Variable selection in macroeconomic stress test
A Bayesian quantile regression approach
Bibliographic Data
| ID | 21542069 |
|---|---|
| Authors | Mai Dao (0000-0003-1727-0624, Wichita State University), Lam Nguyen (0000-0003-3347-4379, Citigroup, corresponding author) |
| Year | 2025 |
| Volume | 68 |
| Issue | 3 |
| Pages | 1113-1169 |
| Publication date | 2025-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Empirical Economics (JOURNAL) |
| Journal identifiers | ISSN: 0377-7332 • E-ISSN: 1435-8921 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00181-024-02668-y |
| OpenAlex | W4403877832 |
| Language | EN |
| References cited | 42 |
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
Bayesian probability · Econometrics · Economics · Feature selection · Quantile regression · Regression · Regression testing · Statistics · Advanced Statistical Methods and Models · Computer Science · Market Dynamics and Volatility · Mathematics · Statistical Methods and Inference · Artificial Intelligence
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| Highly cited | No |
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