## EXECUTIVE SUMMARY

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**Canonical URL:** [EXECUTIVE SUMMARY](https://www.imf.org/-/media/files/publications/tnm/2017/tnm1709.pdf)

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---

### Purpose and scope
- Toolkit developed to assess consistency between real sector and financial sector forecasts within the IMF’s Financial Programming and Policies (FPP) framework.
- Empirical basis: cross-country historical data for 182 economies from 1980 to 2015.
- Financial conditioning variables considered: credit growth, house prices, financial account balance. Economic cycle measure: output gap.
- Methodology: model-free, empirical joint distributions; deliberately simple to facilitate implementation in the FPP framework.

### Key methodological details
- Macro variables:
  - GDP, private consumption, private investment, employment, CPI inflation (annual, real series deflated by GDP deflator).
- Financial variables:
  - Credit: “claims on the private sector by deposit money banks” (annual growth, real terms).
  - House prices: OECD, BIS, and IMF Real Estate Module (real house price index, annual growth).
  - Financial account: net acquisition of financial assets, normalized by current year GDP in US dollars.
- Data cleaning and transforms:
  - Winsorize all macroeconomic and financial variables at the 2 percent and 98 percent levels.
  - Output gap: percentage deviation of real GDP from potential; potential estimated with HP filter with smoothing factor of 6.25.
- Conditional distribution estimation:
  - Estimate inverse CDF for each conditioning variable at each quintile.
  - Estimate CDFs of real variables conditional on single and double conditioning (credit alone; credit + financial account; credit + house prices; credit + output gap).
  - Toolkit reports threshold values at the 10th, 25th, 75th, and 90th percentiles.
  - Flagging rules: values below the 10th percentile or above the 90th percentile trigger a red flag; values between the 10th and 25th percentile, or between the 75th and 90th percentile trigger a yellow flag.

### Empirical findings — aggregate and cross-group patterns
- Overall correlations:
  - Credit growth is positively correlated with real sector performance (GDP, consumption, investment, employment), particularly when credit growth is unusually high or low.
  - Relationship between credit growth and inflation is weak (tends to be weakly negatively correlated).
  - Conditioning on house prices and the output gap in addition to credit improves explanation of real sector outcomes; conditioning on the financial account balance generally does not change the relationship.
- Summary statistics (Panel A, all countries):
  - Mean GDP growth: 3.5 percent
  - Mean consumption growth: 3.4 percent
  - Mean investment growth: 3.6 percent
  - Mean inflation (mean and median): 9.1 percent and 5.1 percent
  - Mean credit growth: 6.1 percent
  - Mean house price growth: 2.5 percent
- Distributional patterns:
  - Dispersion: interquantile range of unconditional GDP growth is 4.6 percent in emerging markets and 5.4 percent in low-income countries, versus 3.1 percent in advanced economies.
  - Conditional GDP growth thresholds (all countries example):
    - Conditional on low credit: 25th and 75th thresholds are -1.4 percent and 4.3 percent respectively.
    - Conditional on high credit: 25th and 75th thresholds are 3.6 percent and 8.1 percent respectively.
  - Investment and consumption growth are more sensitive to credit conditions than GDP growth and employment.
  - Inflation: lower quantiles (10th and 25th) do not vary much with credit conditions; upper quantiles (75th and 90th) can be higher when credit growth is low, especially in emerging markets (example for emerging markets: upper decile and upper quantile when credit growth is low are 61.9 percent and 26.3 percent, compared to 19.4 percent and 10.6 percent when credit growth is high).
- Double conditioning insights:
  - Credit + house prices: GDP growth is lower when both credit and house price growth are in the lowest quintile and higher when both are in the highest quintile; conditioning on both intensifies the association relative to credit alone.
  - Credit + output gap: relationship between credit growth and GDP is stronger when the output gap is unusually low or unusually high; credit and GDP growth are likely to be low after crises (large negative output gaps).
  - Credit + financial account: adding information on high or low financial account balances does not systematically change the relationship between credit and GDP growth.

### Evidence from related literature (selected quantitative findings cited)
- Dell’Ariccia et al. (2012) (sample of 170 countries, 1970–2010):
  - Credit-to-GDP ratio growth in credit booms: 16.8 percent vs 1.6 percent in normal times.
  - Investment growth: 10.3 percent during booms vs 4.2 percent in normal times.
  - House price growth: 9.5 percent vs 1.8 percent.
  - Equity price growth: 11 percent vs 3.8 percent.
  - Current account: deteriorates by about 1 percent of GDP during a credit boom.
  - Inflation: 10.7 percent in boom years vs 9.3 percent in non-boom years.
  - Higher pre-crisis credit-to-GDP associated with larger GDP losses in 2007-2009.
- Claessens, Kose, and Terrones (2012):
  - Recessions that coincide with credit crunches are twice as costly as recessions without a credit crunch (measured by cumulative loss in real GDP).
  - Expansions that coincide with credit or housing booms have cumulative gains twice as large as expansions without such booms.
- Abiad et al. (2011) on “creditless recoveries”:
  - Real GDP growth in creditless recoveries: 3.8 percent vs 6.3 percent otherwise.
  - Probability of returning to pre-recession trend: 45 percent in the creditless case versus 65 percent otherwise.

### Toolkit guidance and intended use
- Toolkit use cases:
  - Compare a forecast scenario with historical norms via conditional empirical distributions.
  - Flag joint real-financial forecasts that are unlikely based on historical data.
  - Flexibility: users can condition on one or two financial variables and select comparison groups by income level, region, or source of export earnings.
- Implementation notes:
  - Designed for minimal data requirements to cover a large set of economies.
  - Winsorization at the 2 percent and 98 percent levels applied to inputs.
  - Users should interpret toolkit outputs within country-specific macroeconomic narratives; toolkit outcomes are informative but do not substitute for country judgement (examples: investment may be high despite weak credit if corporates use retained earnings).
  - Users can input the output gap forecast from the macro scenario; output gap used instead of a crisis indicator for simplicity.

### Concluding remarks
- The toolkit provides empirical discipline for integrating financial sector forecasts into the FPP framework by exploiting joint historical distributions across countries.
- Main empirical conclusions:
  - Credit growth is linked to real sector performance (GDP, consumption, investment, employment), especially when credit growth is unusually high or low.
  - Credit growth and inflation tend to be weakly negatively correlated.
  - Conditioning on house prices and the output gap improves explanatory power; including the financial account balance does not appear to add explanatory power.

*Source: tnm1709 - EXECUTIVE SUMMARY*

### EXECUTIVE SUMMARY

### EXECUTIVE SUMMARY

### Purpose and scope
- Toolkit developed to assess consistency between real sector and financial sector forecasts within the IMF’s Financial Programming and Policies (FPP) framework.
- Empirical basis: cross-country historical data for 182 economies from 1980 to 2015.
- Financial conditioning variables considered: credit growth, house prices, financial account balance. Economic cycle measure: output gap.
- Methodology: model-free, empirical joint distributions; deliberately simple to facilitate implementation in the FPP framework.

### Key methodological details
- Macro variables: GDP, private consumption, private investment, employment, CPI inflation (annual, real series deflated by GDP deflator).
- Financial variables:
  - Credit: “claims on the private sector by deposit money banks” (annual growth, real terms).
  - House prices: OECD, BIS, and IMF Real Estate Module (real house price index, annual growth).
  - Financial account: net acquisition of financial assets, normalized by current year GDP in US dollars.
- Data cleaning and transforms:
  - Winsorize all macroeconomic and financial variables at the 2 percent and 98 percent levels.
  - Output gap: percentage deviation of real GDP from potential; potential estimated with HP filter with smoothing factor of 6.25.
- Conditional distribution estimation:
  - Estimate inverse CDF for each conditioning variable at each quintile.
  - Estimate CDFs of real variables conditional on single and double conditioning (credit alone; credit + financial account; credit + house prices; credit + output gap).
  - Toolkit reports threshold values at the 10th, 25th, 75th, and 90th percentiles.
  - Flagging rules: values below the 10th percentile or above the 90th percentile trigger a red flag; values between the 10th and 25th percentile, or between the 75th and 90th percentile trigger a yellow flag.

### Empirical findings — aggregate and cross-group patterns
- Overall correlations:
  - Credit growth is positively correlated with real sector performance (GDP, consumption, investment, employment), particularly when credit growth is unusually high or low.
  - Relationship between credit growth and inflation is weak (tends to be weakly negatively correlated).
  - Conditioning on house prices and the output gap in addition to credit improves explanation of real sector outcomes; conditioning on the financial account balance generally does not change the relationship.
- Summary statistics (Panel A, all countries):
  - Mean GDP growth: 3.5 percent
  - Mean consumption growth: 3.4 percent
  - Mean investment growth: 3.6 percent
  - Mean inflation (mean and median): 9.1 percent and 5.1 percent
  - Mean credit growth: 6.1 percent
  - Mean house price growth: 2.5 percent
- Distributional patterns:
  - Dispersion: interquantile range of unconditional GDP growth is 4.6 percent in emerging markets and 5.4 percent in low-income countries, versus 3.1 percent in advanced economies.
  - Conditional GDP growth thresholds (all countries example):
    - Conditional on low credit: 25th and 75th thresholds are -1.4 percent and 4.3 percent respectively.
    - Conditional on high credit: 25th and 75th thresholds are 3.6 percent and 8.1 percent respectively.
  - Investment and consumption growth are more sensitive to credit conditions than GDP growth and employment.
  - Inflation: lower quantiles (10th and 25th) do not vary much with credit conditions; upper quantiles (75th and 90th) can be higher when credit growth is low, especially in emerging markets (example for emerging markets: upper decile and upper quantile when credit growth is low are 61.9 percent and 26.3 percent, compared to 19.4 percent and 10.6 percent when credit growth is high).
- Double conditioning insights:
  - Credit + house prices: GDP growth is lower when both credit and house price growth are in the lowest quintile and higher when both are in the highest quintile; conditioning on both intensifies the association relative to credit alone.
  - Credit + output gap: relationship between credit growth and GDP is stronger when the output gap is unusually low or unusually high; credit and GDP growth are likely to be low after crises (large negative output gaps).
  - Credit + financial account: adding information on high or low financial account balances does not systematically change the relationship between credit and GDP growth.

### Evidence from related literature (selected quantitative findings cited)
- Dell’Ariccia et al. (2012) (sample of 170 countries, 1970–2010):
  - Credit-to-GDP ratio growth in credit booms: 16.8 percent vs 1.6 percent in normal times.
  - Investment growth: 10.3 percent during booms vs 4.2 percent in normal times.
  - House price growth: 9.5 percent vs 1.8 percent.
  - Equity price growth: 11 percent vs 3.8 percent.
  - Current account: deteriorates by about 1 percent of GDP during a credit boom.
  - Inflation: 10.7 percent in boom years vs 9.3 percent in non-boom years.
  - Higher pre-crisis credit-to-GDP associated with larger GDP losses in 2007-2009.
- Claessens, Kose, and Terrones (2012):
  - Recessions that coincide with credit crunches are twice as costly as recessions without a credit crunch (measured by cumulative loss in real GDP).
  - Expansions that coincide with credit or housing booms have cumulative gains twice as large as expansions without such booms.
- Abiad et al. (2011) on “creditless recoveries”:
  - Real GDP growth in creditless recoveries: 3.8 percent vs 6.3 percent otherwise.
  - Probability of returning to pre-recession trend: 45 percent in the creditless case versus 65 percent otherwise.

### Toolkit guidance and intended use
- Toolkit use cases:
  - Compare a forecast scenario with historical norms via conditional empirical distributions.
  - Flag joint real-financial forecasts that are unlikely based on historical data.
  - Flexibility: users can condition on one or two financial variables and select comparison groups by income level, region, or source of export earnings.
- Implementation notes:
  - Designed for minimal data requirements to cover a large set of economies.
  - Winsorization at the 2 percent and 98 percent levels applied to inputs.
  - Users should interpret toolkit outputs within country-specific macroeconomic narratives; toolkit outcomes are informative but do not substitute for country judgement (examples: investment may be high despite weak credit if corporates use retained earnings).
  - Users can input the output gap forecast from the macro scenario; output gap used instead of a crisis indicator for simplicity.

### Concluding remarks
- The toolkit provides empirical discipline for integrating financial sector forecasts into the FPP framework by exploiting joint historical distributions across countries.
- Main empirical conclusions:
  - Credit growth is linked to real sector performance (GDP, consumption, investment, employment), especially when credit growth is unusually high or low.
  - Credit growth and inflation tend to be weakly negatively correlated.
  - Conditioning on house prices and the output gap improves explanatory power; including the financial account balance does not appear to add explanatory power.

*Source: tnm1709 - EXECUTIVE SUMMARY*

### References

### References

### Credit, Creditless Recoveries, and Credit Cycles
- Abiad, Abdul, Giovanni Dell’Ariccia, and Grace B Li, 2011. “Creditless Recoveries,” IMF Working Papers 11/58, International Monetary Fund.
- Kiyotaki, Nobuhiro, and John Moore, 1997. “Credit Cycles”. Journal of Political Economy. 105 (2): pp. 211–248.
- Igan, Deniz, Alain Kabundi, Francisco Nadal De Simone. Marcelo Pinheiro, and Natalia Tamirisa, 2009. “Three Cycles: Housing, Credit, and Real Activity”. IMF Working Paper 09/231, International Monetary Fund.
- Gilchrist, Simon and Egon Zakrajsek, 2012. “Credit Spreads and Business Cycle Fluctuations,” American Economic Review, vol. 102(4), pp. 1692-1720.

### Macrofinancial Stability, Real Estate, and Policy Responses
- Crowe, Chris, Giovanni Dell’Ariccia, Deniz Igan, and Pau Rabanal, 2011. “Policies for Macrofinancial Stability: Options to Deal with Real Estate Booms,” IMF Staff Discussion Notes 11/02, International Monetary Fund.
- Dell’Ariccia, Giovanni, Luc Laeven, Deniz Igan, Tong Hui, Bas Bakker, and Jerome Vandenbussche, 2012. “Policies for Macrofinancial Stability; How to Deal with Credit Booms,” IMF Staff Discussion Notes 12/06, International Monetary Fund.
- Kannan, Prakash, Pau Rabanal, and Alasdair Scott, 2011. “Recurring Patterns Around House Price Busts” Applied Economics Letters, Vol. 18, No. 2, pp. 107-113.

### Business Cycles, International Cycles, and Capital Flows
- Backus, D.K., P.J. Kehoe and F.E. Kydland, 1995, “International business cycles: Theory and evidence”, in C. Plosser (ed.), Frontiers of Business Cycle Research, Princeton University Press, Princeton, NJ, pp. 331-57.
- Claessens, Stijn, M. Ayhan Kose, and Marco Terrones, E., 2012, “How Do Business and Financial Cycles Interact?” Journal of International Economics, Vol. 87, No. 1, pp. 178-190.
- Gourinchas, Pierre-Olivier and Olivier Jeanne, 2003. “Capital Flows to Developing Countries: The Allocation Puzzle,” Review of Economic Studies, Vol.80, pp. 1484-1515.
- Lucas, Robert, 1990. “Why doesn’t Capital Flow from Rich to Poor Countries?” American Economic Review, vol. 80(2): pp. 92–96

### Financial Accelerators, Bond Markets, and Financial Information
- Bernanke, Ben, Mark Gertler, and Simon Gilchrist, , 1999. “The financial accelerator in a quantitative business cycle framework,” In J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pp. 1341-1393.
- Philippon, Thomas, 2009, “The Bond Market’s q,” The Quarterly Journal of Economics, Vol. 124, No. 3, pp. 1011-1056.
- Chen, Sophia and Romain Ranciere, 2016, “Financial Information and Macroeconomic Forecasts”, IMF Working Paper No. 16/251, International Monetary Fund.

### Empirical Studies on Crises and Output Dynamics
- Cerra, Valerie and Sweta Saxena, 2008. “Growth Dynamics: The Myth of Economic Recovery,” American Economic Review, vol. 98(1), pp. 439-457.
- International Monetary Fund, 2009. “What’s the Damage? Medium-Term Output Dynamics after Financial Crises”. Chapter 4 of World Economic Outlook, October 2009.

*Content derived from the References section of the source PDF.*

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_Source: https://www.imf.org/-/media/files/publications/tnm/2017/tnm1709.pdf_