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Variable selection in macroeconomic stress test: a Bayesian quantile regression approach

Author

Listed:
  • Mai Dao

    (Wichita State University)

  • Lam Nguyen

    (Economic Scenario Design Team, Citigroup)

Abstract

The key assumption in stress test scenarios is that selected risk factors are useful in predicting banks’ tail risks under severe economic conditions. We argue that high-dimensional Bayesian quantile regression models with shrinkage priors are ideal for identifying those factors. We illustrate our methods by identifying key drivers for banks with different asset sizes from a high-dimensional database. We found that leverage indicators, asset prices, and labor market measures are the best predictors of banks’ performance. The usefulness of our methods is further demonstrated by a forecast comparison between the selected variables and those used in the regulatory stress tests.

Suggested Citation

  • Mai Dao & Lam Nguyen, 2025. "Variable selection in macroeconomic stress test: a Bayesian quantile regression approach," Empirical Economics, Springer, vol. 68(3), pages 1113-1169, March.
    • Handle: RePEc:spr:empeco:v:68:y:2025:i:3:d:10.1007_s00181-024-02668-y
    DOI: 10.1007/s00181-024-02668-y
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    More about this item

    Keywords

    Bayesian inference; Quantile regression; Shrinkage priors; Macro stress testing; Systemic risk; Growth-at-risk;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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