Data Tables

All CSV data from the BIS Framework analysis

Signal Extraction Results

7 Threshold Levels (2-14 pp) | Highlight: 4 pp (BIS optimal)
thresholdtrue_positivesfalse_positivestrue_negativesfalse_negativessensitivityspecificityprecisiontype1_errortype2_errornoise_to_signalusefulness
2.0000113.01457.02098.051.00000.68900.59020.07200.40980.31100.59480.1396
4.0000105.01146.02409.059.00000.64020.67760.08390.32240.35980.50350.1589
6.000089.0000877.02678.075.00000.54270.75330.09210.24670.45730.45460.1480
8.000074.0000640.02915.090.00000.45120.82000.10360.18000.54880.39900.1356
10.000062.0000479.03076.0102.00.37800.86530.11460.13470.62200.35640.1217
12.000047.0000362.03193.0117.00.28660.89820.11490.10180.71340.35530.0924
14.000037.0000260.03295.0127.00.22560.92690.12460.07310.77440.32420.0762

Machine Learning Model Comparison

5 Models | Highlight: Logistic Regression (Best)
ModelAUC-ROCPrecisionRecallF1 ScoreBrier ScoreCV AUC (mean)CV AUC (std)
Logistic Regression0.69450.08320.94640.15300.1918--
Gradient Boosting0.55860.06220.91070.11640.0821--
XGBoost0.53960.06280.98210.11800.08820.44270.1374
LightGBM0.51710.06090.96430.11450.09310.47890.0633
Random Forest0.50440.05710.89290.10740.1239--

Feature Importance

12 Engineered Features Ranked by Importance
FeatureLogistic RegressionRandom ForestGradient BoostingXGBoostLightGBMAverage
gap_lag80.25310.11450.14710.097930561.1225
gap_zscore1.15750.14470.21090.113828857.9254
gap_vol_8q0.23480.09770.13040.090628056.1107
gap_change_4q0.26040.10520.10020.081626553.1095
gap_lag40.26310.10290.07260.091226252.5060
credit_gap0.36700.09140.09070.105319238.5309
gap_vol_4q0.10230.05960.05840.065814128.2572
gap_lag20.26650.08620.09000.107513126.3100
gap_lag10.32550.09770.03690.076610921.9073
gap_accel_1q0.07830.02420.01960.04579218.4336
gap_from_max_4q0.13710.05080.01940.07358817.6561
gap_change_1q0.25830.02510.02400.05058517.0716

Lead Time Analysis

Early Warning Performance at Different Horizons
lead_quarterslead_yearsoptimal_thresholdsensitivityspecificitytype1_errornoise_to_signalusefulness
4.00001.00004.00000.71430.66940.33060.46280.1918
8.00002.00004.00000.66070.67370.32630.49390.1672
12.00003.00004.00000.64020.67760.32240.50350.1589
16.00004.00004.00000.60850.68010.31990.52580.1443

Country Statistics

Descriptive Statistics by Country
countrycountmeanstdminmaxmedianq25q75skewnesskurtosisjarque_bera_statjarque_bera_pvalueis_normal
AT219-1.55894.9546-14.30008.9000-1.3000-4.60001.7500-0.3056-0.08193.46690.1767True
AU221-0.75169.9930-27.700018.00000.5000-8.70005.3000-0.3616-0.11084.91870.0855True
BR783.45514.0139-3.600013.40003.0500-0.07506.57500.3489-0.82333.85580.1455True
CA2221.53657.9596-19.000019.70003.0000-3.07506.9000-0.55130.252411.54000.0031False
CH2191.797310.3666-25.700029.40003.2000-7.150010.1500-0.0283-0.70214.67310.0967True

Panel Data

3,719 Observations | 19 Countries | Paginated (100 rows/page)
Page: 1 / 38
country_codedatecredit_gapyearquarterborrower_sectorsector_description

Literature References

200 Papers from OpenAlex
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