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Summary of "Semi-Structural Credit Gap Estimation"

Core Content

This paper introduces a semi-structural econometric approach to estimate household credit (HH credit) gaps, which are used to identify excessive credit growth that may signal financial instability. The authors argue that understanding the trend level of credit is essential to distinguish between normal and excessive credit growth, and they propose using economic theory to derive the trend equation rather than relying solely on statistical methods.

Main Views and Key Information

1. Motivation and Overview

• Excessive credit growth and leverage are key drivers of financial crises.
• Measuring what part of credit is "excessive" is unclear.
• Most empirical literature uses purely statistical approaches, which lack economic interpretation and fail to account for catch-up processes.
• Some studies attempt to estimate equilibrium credit, but challenges remain in accurately capturing the theoretical underpinnings.

2. Structural Model for HH Credit Trend

• Based on the OLG model by Eggertsson and Mehrotra (2014), with additional assumptions.
• The model incorporates demographics, institutional quality, equilibrium real interest rates, and real potential GDP.
• The trend equation for HH credit is derived using logarithmic transformations of the variables.

3. Semi-Structural Econometric Set-Up

• A state space model is used to decompose HH credit into trend and cycle components.
• The credit gap is modeled as an AR(2) process, allowing for flexible cycle dynamics.
• The model is estimated using an unobserved components framework.
• The analysis covers 12 EU countries: BE, DE, DK, ES, FI, FR, GB, IE, IT, NL, PT, SE.

4. Empirical Results for HH Credit Gaps

• Credit gaps exhibit long cycles (typically 15–25 years) with large amplitudes (±15% to ±30%).
• Signalling power is strong: the semi-structural HH credit gap provides early warning of financial crises with AUROC values up to 0.90 for pre-crisis horizons of 12–5 quarters.
• The semi-structural approach outperforms various statistical credit transformations in terms of signalling accuracy.
• The framework allows for economic interpretation of changes in credit gaps, decomposing them into driving factors such as interest rates, demographics, and institutional quality.

5. Institutional Quality Proxy

• Institutional quality is proxied using real GDP per capita.
• This proxy shows high correlation across countries and over time.
• The tightness of borrowing constraints is modeled as an S-curve function of institutional quality, capturing non-linear effects.

6. Estimated Coefficients and Shock Standard Deviations

• Coefficients for real interest rate and other variables have expected signs, indicating the model's validity.
• Shock standard deviations to the HH credit cycle range between 0.4% and 0.8%, showing moderate volatility.
• The AR(1) and AR(2) coefficients are highly significant across all countries, indicating strong persistence in the credit cycle.

Conclusion

• The semi-structural approach to estimating HH credit gaps is promising and useful for macroprudential policy.
• It provides early warning signals for financial crises and allows for economic interpretation of credit trends.
• The framework complements purely statistical measures by incorporating theoretical insights and economic fundamentals.

References

• Albuquerque, B. et al. (2015): "US household deleveraging following the Great Recession - a model-based estimate of equilibrium debt."
• Buncic, D. and M. Melecky (2014): "Equilibrium credit: The reference point for macroprudential supervisors."
• Cottarelli, C. et al. (2005): "Early birds, late risers, and sleeping beauties: Bank credit growth to the private sector in Central and Eastern Europe and in the Balkans."
• Duca, M. L. et al. (2017): "A new database for financial crises in European countries."
• Eggertsson, G. B. and N. R. Mehrotra (2014): "A Model of Secular Stagnation."
• Juselius, M. and M. Drehmann (2015): "Leverage dynamics and the real burden of debt."
• Laubach, T. and J. C. Williams (2003): "Measuring the Natural Rate of Interest."

Key Highlights

• Theoretical basis: Credit gaps are derived from a structural model grounded in economic theory.
• Cycles and amplitudes: HH credit gaps show long-term cycles (20 years on average) with large variations.
• Early warning: The semi-structural credit gap has strong signalling power for financial crises.
• Economic interpretation: The framework allows for the decomposition of credit gaps into underlying economic factors.
• Usefulness for policy: The method is valuable for macroprudential supervision, offering policy-relevant insights.