1. Decomposing the business cycle: the relative importance of country-specific and common shocks for small-open economies within the euro area
Bruno De Backer Hans Dewachter
Namur, 5th Belgian Macroeconomics Workshop 12 September 2017
The views expressed in this presentation are those of the authors and do not necessarily reflect the views of the National Bank of Belgium.

2. The (reduced-form) model
Overview
Related literature
The (reduced-form) model
Structural analysis with zero and sign restrictions
Decomposition of business cycles within the euro area
Tentative conclusion

3. Related literature
Model developed in the context of a working group at the ECB, initially aiming at harmonising/improving methods used for credit projections
Since VAR model, structural analysis can also be carried out: aim at revealing the importance of credit supply shocks for the business cycle
Euro area particularly interesting given importance of banking sector.
Related literature (credit supply shocks), see among others:
Gambetti and Musso (2012);
Darracq Paries et al. (2014);
Duchi and Elbourne (2016);
Peersman (2012);
Hristov et al. (2012);
Gulan et al. (2014).
What is new with respect to the literature:
Distinction between country-specific and common shocks;
Many shocks identified with (zero and) sign restrictions (in a VAR set-up);
Transversal study of small-open economies in the euro area.
Preliminary version of the model, hence preliminary results (and
tentative conclusion)

4. The (reduced-form) model
Overview
Related literature
The (reduced-form) model
Structural analysis with zero and sign restrictions
Decomposition of business cycles within the euro area
Tentative conclusion

5. Small open-economy Bayesian VAR (BVAR) model with Minnesota priors identified with sign and zero restrictions
The domestic (small-open) economy does not influence the euro area (EA) economy:
In the reduced-form VAR set-up, block exogeneity is imposed with zero restrictions:
𝑌 𝐵𝐸,𝑡 𝑌 𝐸𝐴,𝑡 =𝐶+ 𝐵 11 𝐵 12 𝟎 𝐵 𝑌 𝐵𝐸,𝑡−1 𝑌 𝐸𝐴,𝑡−1 + 𝜖 𝑡
𝜖 𝑡 ~ 𝑖.𝑖.𝑑. 𝑁 0,Ω
Zero restrictions are imposed with zero prior means and very small prior variances.
The covariance matrix Ω is decomposed based on zero and sign restrictions: zeros ensure that “country-specific shocks” do not have any impact at the euro area level.
Independent Normal-Inverse Wishart Minnesota prior enforce some structure but let the data speak:
Priors on own lags are loose;
Priors on other lags are more constraining;
Priors on lags in general leave much freedom to 1st lags but not to 4th (last) lags.

6. Classic small-scale VAR set-up (GDP, 𝝅, 𝒓) augmented with “credit variables” and the CISS¹
Description
Country-specific variables
GDP growth
Y-o-y growth of real GDP
Core inflation
Y-o-y growth of core HICP
House price growth
Y-o-y growth of residential real estate prices
Household spread
Weighted MIR new business rate for households, and difference with euro area long-term rate (see below)
NFC spread
Weighted MIR new business rate for NFCs, and difference with euro area long-term rate
Mortgage credit growth
Y-o-y growth of domestic bank credit to resident households for house purchase including securitised loans
Consumer loans growth
Y-o-y growth of domestic bank credit to resident households for consumption
Growth of other credit to households
Y-o-y growth of domestic bank credit to resident households for other purposes
NFC credit growth
Y-o-y growth of domestic bank credit to resident NFCs including securitised loans
Euro area variables
idem
Spread
Weighted MIR new business rate for households and NFCs, and difference with euro area long-term rate
Credit growth
Y-o-y growth of euro area bank credit to euro area households and NFCs including securitised loans
Short-term rate
3-month EURIBOR
Long-term rate
5-year rate of EURIBOR 6-month swap
CISS
Composite Index of Systemic Stress
¹ The estimation period is 200Q2-2017Q1. Quarterly series are obtained from monthly series by averaging the three monthly data points over a quarter.

7. Some additional zero restrictions to keep the model down to a reasonable size
Equations
Domestic equations
Euro area equations
GDP 𝜋
House prices
HH spread
NFC spread
Mortgage loans
Consumer loans
Other loans to HH
NFC loans
Spread
Credit
ST rate
LT rate
CISS
Variables
Domestic variables v
Euro area variables
Consumer and other loans kind of in a satellite model
Block exogeneity
Credit systems for HHs and NFCs are not directly related
Pure euro area block fully free

8. An example of how the (reduced-form) BVAR model is used: conditional forecasting of credit variables¹
Forecasts of credit variables are produced outside the core “Broad Macroeconomic Projection Exercises” (BMPEs)
Within the context of a BMPE, a path can be imposed on almost all variables in the BVAR (GDP, 𝜋,…)
Given these paths (“conditions”), the BVAR can forecast credit growth and produce forecasts that are consistent with forecasts of other variables
Conditional forecasting methodology based on:
Doan, Litterman and Sims (1986);
Waggoner and Zha (1999);
Jarociński (2010).
¹ Although the identification of structural shocks is not needed to compute conditional forecasts when all structural shocks are allowed to differ from zero, the posteriors of reduced-form parameters conditional on some sign and zero restrictions can differ when these restrictions are imposed (see Arias, Rubio-Ramirez and Waggoner, 2014).

9. The (reduced-form) model
Overview
Related literature
The (reduced-form) model
Structural analysis with zero and sign restrictions
Decomposition of business cycles within the euro area
Tentative conclusion

10. Structural analysis with zero and sign restrictions1
Structural shocks
Local demand
Local supply
Local mortgage credit supply
Local NFC credit supply
Common demand
Common supply
Common monetary policy
Common credit supply
Common systemic stress
Variables impacted
Domestic variables
GDP +
0 (-) - 𝜋
House prices
HH spread
NFC spread
Mortgage loans
Consumer loans
Other loans to HH
NFC loans
Euro area variables
Spread
Credit
ST rate
LT rate
CISS
Distinct sector credit supply shocks at the country level
Credit supply shocks move quantities and prices in opposite directions
No distinction among sectors for the common credit supply shock
General demand shocks are also credit demand shocks
Country-specific (local) shocks do not have any instantaneous impact on euro area variables
Identification of systemic stress shocks
¹ Methodology based on Arias, Rubio-Ramirez and Waggoner (2014). The zero and sign restrictions are imposed on impact, except for the “(-)” which is imposed in the first period following impact.

11. Impulse response functions: common demand shock1 (in %)BE AT EL FI IE NL PT
¹ Median IRFs per country.

12. Impulse response functions: local demand shock1 (in %)BE AT EL FI IE NL PT
¹ Median IRFs per country.

13. Impulse response functions: common credit supply shock1 (in %)BE AT EL FI IE NL PT
¹ Median IRFs per country.

14. Impulse response functions: local mortgage credit supply shock1 (in %)BE AT EL FI IE NL PT
¹ Median IRFs per country.

15. Impulse response functions: common systemic stress shock1 (in %)BE AT EL FI IE NL PT
¹ Median IRFs per country.

16. Impulse response functions: common monetary policy shock1 (in %)BE AT EL FI IE NL PT
¹ Median IRFs per country.

17. The (reduced-form) model
Overview
Related literature
The (reduced-form) model
Structural analysis with zero and sign restrictions
Decomposition of business cycles within the euro area
Tentative conclusion

18. Historical decompositions of business cycles: country-specific vs
Historical decompositions of business cycles: country-specific vs. common shocks¹ (in %, year-on-year real GDP growth demeaned)
Contributions of country-specific (local) shocks
Contributions of common shocks
¹ Mean historical decompositions based on stationary posterior draws. The contributions of unidentified shocks are not reported.

19. Historical decompositions of business cycles: the role of (local and common) credit supply shocks during the crises¹ (in %, year-on-year real GDP growth demeaned)
Local NFC credit supply shocks
Local mortgage credit supply shocks
Common credit supply shocks
¹ Mean historical decompositions based on stationary posterior draws. The contributions of unidentified shocks are not reported.

20. Historical decompositions of business cycles: country-specific vs
Historical decompositions of business cycles: country-specific vs. common demand shocks¹ (in %, year-on-year real GDP growth demeaned)
Contributions of country-specific (local) demand shocks
Contributions of common demand shocks
¹ Mean historical decompositions based on stationary posterior draws. The contributions of unidentified shocks are not reported.

21. Historical decompositions of business cycles: systemic stress shocks¹ (in %, year-on-year real GDP growth demeaned)
Contributions of systemic stress shocks
¹ Mean historical decompositions based on stationary posterior draws. The contributions of unidentified shocks are not reported.

22. Historical decompositions of business cycles: monetary policy shocks¹ (in %, year-on-year real GDP growth demeaned)
Contributions of monetary policy shocks
¹ Mean historical decompositions based on stationary posterior draws. The contributions of unidentified shocks are not reported.

23. What explains the currently low level of (core) inflation in the euro area?¹ (in %, euro area core inflation demeaned) BE AT EL FI NL PT
¹ Mean historical decompositions based on stationary posterior draws.

24. Historical decompositions of NFC credit cycles: country-specific vs
Historical decompositions of NFC credit cycles: country-specific vs. common shocks¹ (in %, year-on-year NFC credit growth demeaned)
Contributions of country-specific (local) shocks
Contributions of common shocks
¹ Mean historical decompositions based on stationary posterior draws. The contributions of unidentified shocks are not reported.

25. Historical decompositions of NFC spreads: country-specific vs
Historical decompositions of NFC spreads: country-specific vs. common shocks¹ (in %, year-on-year NFC spread demeaned)
Contributions of country-specific (local) shocks
Contributions of common shocks
¹ Mean historical decompositions based on stationary posterior draws. The contributions of unidentified shocks are not reported.

26. The (reduced-form) model
Overview
Related literature
The (reduced-form) model
Structural analysis with zero and sign restrictions
Decomposition of business cycles within the euro area
Tentative conclusion

27. Tentative conclusion
Over the past fifteen years, common shocks tended to dominate the dynamics of small-open economies in the euro area
Based on results for BE, AT, FI, NL, PT;
Except in Greece where country-specific shocks have been large since the financial crisis.
In particular, common credit supply shocks help explain the troughs in GDP growth during both the financial and sovereign debt crises
These shocks seem to be still weighing on economic growth;
By contrast, common demand shocks only help explain the trough of the financial crisis.
(Credit) supply shocks partly explain the low level of inflation currently observed in the euro area
But the contributions of unidentified shocks is large.
Common shocks also explain the bulk of changes in credit growth rates over the past fifteen years, while credit cycles had been somewhat desynchronised in the euro area
Part of the current levels of bank lending rates is attributable to common credit supply shocks

28. Background slides

29. Historical decompositions of business cycles: country-specific vs
Historical decompositions of business cycles: country-specific vs. common supply shocks¹ (in %, year-on-year real GDP growth demeaned)
Contributions of country-specific (local) supply shocks
Contributions of common supply shocks
¹ Mean historical decompositions based on stationary posterior draws. The contributions of unidentified shocks are not reported.

30. Historical decomposition of the euro area business cycle¹ (in %, euro area year-on-year real GDP growth demeaned) BE AT EL FI NL PT
¹ Mean historical decomposition based on stationary posterior draws.

31. Historical decompositions of mortgage credit cycles: country-specific vs. common shocks¹ (in %, year-on-year mortgage credit growth demeaned)
Contributions of country-specific (local) shocks
Contributions of common shocks
¹ Mean historical decompositions based on stationary posterior draws. The contributions of unidentified shocks are not reported.

32. Historical decompositions of mortgage spreads: country-specific vs
Historical decompositions of mortgage spreads: country-specific vs. common shocks¹ (in %, year-on-year mortgage spread demeaned)
Contributions of country-specific (local) shocks
Contributions of common shocks
¹ Mean historical decompositions based on stationary posterior draws. The contributions of unidentified shocks are not reported.

33. Way forward? Priors for reduced-form VAR model:
Gain insights on adequate Minnesota hyperparameters with a historical forecasting exercise.
Historical and variance decompositions:
Work on identification strategy of structural shocks.

34. Bibliography Sign and zero restrictions: Credit supply shocks:
Arias, J. E., J. F. Rubio-Ramírez and D. F. Waggoner “Inference based on SVARs identified with sign and zero restrictions: theory and applications,” Board of Governors of the Federal Reserve System, International Finance Discussion Paper No
Credit supply shocks:
Gambetti, L. and A. Musso (2012), “Loan supply shocks and the business cycle,” ECB, Working Paper No
Darracq Paries, M., L. Maurin and D. Moccero (2014), “Financial conditions index and credit supply shocks in the euro area,” ECB, Working Paper No
Duchi, F. and A. Elbourne (2016), “Credit supply shocks in the Netherlands,” CPB Netherlands Bureau for Economic Policy Analysis, Discussion Paper No. 320.
Peesman, G. (2012), “Bank lending shocks and the euro area business cycle,” Ghent University, mimeo.
Hristov, N, O. Hülsewig and T. Wollmershäuser (2012), “Loan supply shocks during the financial crisis: evidence from the euro area” Journal of International Money and Finance, Vol. 31(3),
Gulan, A., M. Haavio and J. Kilponen (2014), “Kiss me deadly: from Finnish great depression to great recession,” Bank of Finland, Discussion Paper No. 24.
Conditional forecasts:
Doan, T., R. Litterman and C. Sims “Forecasting and conditional projection using realistic prior distributions,” Federal Reserve Bank of Minneapolis, Staff Report No. 93.
Waggoner, D. F. and T. Zha “Conditional forecasts in dynamic multivariate models,” Review of Economics and Statistics, vol. 81(4), pp
Jarociński, M “Conditional forecasts and uncertainty about forecasts revisions in vector autoregressions,” Economics Letters, vol. 108, pp