Two and a Half Solitudes: Shared Language Versus Shared Geography in the National Capital Region
THE CREDIT CYCLE AND THE BUSINESS CYCLE IN CANADA AND THE U.S.: TWO SOLITUDES?
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Transcript of THE CREDIT CYCLE AND THE BUSINESS CYCLE IN CANADA AND THE U.S.: TWO SOLITUDES?
THE CREDIT CYCLE AND THE BUSINESS CYCLE IN CANADA AND THE U.S.:
TWO SOLITUDES?
Pierre L. Siklos WLU &Viessmann European Research
CentreBrady Lavender, Bank of Canada
“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)
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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)
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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?
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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’?
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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
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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
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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 Bank’s Senior Loan Officer Survey;” *
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*“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
Testing Strategy: Outline
ExtendedCore +Core
VAR/VECM
MacroMacro
Demand factors
Credit Credit
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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.”
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Testing Strategy: Extension
ExtendedFAVARCore
VAR/VECM –CANADA
U.S. PCA
Macro
Credit
CAD VAR Core + Credit
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Testing Strategy: Equations
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0 1 1tt t y A A y ε
0 1 1 2 1tt tt y A A y A z ε
'0 1tt t y A y ε
* ' ' * '0 1 1 2 1tt tt y A A y A z ε
US UStt t y F e
1
1
( )ttt
tt
F FL
y y
Data & Stylized Facts• SLOS U.S. (since 1970s) & Canada (since late 1990s) ~ ‘balance of opinion’
– “Over the past three months, how have your bank’s 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 institution’s 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
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Figure 1a Senior Officer Loan Survey and Commercial Loans, 1972-2011: U.S.
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-40
-20
0
20
40
60
80
100
1975 1980 1985 1990 1995 2000 2005 2010
Net tightening of standards Commercial loan growth
Perc
ent (
annu
al)
No standards reported1984- 90
Figure 1b Senior Officer Loan Survey and Commercial Loans, 1999-2011: Canada
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-40
-20
0
20
40
60
80
100
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Balance of Overall credit standards Growth in business credit
SLOS
inde
x
Annual precent change
Data & Stylized Facts
• SLOS U.S. (since 1970s) & Canada (since late 1990s) ~ ‘balance of opinion’
– “Over the past three months, how have your bank’s 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 institution’s 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
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Price and Non-Price Survey Indicators: SLOS for Canada
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-60
-40
-20
0
20
40
60
80
100
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Differential SLOS: non-priceSLOS: pricing
+ m
eans
net
tigh
teni
ng o
f sta
ndar
ds -
mea
ns n
et lo
osen
ing
of st
anda
rds
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
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Core Series US
19
8.4
8.6
8.8
9.0
9.2
9.4
9.6
1975 1980 1985 1990 1995 2000 2005 2010
US rea l GDP
3.2
3.6
4.0
4.4
4.8
1975 1980 1985 1990 1995 2000 2005 2010
GDP deflator
1
2
3
4
5
1975 1980 1985 1990 1995 2000 2005 2010
Oi l Price
0
4
8
12
16
20
1975 1980 1985 1990 1995 2000 2005 2010
US federal funds rate
7.0
7.5
8.0
8.5
9.0
9.5
1975 1980 1985 1990 1995 2000 2005 2010
Commercial loans
-40
-20
0
20
40
60
80
100
1975 1980 1985 1990 1995 2000 2005 2010
SLOS
Loga
rithm
of the
leve
ls
Loga
rithm
of the
leve
ls (ind
ex)
Loga
rithm
of the
leve
ls
Perce
nt
Loga
rithm
of the
leve
ls (Bil
lions
of $)
+ ind
icates
net ti
ghten
ing of
stan
dards
- ind
icates
net lo
osen
ing of
stan
dards
Core Series Canada
20
13.80
13.85
13.90
13.95
14.00
14.05
14.10
14.15
2000 2002 2004 2006 2008 2010
rea l GDP
4.52
4.56
4.60
4.64
4.68
4.72
4.76
4.80
4.84
2000 2002 2004 2006 2008 2010
GDP defla tor
5.4
5.6
5.8
6.0
6.2
6.4
6.6
6.8
2000 2002 2004 2006 2008 2010
Commodity prices
0
1
2
3
4
5
6
2000 2002 2004 2006 2008 2010
Overnight Ra te
13.5
13.6
13.7
13.8
13.9
14.0
14.1
2000 2002 2004 2006 2008 2010
Business credit
-40
-20
0
20
40
60
80
2000 2002 2004 2006 2008 2010
SLOS
Loga
rithm
of th
e lev
elsLo
garit
hm of
the l
evels
Loga
rithm
of th
e lev
els
Loga
rithm
of th
e lev
els
Perce
nt+ s
ignifie
s net
tight
ening
of cr
edit s
tand
ards
- sign
ifies n
et lo
osen
ing of
cred
it sta
ndar
ds
FCI
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-4
-3
-2
-1
0
1
2
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
BOC_FCI
+ =
imp
rovi
ng
fin
anci
al c
on
dit
ion
s/ -
= d
eter
iora
tin
g fi
nan
cial
co
nd
itio
ns
USA Canada
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
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An Important Addition: real-time dataVintages: U.S. Real GDP Significance Vintages: U.S. Potential
Output2000 December Just before P 2000 July
2002 March Just after T 2002 February
2007 September Just before P – PRE-CRISIS 2007 August
2009 September Just after T – FIN CRISIS 2009 August
Vintages: CAN real GDP Significance From Bank of Canada
2002 March See US
2007 Q3 See US
2007 Q4 Peak CAD bus cycle
2009 Q3 BoC interest rate comm.
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An Important Addition:real-time data
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-10
-8
-6
-4
-2
0
2
4
6
1975 1980 1985 1990 1995 2000 2005 2010
2000 Q4 vintage 2002 Q1 vintage2007Q3 vintage 2009 Q3 vintage
-5
-4
-3
-2
-1
0
1
2
3
4
1975 1980 1985 1990 1995 2000 2005 2010
2000 Q4 vintage 2002 Q1 vintage2007 Q3 vintage 2009 Q3 vintage
100 tim
es (log
real GD
P - log
poten
tial rea
l GDP)
Congressional Budget Office Estimates
H-P Filter estimates
US
Output Gaps in Real Time: A Closer Look for the US I
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-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2000 2002 2004 2006 2008 2010 2012 2014
CBO 2000 prices - 2009 vintage CBO 2005 prices - August 2011 vintage
US Potential Output- CBO Estimate
Norm
aliz
ed
Output Gaps in Real Time: A Closer Look for the US II
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-4
-3
-2
-1
0
1
2
3
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
January 2008 December 2008December 2009
Pe
rce
nt
-12
-10
-8
-6
-4
-2
0
2
4
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
July 2009 August 2009
-10
-8
-6
-4
-2
0
2
4
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
June 2010 December 2010March 2011 August 2011
Pe
rce
nt
Output gaps - US
An Important Addition:real-time data
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Canada
-5
-4
-3
-2
-1
0
1
2
3
2000 2002 2004 2006 2008 2010
GAP_200201 GAP_200703GAP_200704 GAP_200903
-.3
-.2
-.1
.0
.1
.2
2000 2002 2004 2006 2008 2010
GAPHP_200201 GAPHP_200703GAPHP_200704 GAPHP_200903
Percen
tBank of Canada
H-P Filter
Canadian Real Time Data: A Specific Vintage, 2011 Q1
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1,260,000
1,280,000
1,300,000
1,320,000
1,340,000
1,360,000
1,380,000
I II III IV I II III IV I II III IV I II III IV I
2007 2008 2009 2010 2011
Potential real GDP 2008Q1 Potential real GDP 2009Q1Porential real GDP 2010Q1 Potential real GDP 2011Q1realized real GDP 2011Q1
Mill
ions
$Real ized and Potential Output - CANADA
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
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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
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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?
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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
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IRFs: US, 1999-2011*
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-.02
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10 11 12
Resp
onse
of l
og re
al G
DP to
SLO
S
-.08
-.04
.00
.04
.08
1 2 3 4 5 6 7 8 9 10 11 12
Resp
onse
of l
og L
oans
to S
LOS
-8
-4
0
4
8
12
1 2 3 4 5 6 7 8 9 10 11 12
Resp
onse
of S
LOS
to lo
g Lo
ans
QuartersQuarters Quarters
FIGURE 3: EXTENDED VAR
IRFs: 2009 Q3 Vintage
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-.02
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10 11 12
Resp
onse
of l
og r
eal G
DP
to S
LOS
-.08
-.04
.00
.04
.08
1 2 3 4 5 6 7 8 9 10 11 12
Resp
onse
of l
og L
oans
to
SLO
S
-8
-4
0
4
8
12
1 2 3 4 5 6 7 8 9 10 11 12
Resp
onse
of S
LOS
to lo
g Lo
ans
Quarters QuartersQuarters
FIGUR 2c 1st COLUMN, EXTENDED VAR
Empirical Results, Canada: Summary
• Results reminiscent of ones for the U.S.: Recall that the GFC did not materially affect Canada’s 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
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US Factor Scores for CAD FAVAR
36
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2000 2002 2004 2006 2008 2010
Macro conditions Macro conditions 2009Q3
-2
-1
0
1
2
3
4
2000 2002 2004 2006 2008 2010
Financial conditions 2009Q3 Financial conditions
Factor
scores
Also see FIGURE 5
Macro US Factor Scores
37
-6
-5
-4
-3
-2
-1
0
1
2
1975 1980 1985 1990 1995 2000 2005 2010
1972-2011 1989-20111999-2011 1999-2011 (2009Q3 vintage)
Fact
or S
core
s
Financial US Factor Scores
38
-2
-1
0
1
2
3
4
5
1975 1980 1985 1990 1995 2000 2005 2010
1972-2011 1989-20111999-2011 1999-2011 (vintage 2009Q3)
Fact
or S
core
s
FAVAR for CANADA
39
-.0004
-.0002
.00 00
.00 02
.00 04
1 2 3 4 5 6 7 8 9 10 11 12
Resp
onse
of l
og r
eal G
DP
to S
LOS
-.008
-.004
.000
.004
.008
1 2 3 4 5 6 7 8 9 10 11 12Resp
onse
of l
og B
usin
ess
Cred
ot t
o SL
OS
-8
-4
0
4
8
12
1 2 3 4 5 6 7 8 9 10 11 12Resp
onse
of S
LOS
to lo
g Bu
sine
ss C
redi
t
Scaling changed
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
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VD: An Illustration
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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
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