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Empirically Evaluating Economic Policy in Real Time
John B. Taylor
The Martin Feldstein LectureNational Bureau of Economic Research
July 10, 2009
The Purpose of Today’s Feldstein Lecture
• To evaluate macroeconomic policy during the crisis, focusing on the period since the panic in the fall 2008 by comparing actual policy with counterfactuals:– About monetary policy– About fiscal policy• Each counterfactual is composed of several sub-
counterfactuals• “Real time” because the crisis is ongoing
Why the Emphasis on “Real Time”?
• The financial crisis has made it essential – You have to evaluate policy quickly to be helpful
• Interesting: loads of new data and new policies• Raises new questions about the use of high
frequency data and simulation techniques• It really is different
How is it different?• Most macro-money research not done in real time
– Taylor (1979): • fixed growth rate rule would work better than actual policy but
not as good as optimal policy: 1953-75– Feldstein and Stock (1997):
• evaluated policy rules for M2: 1959-92– Cecchetti, Flores-Lagunes, Krause (2006):
• Has Monetary Policy Become More Efficient? 24 countries: 1982-1998
– Bernanke (2005) • Analysis of Great Moderation: pre and post 1984
• A recent exception – Lars Svensson (2009):
• modified Taylor Curve based on forecasts• Evaluates Riksbank policy: February 2009
The Counterfactuals
Figure 1 from Taylor (1979)
Figure 1 from Bernanke (2005), “The Great Moderation”
Figure 3. The modified Taylor curve
Figure 3 from Svensson (2009) “Evaluating Monetary Policy”
Four Phases of the Crisis
• The Root Causes – 2003-2006– The Great Deviation?
• From Flare-up to Panic: Aug 2007- Sept 2008– The Great Delay?
• Panic of Sept 2008– The Great Scare?
• Post Panic Period: Sept 2008- present
The Focus of this Lecture
The Panic of Fall ‘08
The Panic of Fall ‘08
Actual Monetary Policy Since the Panic of 2008
• During the week of 17 Sept 2008, reserve balances rose, for the first time, above the level required to keep the federal funds rate on target, which was then 2% – Quantitative easing (QE) began before the funds rate hit 0– Reserves were increased to finance loans, first to
investment banks through PDCF, and to purchase securities
– Reserves were not increased to accommodate a shift in money demand, or a decline in velocity
• Reserves then continued to increase through end of 08 – To finance additional securities purchases and loans– Drove interest rate to zero , which FOMC then ratified– Have remained elevated and are expected to increase
0
200
400
600
800
1,000
2005 2006 2007 2008 2009
Reserve Balances of
Depository Institutions at
Federal Reserve Banks
Billions of dollars
September 10, 2008$ 8 Billion
December 31, 2008$ 848 Billion
0
200
400
600
800
1,0003
0 J
UL
13
AU
G
27
AU
G
10
SE
P
24
SE
P
08
OC
T
22
OC
T
05
NO
V
19
NO
V
03
DE
C
17
DE
C
31
DE
C
14
JA
N
28
JA
N
11
FE
B
25
FE
B
11
MA
R
25
MA
R
08
AP
R
22
AP
R
06
MA
Y
20
MA
Y
03
JU
N
17
JU
N
Weekly AverageWednesday Level
Billions of dollars
400
800
1,200
1,600
2,000
2,400
30
JU
L
13
AU
G
27
AU
G
10
SE
P
24
SE
P
08
OC
T
22
OC
T
05
NO
V
19
NO
V
03
DE
C
17
DE
C
31
DE
C
14
JA
N
28
JA
N
11
FE
B
25
FE
B
11
MA
R
25
MA
R
08
AP
R
22
AP
R
06
MA
Y
20
MA
Y
03
JU
N
17
JU
N
SecuritiesReposLoans from TAFOther loansPrivate PortfolioCB Liquidity SwapOther Assets
Billions of dollars
Major Factors Supplying Reserves
0
100
200
300
400
500
600
700
800
900
30
JU
L
13
AU
G
27
AU
G
10
SE
P
24
SE
P
08
OC
T
22
OC
T
05
NO
V
19
NO
V
03
DE
C
17
DE
C
31
DE
C
14
JA
N
28
JA
N
11
FE
B
25
FE
B
11
MA
R
25
MA
R
08
AP
R
22
AP
R
06
MA
Y
20
MA
Y
03
JU
N
17
JU
N
Commercial Paper Funding FacilityMaiden Lane I (Bear Stearns)Maiden Lane II (AIG)Maiden Lane III (AIG)Mortgage Backed SecuritiesGSE Debt
Billions of dollars
Private Portfolio
0
200
400
600
800
1,000
0.0
0.5
1.0
1.5
2.0
2.5
30 J
ul6
Aug
13 A
ug20
Aug
27 A
ug3
Sep
10 S
ep17
Sep
24 S
ep1
Oct
8 O
ct15
Oct
22 O
ct29
Oct
5 N
ov12
Nov
19 N
ov26
Nov
3 D
ec10
Dec
17 D
ec24
Dec
31 D
ec
billionsof dollars
percent
Reserve Balances(left scale)
Effective Federal Funds Rate(right scale)
Timing of FOMC Decisions: Fall 2008• 10/8: FOMC votes to cut funds rate to 1.5 % from 2.0 %
– But for the 2 weeks ending Oct 8, the funds rate was already well below 2.0 %, averaging 1.45 %
• 10/29: FOMC votes to cut funds rate to 1 % from 1.5% – But for the 2 weeks ending Oct 29, the funds rate was
already well below 1.5 %, averaging .76 % • 12/16: FOMC votes to cut funds rate to 0-.25% from 1%
– But for the 2 weeks ending Dec 17, the rate was already in that range, averaging .14 %
• Thus, decisions to increase reserve balances drove down the funds rate rather than the FOMC decisions about the target rate.
Choosing a Counterfactual Monetary Policy
• A general counterfactual: When the optimal interest rate (e.g. Taylor rule) hits “zero” (or slightly positive, say .25%)• Trading Desk keeps reserve balances at a level consistent
with .25 % and keeps the growth rate of money from falling– like a CGRR
• This is how quantitative monetary models are usually simulated for policy evaluation. (Wieland presentation yesterday)
• Avoids Great Depression or Japan Lost Decade mistakes where money growth declined
• A more specific counterfactual, closer to actual policy: • MBS = $0 (rather than $500B and rising to $1,200B) • TAF = $0 (rather than as much as $500B), • Purchases of LT Treasuries = $0 (rather than up $300B)
0
200
400
600
800
1,000
2005 2006 2007 2008 2009
Reserve Balances of
Depository Institutions at
Federal Reserve Banks
Billions of dollars
September 10, 2008$ 8 Billion
December 31, 2008$ 848 Billion
0
200
400
600
800
1,000
2005 2006 2007 2008 2009
Counterfactual Reserve Balances of
Depository Institutions at
Federal Reserve Banks
Billions of dollars
September 10, 2008$ 8 Billion
0
200
400
600
800
1,0003
0 J
UL
13
AU
G
27
AU
G
10
SE
P
24
SE
P
08
OC
T
22
OC
T
05
NO
V
19
NO
V
03
DE
C
17
DE
C
31
DE
C
14
JA
N
28
JA
N
11
FE
B
25
FE
B
11
MA
R
25
MA
R
08
AP
R
22
AP
R
06
MA
Y
20
MA
Y
03
JU
N
17
JU
N
Weekly AverageWednesday Level
Billions of dollars
0
200
400
600
800
1,0003
0 J
UL
13
AU
G
27
AU
G
10
SE
P
24
SE
P
08
OC
T
22
OC
T
05
NO
V
19
NO
V
03
DE
C
17
DE
C
31
DE
C
14
JA
N
28
JA
N
11
FE
B
25
FE
B
11
MA
R
25
MA
R
08
AP
R
22
AP
R
06
MA
Y
20
MA
Y
03
JU
N
17
JU
N
Counterfactual Weekly Average
Billions of dollars
400
800
1,200
1,600
2,000
2,400
30
JU
L
13
AU
G
27
AU
G
10
SE
P
24
SE
P
08
OC
T
22
OC
T
05
NO
V
19
NO
V
03
DE
C
17
DE
C
31
DE
C
14
JA
N
28
JA
N
11
FE
B
25
FE
B
11
MA
R
25
MA
R
08
AP
R
22
AP
R
06
MA
Y
20
MA
Y
03
JU
N
17
JU
N
SecuritiesReposLoans from TAFOther loansPrivate PortfolioCB Liquidity SwapOther Assets
Billions of dollars
Major Factors Supplying Reserves
400
800
1,200
1,600
2,000
2,400
30
JU
L
13
AU
G
27
AU
G
10
SE
P
24
SE
P
08
OC
T
22
OC
T
05
NO
V
19
NO
V
03
DE
C
17
DE
C
31
DE
C
14
JA
N
28
JA
N
11
FE
B
25
FE
B
11
MA
R
25
MA
R
08
AP
R
22
AP
R
06
MA
Y
20
MA
Y
03
JU
N
17
JU
N
CF SecuritiesReposOther loansCF Private PortfolioCB Liquidity SwapOther Assets
Billions of dollars
Major Factors Supplying Reserves
0
100
200
300
400
500
600
700
800
9003
0 J
UL
13
AU
G
27
AU
G
10
SE
P
24
SE
P
08
OC
T
22
OC
T
05
NO
V
19
NO
V
03
DE
C
17
DE
C
31
DE
C
14
JA
N
28
JA
N
11
FE
B
25
FE
B
11
MA
R
25
MA
R
08
AP
R
22
AP
R
06
MA
Y
20
MA
Y
03
JU
N
17
JU
N
Commercial Paper Funding FacilityMaiden Lane I (Bear Stearns)Maiden Lane II (AIG)Maiden Lane III (AIG)Mortgage Backed SecuritiesGSE Debt
Billions of dollars
Private Portfolio
0
100
200
300
400
500
600
700
800
9003
0 J
UL
13
AU
G
27
AU
G
10
SE
P
24
SE
P
08
OC
T
22
OC
T
05
NO
V
19
NO
V
03
DE
C
17
DE
C
31
DE
C
14
JA
N
28
JA
N
11
FE
B
25
FE
B
11
MA
R
25
MA
R
08
AP
R
22
AP
R
06
MA
Y
20
MA
Y
03
JU
N
17
JU
N
Commercial Paper Funding FacilityMaiden Lane I (Bear Stearns)Maiden Lane II (AIG)Maiden Lane III (AIG)GSE Debt
Billions of dollars
Counterfactual Private Portfolio
140
160
180
200
220
240
260
280
300
Jan 07 Jul 07 Jan 08 Jul 08 Jan 09 Jul 09
30 year fixed mortgage rateminus 10 year Treasuries
Basis points
0
100,000
200,000
300,000
400,000
500,000
Jan 07 Jul 07 Jan 08 Jul 08 Jan 09 Jul 09
Millions of dollars
Fed purchases of MBS
0
20
40
60
80
100
120
140
160
07M01 07M07 08M01 08M07 09M01 09M07
CDS on FNMA Bonds
Basis Points
Spread: FNMA Bonds - Treasuries
140
160
180
200
220
240
260
280
300
Jan 07 Jul 07 Jan 08 Jul 08 Jan 09 Jul 09
Basis Points
30 year fixed mortgage rateminus 10 year Treasuries
with MBSpurchases
withoutMBS purchases
0
1
2
3
4
5
0
100
200
300
400
500
600
Jan 07 Jul 07 Jan 08 Jul 08 Jan 09
Term Auction Facility(right scale)
Lois
Libor-repo
PercentBillions of dollars
0
50,000
100,000
150,000
200,000
2.0
2.5
3.0
3.5
4.0
4.5
Oct 08 Jan 09 Apr 09
Ten Year Treasury Yield(right axis)
Millions of Dollars
Cumulative Fed Purchasesof Long Term Treasuries(left axis)
Percent
What About Fiscal Policy?
• What should be the counterfactual? – Only the automatic stabilizers?
• No stimulus package in 2008? • No stimulus package in 2009?
• Still too early for 2009, but here is a preliminary look…
9,200
9,600
10,000
10,400
10,800
11,200
Jan 07 Jul 07 Jan 08 Jul 08 Jan 09
Personal consumptionexpenditures
Disposable personalincome
without stimulus package
Billions of dollars
Here We Go Again?
Deficit, Federal Government Purchases, and Transfers to State and Local Governments for Purchases of Goods and Services in
the February 2009 Stimulus Legislation (billions of dollars)
Fiscal Increase Increase Increase Year in Federal in Transfers to in
FederalPurchases States, Localities Deficit
2009 21 48 1842010 47 107 4002011 46 47 1342012 36 8 362013 25 4 271014 27 0 222015 11 0 52016 -2 0 -82017 -3 0 -72018 -2 0 -6
Table 3 from Cogan, Cwik, Taylor, Wieland (2009)
What about government purchases?
New Keynesian Estimate of Impact of Government Purchases
G is about as forecast by CCTW in Q1 and Q2
-.4
-.2
.0
.2
.4
.6
.8
2009 2010 2011 2012 2013
Government purchases (G)
Consumption (C)
Investment (I)
C plus I
Percent of GDP
Conclusion• The financial crisis demonstrates the need for
real time policy evaluation.– Fast-moving events, unprecedented policy responses– 24 hr news cycle, blogs, quick spread of 1st idea– Good policy depends on it – Will not end with the crisis
• We now can collect and process data rapidly. – No reason to wait 30 years as Friedman/Schwartz
• Can do better:– More higher-frequency data (eg. Diebold)– Improved techniques (eg. Svensson)– Better outlets (Journal of Real Time Policy Evaluation)