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Investigating Causes of Model Instability: Properties of the Prediction Accuracy Index

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  • Ross Taplin

    (Curtin Business School, Curtin University, Kent St, Bentley, WA 6102, Australia)

Abstract

The Prediction Accuracy Index (PAI) monitors stability, defined as whether the predictive power of a model has deteriorated due to a change in the distribution of the explanatory variables since its development. This paper shows how the PAI is related to the Mahalanobis distance, an established statistic for examining high leverage observations in data. This relationship is used to explore properties of the PAI, including tools for how the PAI can be decomposed into effects due to (a) individual observations/cases, (b) individual variables, and (c) shifts in the mean of variables. Not only are these tools useful for practitioners to help determine why models fail stability, but they also have implications for auditors and regulators. In particular, reasons why models containing econometric variables may fail stability are explored and suggestions to improve model development are discussed.

Suggested Citation

  • Ross Taplin, 2023. "Investigating Causes of Model Instability: Properties of the Prediction Accuracy Index," Risks, MDPI, vol. 11(6), pages 1-15, June.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:6:p:110-:d:1166121
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    References listed on IDEAS

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    1. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
    2. Chamay Kruger & Willem Daniel Schutte & Tanja Verster, 2021. "Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions," Risks, MDPI, vol. 9(11), pages 1-26, November.
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