Transcript Lending-Standards-Business-Loans-and-Output-CEA-JUNE

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
“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
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
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
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
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
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;” *
*“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
8
Testing Strategy: Outline
Core
Core +
Extended
Macro
Macro
VAR/
VECM
Credit
Demand
factors
Credit
9
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
Testing Strategy: Extension
Core
FAVAR
Extended
Macro
U.S. PCA
VAR/
VECM –
Credit
CANADA
CAD VAR
Core + Credit
11
Testing Strategy: Equations
y t  A 0  A 1 y t 1  ε t
y t  A 0  A 1 y t 1  A 2 z t 1  ε t
y
*
 A 0  A1y
'
t
'
*
 A 2 z t 1  ε t
'
t 1
 y t  A 0   y t 1  ε t
'
y t   Ft
US
US
 Ft 
 Ft  1 
    (L ) 
  t
 yt 
 y t 1 
 et
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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
13
Figure 1a Senior Officer Loan Survey and
Commercial Loans, 1972-2011: U.S.
100
80
Percent (annual)
60
40
20
0
-20
-40
1975
1980
1985
1990
Net tightening of standards
1995
2000
2005
Commercial loan growth
14
2010
100
2.5
80
2.0
60
1.5
40
1.0
20
0.5
0
0.0
-20
-0.5
-40
-1.0
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Balance of Overall credit standards
Growth in business credit
15
Annual precent change
SLOS index
Figure 1b Senior Officer Loan Survey and
Commercial Loans, 1999-2011: Canada
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
16
Price and Non-Price Survey Indicators:
SLOS for Canada
100
+ means net tightening of standards
- means net loosening of standards
80
60
40
20
0
-20
-40
-60
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Differential
SLOS: pricing
SLOS: non-price
17
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
Core Series US
US re a l GDP
GDP de fl a tor
4.8
9.4
Logarithm of the levels (index)
Logarithm of the levels
9.6
9.2
9.0
8.8
8.6
8.4
4.4
4.0
3.6
3.2
1975
1980
1985
1990
1995
2000
2005
2010
1975
1980
Oi l Pri ce
1995
2000
2005
2010
20
16
4
12
Percent
3
8
2
4
1
0
1975
1980
1985
1990
1995
2000
2005
2010
1975
1980
1985
Commercia l l oa ns
1990
1995
2000
2005
2010
1995
2000
2005
2010
SLOS
100
+ indicates net tightening of standards
- indicates net loosening of standards
9.5
Logarithm of the levels (Billions of $)
1990
US fe de ra l funds ra te
5
Logarithm of the levels
1985
9.0
8.5
8.0
7.5
80
60
40
20
0
-20
7.0
-40
1975
1980
1985
1990
1995
2000
2005
2010
1975
1980
1985
1990
19
Core Series Canada
GDP de fl a tor
4.84
14.10
4.80
Logarithm of the levels
Logarithm of the levels
re a l GDP
14.15
14.05
14.00
13.95
13.90
4.76
4.72
4.68
4.64
4.60
13.85
4.56
4.52
13.80
2000
2002
2004
2006
2008
2000
2010
2002
6.8
6
6.6
5
6.4
2010
2006
2008
2010
2006
2008
2010
4
Percent
Logarithm of the levels
2008
2006
Ove rni ght Ra te
Commodi ty pri ce s
6.2
6.0
3
2
5.8
1
5.6
0
5.4
2000
2002
2004
2006
2008
2000
2010
2002
2004
SLOS
Business credit
+ signifies net tightening of credit standards
- signifies net loosening of credit standards
14.1
Logarithm of the levels
2004
14.0
13.9
13.8
13.7
80
60
40
20
0
-20
13.6
-40
13.5
2000
2002
2004
2006
2008
2010
2000
2002
2004
20
+ = improving financial conditions/ - = deteriorating financial conditions
FCI
USA
BOC_FCI
2
1
0
-1
-2
-3
-4
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Canada
21
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 data
Vintages: U.S. Real GDP
Significance
Vintages: U.S. Potential
Output
2000 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.
23
An Important Addition:
real-time data
Congres s i ona l Budget Offi ce Es ti ma tes
6
4
2
0
-2
100 times (log real GDP - log potential real GDP)
-4
-6
-8
-10
1975
1980
1985
1990
2000 Q4 vi nta ge
2007Q3 vi nta ge
1995
2000
2005
2010
US
2002 Q1 vi nta ge
2009 Q3 vi nta ge
H-P Fi l ter es ti ma tes
4
3
2
1
0
-1
-2
-3
-4
-5
1975
1980
1985
1990
2000 Q4 vi nta ge
2007 Q3 vi nta ge
1995
2000
2005
2010
2002 Q1 vi nta ge
2009 Q3 vi nta ge
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Output Gaps in Real Time:
A Closer Look for the US I
US Potential Output- CBO Estimate
2.0
1.5
Normalized
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
2000
2002
2004
2006
CBO 2000 prices - 2009 vintage
2008
2010
2012
2014
CBO 2005 prices - August 2011 vintage
25
Output Gaps in Real Time:
A Closer Look for the US II
Output gaps - US
3
4
4
2
2
2
0
1
0
P e rce n t
P e rce n t
-2
0
-4
-1
-2
-4
-6
-2
-6
-8
-3
-10
-8
-4
-12
-10
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
January 2008
December 2009
December 2008
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
July 2009
August 2009
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
June 2010
March 2011
December 2010
August 2011
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An Important Addition:
real-time data
Bank of Canada
3
2
1
0
-1
-2
-3
-4
-5
Percent
2000
2002
2004
GAP_200201
GAP_200704
2006
2008
2010
Canada
GAP_200703
GAP_200903
H-P Filter
.2
.1
.0
-.1
-.2
-.3
2000
2002
2004
GAPHP_200201
GAPHP_200704
2006
2008
2010
GAPHP_200703
GAPHP_200903
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Canadian Real Time Data:
A Specific Vintage, 2011 Q1
Realized and Potential Output - CANADA
1,380,000
1,360,000
Millions $
1,340,000
1,320,000
1,300,000
1,280,000
1,260,000
I
II
III
2007
IV
I
II
III
IV
2008
Potential real GDP 2008Q1
Porential real GDP 2010Q1
realized real GDP 2011Q1
I
II
III
2009
IV
I
II
III
IV
2010
I
2011
Potential real GDP 2009Q1
Potential real GDP 2011Q1
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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 GDPLoans}
• 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?
31
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
. 01
. 00
-. 01
-. 02
1
2
3
4
5
6
7
Quarters
8
9
10
11
12
. 08
Response of SLOS to log Loans
. 02
Response of log Loans to SLOS
Response of log real GDP to SLOS
IRFs: US, 1999-2011*
. 04
. 00
-. 04
-. 08
1
2
3
4
5
6
7
8
9
10
11
12
12
8
4
0
-4
-8
1
2
3
4
5
6
7
8
9
10
Quarters
Quarters
FIGURE 3: EXTENDED VAR
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11
12
.01
.00
-.01
-.02
.08
Response of SLOS to log Loans
.02
Response of log Loans to SLOS
Response of log real GDP to SLOS
IRFs: 2009 Q3 Vintage
.04
.00
-.04
-.08
1
2
3
4
5
6
7
Quarters
8
9
10
11
12
12
8
4
0
-4
-8
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
Quarters
Quarters
FIGUR 2c 1st COLUMN, EXTENDED VAR
34
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
35
US Factor Scores for CAD FAVAR
1.8
1.6
1.4
1.2
1.0
Factor scores
0.8
0.6
2000
2002
2006
2004
Ma cro condi ti ons
2008
2010
Ma cro condi ti ons 2009Q3
4
3
2
1
0
-1
-2
2000
2002
2004
Fi na nci a l condi ti ons 2009Q3
2006
2008
2010
Fi na nci a l condi ti ons
Also see FIGURE 5
36
Macro US Factor Scores
2
1
Factor Scores
0
-1
-2
-3
-4
-5
-6
1975
1980
1985
1972-2011
1999-2011
1990
1995
2000
2005
2010
1989-2011
1999-2011 (2009Q3 vintage)
37
Financial US Factor Scores
5
4
Factor Scores
3
2
1
0
-1
-2
1975
1980
1985
1972-2011
1999-2011
1990
1995
2000
2005
2010
1989-2011
1999-2011 (vintage 2009Q3)
38
.0 0 0 2
.0 0 0 0
-.0 0 0 2
-.0 0 0 4
1
2
3
4
5
6
7
8
9
10
11
12
.0 0 8
.0 0 4
.0 0 0
-.0 0 4
-.0 0 8
1
2
3
4
5
6
7
8
9
10
11
12
Response of SLOS to log Business Credit
.0 0 0 4
Response of log Business Credot to SLOS
Response of log real GDP to SLOS
FAVAR for CANADA
12
8
4
0
-4
-8
1
2
3
4
Scaling changed
39
5
6
7
8
9
10
11
12
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
VD: An Illustration
41
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