Monica Billio, Roberto Casarin, Fausto Corradin, Antonio Peruzzi
arXiv 25 Mar 2025 · Statistics — Methodology
arXiv:2503.19515 · PDF · DOI · OpenAlex · Extracted main text
Anomalies in economic and financial data -- often linked to rare yet impactful events -- are of theoretical interest, but can also severely distort inference. Although outlier-robust methodologies can be used, many researchers prefer pre-processing strategies that remove outliers. In this work, an efficient sequential Bayesian framework is proposed for outlier detection based on the predictive Bayes Factor (BF). The proposed method is specifically designed for large, multidimensional datasets and extends univariate Bayesian model outlier detection procedures to the matrix-variate setting. Leveraging power-discounted priors, tractable predictive BF are obtained, thereby avoiding computationally intensive techniques. The BF finite sample distribution, the test critical region, and robust extensions of the test are introduced by exploiting the sampling variability. The framework supports online detection with analytical tractability, ensuring both accuracy and scalability. Its effectiveness is demonstrated through simulations, and three applications to reference datasets in macroeconomics and finance are provided.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | West (1986) `Bayesian Model Monitoring', Journal of the Royal Statistical Society: Series B (Methodological) 48(1), 70–78 | 0.941 | 6 | 4 | 83% |
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| 3 | Rose (2004) `Do We Really Know that the WTO Increases Trade', American Economic Review 94(1), 98–114 | 0.737 | 4 | 3 | 50% |
| 4 | Billio, Casarin, Kaufmann \ Iacopini (2023) `Bayesian Dynamic Tensor Regression', Journal of Business and Economic Statistics 41(2), 429–439 | 0.737 | 4 | 2 | 75% |
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| 9 | Pettit (1992) `Bayes Factors for Outlier Models using the Device of Imaginary Observations', Journal of the American Statistical Association 8… | 0.644 | 2 | 2 | 100% |
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Showing the top 10 of 78 scored citations.