1. THE CREDIT CYCLE AND THE BUSINESS CYCLE IN CANADA AND THE U.S.: TWO SOLITUDES? Pierre L. Siklos WLU & Viessmann European Research Centre Brady Lavender, Bank of Canada

2. While most central banks have added a financial stability objective in recent years, the monetary policy and financial stability wings of many of our institutions have operated as two solitudes. (Carney 2009) 2

3. The Role of Credit Credit availability is an essential part of MP effectiveness – Known for a long time (e.g., Roosa 1951) but ignored or forgotten, until recently Two channels of influence exist – price channel (i.e., interest rate) – non-price or credit rationing channel (e.g., stemming from asymmetric information problems) 3

4. Motivation & Background Current economic environment highlights the links between the real and financial sectors – Of special interest: the role of credit Credit has long been known to have a price and a non-price element – Price: interest rate – Non-price: credit standards Could the non-price element be macro-economically important? 4

5. The Questions Asked Do changing credit conditions influence real economic outcomes? – The key source: survey of lending standards Do (monetary) policy rate shocks influence loan standards? How does the picture change when real time data are used? If the U.S. and Canada are compared, then – Are they indeed two solitudes? 5

6. Bottom Line: Previewing Some Results Tightening of standards is associated with a reduction of loans, output, and tighter monetary policy in BOTH the US and Canada Survey data contains some useful information that should be incorporated into macro-models, especially in light of the necessity to consider financial frictions U.S. and Canada appear to be two solitudes: impact of standards on loans considerably larger in US than in Canada 6

7. Related Literature From Roosa (1951) to Fuerst (1994) – Credit availability influences the effectiveness of MP Blanchard and Fischer (1989) – Credit rationing exists, so interest rates are not market clearing Stiglitz and Weiss (1981) – Interest rate changes create adverse selection (withdrawal of risk averse borrowers) and moral hazard problems (incentives to engage in risky behavior): imperfect information in credit markets means they are not market clearing Schreft and Owens (1991) – Lending standards can change before cost of funds does. Therefore, non-price lending standards represent an important link between MP and the financial sector Measured via surveys 7

8. Non-Price Lending Standards and the Macro-economy Lown et.al. (2000) Lown and Morgan (2006) Swiston (2008) Beaton et. Al. (2009) – 1% tightening leads to 2.5% reduction in loans, > 2% fall in investment – Tightening of standards leads to a fall in GDP ( 0.25-1%) – Tightening of MP leads to a tightening of standards (8%) – SLOS data precedes macro-data that would also be reflected in a fall in loans Murray (2012): Stage 2…six key inputs to this meeting: …5. the Banks Senior Loan Officer Survey; * 8 *Monetary Policy Decision-Making at the Bank of Canada, Remarks by Deputy Governor of the Bank of Canada Mortgage Brokers Association of B.C. Vancouver, British Columbia, 7 May 2012

9. Testing Strategy: Outline ExtendedCore +Core VAR/ VECM Macro Demand factors Credit 9

10. An Identification Problem? Increases in loans and investment could be explained by easing lending standards or increasing loan demand. Thus, …it is essential to control for loan demand. There is an identification problem as changes in the price of loans, the going interest rate on loanable funds, reflects both demand and supply factors which operate simultaneously. 10

11. Testing Strategy: Extension ExtendedFAVARCore VAR/ VECM – CANADA U.S. PCAMacroCreditCAD VARCore + Credit 11

12. Testing Strategy: Equations 12

13. Data & Stylized Facts SLOS U.S. (since 1970s) & Canada (since late 1990s) ~ balance of opinion – Over the past three months, how have your banks credit standards for approving loan applications for C&I loans or credit likes – excluding those to finance mergers and acquisitions – changed? 1) Tightened considerably 2) tightened somewhat 3) remained basically unchanged 4) eased somewhat 5) eased considerably UNITED STATES – "How have your institutions general standards (i.e. your appetite for risk) and terms for approving credit changed in the past three months? CANADA Canada has price versus non-price distinction but differences not informative 13

14. Figure 1a Senior Officer Loan Survey and Commercial Loans, 1972-2011: U.S. 14

15. Figure 1b Senior Officer Loan Survey and Commercial Loans, 1999-2011: Canada 15

16. Data & Stylized Facts SLOS U.S. (since 1970s) & Canada (since late 1990s) ~ balance of opinion – Over the past three months, how have your banks credit standards for approving loan applications for C&I loans or credit likes – excluding those to finance mergers and acquisitions – changed? 1) Tightened considerably 2) tightened somewhat 3) remained basically unchanged 4) eased somewhat 5) eased considerably – "How have your institutions general standards (i.e. your appetite for risk) and terms for approving credit changed in the past three months? Canada has price versus non-price distinction but differences not informative 16

17. Price and Non-Price Survey Indicators: SLOS for Canada 17

18. Other Series & Samples Real GDP, GDP Deflator, Commodity prices, Policy Rate – Defines basic macro model Add: Loans, SLOS – Defines core or benchmark model Add: expected real GDP growth, term spread, FCI* – Defines extended model – * acts as a quasi F since it measures risk, liquidity, and leverage in money markets and equity markets as well as in the traditional and shadow banking systems US: 1972:1-2011:1 (disjointed: 1984-1990 omitted), CAD: 1999:2-2011:1 18

19. Core Series US 19

20. Core Series Canada 20

21. FCI 21 USA Canada

22. Other Series & Samples Real GDP, GDP Deflator, Commodity prices – Defines basic macro model Add: Loans, SLOS – Defines core or benchmark model Add: expected real GDP growth, term spread, FCI* – Defines extended model – * acts as a quasi F since it measures risk, liquidity, and leverage in money markets and equity markets as well as in the traditional and shadow banking systems US: 1972:1-2011:1 (disjointed: 1984-1990 omitted), CAD: 1999:2-2011:1 – US: begin with 1972-1990 (Lown & Morgan)** + 1990-2011 & 1972-2011 (disjointed) ** No data for 1968-1971 & vintage of data differs 22

23. An Important Addition: real-time data Vintages: U.S. Real GDPSignificanceVintages: U.S. Potential Output 2000 DecemberJust before P2000 July 2002 MarchJust after T2002 February 2007 SeptemberJust before P – PRE-CRISIS2007 August 2009 SeptemberJust after T – FIN CRISIS2009 August Vintages: CAN real GDPSignificanceFrom Bank of Canada 2002 MarchSee US 2007 Q3See US 2007 Q4Peak CAD bus cycle 2009 Q3BoC interest rate comm. 23

24. An Important Addition: real-time data 24 US

25. Output Gaps in Real Time: A Closer Look for the US I 25

26. Output Gaps in Real Time: A Closer Look for the US II 26

27. An Important Addition: real-time data 27 Canada

28. Canadian Real Time Data: A Specific Vintage, 2011 Q1 28

29. VAR/VECM Issues I Lag length? – AIC, HQ, SC...but parsimony wherever results are robust Series transformations? – All in log levels EXCEPT: SLOS, Spread, forecasted growth rate – Real GDP, GDP Deflator, loans ~ I(1) – SLOS, Comm. Prices, Spread, Policy rate, FCI ~ I(0) S.E. via MC 29

30. VAR/VECM Issues II What CI? – {policy rate – Loans}, {policy rate-SLOS}, {real GDP- Loans} Does the ordering matter? – [MACRO, CREDIT]: Core – [MACRO, DEMAND IDENTIFIERS, CREDIT] Conventional IRFs & VDs + GIRFs 30

31. Empirical Results, US: Summary 1972-2000 – Real GDP falls when standards are raised – Loans decline when standards are tightened – No impact of loans to standards Identification problem solved? Not entirely is EXTENDED model is considered 1972-2011 – Results are similar... Negative impact on real GDP larger Negative impact on loans smaller Could weakening standards and greater importance of financial sector have played a role? 31

32. U.S. Real Time Data Results largely unchanged! –...but a TIGHTENING of standards leads to Loans INCREASE for only for 2009Q3 and it seems PERMANENT Results insensitive to ordering 32

33. IRFs: US, 1999-2011* 33 FIGURE 3: EXTENDED VAR

34. IRFs: 2009 Q3 Vintage 34 FIGUR 2c 1 st COLUMN, EXTENDED VAR

35. Empirical Results, Canada: Summary Results reminiscent of ones for the U.S.: Recall that the GFC did not materially affect Canadas financial sector but we did have a drop in GDP Negative impact on GDP from a tightening of standards disappears in the extended model Small (but stat sig) response of loans from STANDARDS Real time data makes little difference to the results – Real & financial link is NOT dependent on crisis but applies equally to US and CAD data 35

36. US Factor Scores for CAD FAVAR 36 Also see FIGURE 5

37. Macro US Factor Scores 37

38. Financial US Factor Scores 38

39. FAVAR for CANADA 39 Scaling changed

40. Variance Decompositions I Confirms the significant explanatory power of SLOS on real GDP – But SLOS explains more of the VAR of ALL vars than for US data…do standards matter more in Canada? Shocks of Loans to FFR but NOT vice-versa Shocks of FFR to SLOS more important than any other variable – But term spread matters not for the US but is sig for Canada Real time data AMPLIFIES results based on revised data – e.g., SLOS explanatory power for FFR 25 times larger, and SLOS VD for Loans 3 times larger SLOS less important in extended model but Loans become more important 40

41. VD: An Illustration 41

42. Conclusions Empirical exploration of links between lending standards, credit, and macro activity in CANADA and spillovers from the US – Credit shock play a bigger role in US than in Canada….two solitudes – Real time data considerations matter…shock has bigger –ve impact on US real GDP in 2009Q3 vintage – MP was more effective in Canada in impacting loans & standards – SLOS is a proxy for frictions in financial markets and should be considered as a candidate for inclusion in empirical macro models EXTENSIONS? – More VECM work? Longer sample needed – Other types of VAR? may require better theoretical underpinnings 42