corradin and Antonio Peruzzi ()
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Roberto Casarin: European Centre for Living Technology; Venice centre in Economic and Risk Analytics
Fausto Corradin: Ca’ Foscari University of Venice
Antonio Peruzzi: Ca’ Foscari University of Venice

No 2025: 14, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: Bayes Factor (BF) is one of the tools used in Bayesian analysis for model selection. The predictive BF finds application in detecting outliers, which are relevant sources of estimation and forecast errors. An efficient framework for outlier detection is provided and purposely designed for large multidimensional datasets. Online detection and analytical tractability guarantee the procedure's efficiency. The proposed sequential Bayesian monitoring extends the univariate setup to a matrix–variate one. Prior perturbation based on power discounting is applied to obtain tractable predictive BFs. This way, computationally intensive procedures used in Bayesian Analysis are not required. The conditions leading to inconclusive responses in outlier identification are derived, and some robust approaches are proposed that exploit the predictive BF's variability to improve the standard discounting method. The effectiveness of the procedure is studied using simulated data. An illustration is provided through applications to relevant benchmark datasets from macroeconomics and finance.

Keywords: Bayesian Modelling; Bayes Factor; Sequential Model Assessment; Outliers (search for similar items in EconPapers)
JEL-codes: C11 C22 E31 F10 (search for similar items in EconPapers)
Pages: 78 pages
Date: 2025
New Economics Papers: this item is included in nep-ecm and nep-ets
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