Challenges for estimating and forecasting
macroeconomic trends during financial crises:
implications for counter-cyclical policies
Pingfan Hong
Chief for Global Economic Monitoring
UN/DESA
International Seminar at Ottawa, Canada
27-29 May 2009
Views expressed here are solely those of the speaker and they do not necessarily represent those of the
United Nations
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Outline
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Introduction
Forecasting performance of UN/LINK global
modeling system
High Frequency Modeling for Rolling estimation
and forecast
“turning point”: Over-year-ago (oya) Quarterly
GDP growth versus Seasonally Adjusted Annual
Rate (SAAR) of Quarterly GDP growth
The importance of correctly estimating
potential output
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Introduction
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Estimating versus forecasting
Estimating:
yte  E ( yt / I t )
Forecasting:
ytf  E ( yt / I t 1 )
Importance of estimating and forecasting for countercyclical macroeconomic policy: timeliness, consistent,
accuracy, “turning point”, and correct estimate of the
potential gap
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Forecasting performance of UN/LINK
global modeling (1)
Figure 1. Forecasting world GDP
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3
1
-1
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
-3
1972
per cent
5
year
Errors
Forecasts
Observed
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Forecasting performance of UN/LINK
global modeling (2)
Figure 2. forecasting GDP for developed countries
7
3
1
-1
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
-3
1972
per cent
5
year
errors
forecast
observed
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Forecasting performance of UN/LINK
global modeling (3)
figure 3. forecasting GDP for developing countries
7
3
1
-1
-3
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
-5
1972
per cent
5
year
errors
forecast
observed
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Forecasting performance of UN/LINK
global modeling (4)
developed
economies
world
developing
countries
Mean
0.02
0.04
-0.36
Median
0.05
0.05
-0.1
0.7
0.76
1.25
Fraction of
positive errors
0.52
0.5
0.42
Serial
correlation
-0.2
-0.1
0.29
Standard
Deviation
Source: DESA
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High Frequency Modeling for rolling
estimating quarterly GDP
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Collecting weekly data stream
Principle Component
ARIMA
Weekly rolling estimate and forecast of quarterly
GDP
Sources for slides 8-12: L.R. Klein and W. Mak, University of Pennsylvania
Current Quarter Model of the United States Economy
Y. Inada, Konan University Current Quarter Model Forecast For the
Japanese Economy
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Example: US weekly data stream
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Date Economic Indicator for Latest and Prior Month
Apr 01 Construction Spending February -0.9% -3.5%
Apr 01 Auto Sales March 9.9 Million 9.1 Million
Apr 02 Manuf Ships, Inv, & Orders February -0.1%, -1.2%, 1.8% -2.6%, 1.1%, -3.5%
Apr 03 Nonfarm Payroll Employment March -663,000 -651,000
Apr 07 Consumer Credit Outstanding February -$7.5 billion $8.1 billion
Apr 09 Export/Import Price Index March -0.6%, 0.5% -0.3%, -0.1%
Apr 09 Trade Balance February -$26.0 billion -$36.2 billion
Apr 15 Producer Price Index, Total & Core March -1.2%, 0.0% 0.1%, 0.2%
Apr 14 Retail Sales, Total & Ex-Auto March -1.1%, 0.9% 0.3%, 1.0%
Apr 15 Industrial Production March -1.5% -1.5%
Apr 14 Business Inventories February -1.3% -1.3%
Apr 15 Consumer Price Index, Total & Core March -0.1%, 0.2% 0.4%,
0.2%
Apr 16 Housing Starts February 510,000 572,000
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Example: indicators used in US model
for estimating quarterly GDP
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Industrial Production Index
Manufacturers’ orders, deflated by producer price index
Manufacturers’ shipments, deflated by producer price index
Manufacturers’ unfilled orders, deflated by producer price index
Yield spread between 6-month commercial paper and 6-month treasury
bills
Real interest rate (6-month commercial paper yield adjusted by
consumer price index)
Real M1, adjusted by consumer price index
Real retail sales, adjusted by consumer price index
Real personal income, adjusted by consumer price index
Real 10-year treasury yield
Yield spread between 10- and 1-year treasury bills
Nonfarm payrolls
Average weekly hours, production workers: total private
Trade-weighted value of the US dollar, nominal broad dollar index
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Example: Equations for GDP and PGDP
in US model
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Dlog (QGDP) = 0684 – 0.954 Dlog C1
+ 0.304 Dlog C2
-0.0661 Dlog C6
– 0.295 Dlog C7
+ 0.581 AR(1)
– 0.677 MA(1)
Dlog (QPGDP) = 0.817 – 2.463 Dlog C1 + 0.925 Dlog C2
+ 1.383 Dlog C3 – 5.113 Dlog C4
+ 4.189 Dlog C5 – 2.233 Dlog C6
+ 0.908 MA(4)
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Example: Japan H-F model forecast versus
consensus forecast
Source: Y. Inada
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Convergence in the rolling forecast of the US H-F model
3
2
1
0
2008q1
q2
q3
q4
2009q1
offical
-1
est m1
-2
est m2
est m3
-3
est m4
-4
-5
-6
-7
Mean error
RMSE
M1
M2
M3
M4
-2.625
-0.5475
-0.665
-0.8375
3.607652
1.853126
1.144312
1.4058
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Convergence in the rolling forecast of the Japan H-F model
Rolling estimate of GDP for Japan
5
0
2008q1
q2
q3
q4
2009q1
offical
-5
per cent
est m1
est m2
est m3
-10
est m4
-15
-20
Mean error
RMSE
M1
M2
M3
M4
-7.3
-6.7
-3.2
-0.7
8.4
7.2
4.5
2.1
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“turning point”: oya versus saar
Example of China’s GDP
ytoya  (Yt / Yt  4 )  1
4
ytsaar  (Yt sa / Yt sa
)
1
1
China GDP Growth: oya vs saar
16
14
12
per cent
10
oya
8
saaq
6
4
2
0
2007q1
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q2
q3
q4
2008q1
q2
q3
q4
2009q1
Sources: China NBS, JPM
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Importance of correct estimate of potential output
for counter cyclical macroeconomic policy
it   t  r *   ( t   * )  (1   )( yt  y* )
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Taylor rule:
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Hodrick-Prescott filter for estimating potential GDP growth :
T
min
(y
t 1
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t
t )
T 1
2
   [( t 1   t )  ( t   t 1 )]2
t 2
Production function for estimating potential GDP growth:
y*  f (k * , l * )
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Estimate of output Gap for the US economy
by H-P filter
US GDP GAp by H-P filter
11800
11700
11600
11500
11400
11300
USA_YGDP
USA_YGDP_HP2005
11200
11100
11000
10900
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05
Q
1
20
05
Q
2
20
05
Q
3
20
05
Q
4
20
06
Q
1
20
06
Q
2
20
06
Q
3
20
06
Q
4
20
07
Q
1
20
07
Q
2
20
07
Q
3
20
07
Q
4
20
08
Q
1
20
08
Q
2
20
08
Q
3
20
08
Q
4
20
09
Q
1
10800
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Estimate output Gap for the US economy
by production function
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Source: Business Week
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Are these Output GAPs corrected
estimated?
Output gap % of GDP
Record levels of
spare capacity
6
4
2
0
-2
High-income
-4
Developing
-6
-8
09 10 11
1970
1975
1980
1985
1990
1995
2000
05
10
Source: World Bank.
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Concluding remarks
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It’s a big challenge to make a timely and
consistent estimate and forecast for
economic trends during financial crisis
But they are crucial for macroeconomic
policies
We can make improvement
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