Real Time Data for Norway: Challenges for
Monetary Policy
Tom Bernhardsen, Øyvind Eitrheim, Anne Sofie Jore and Øistein Røisland
Norges Bank
Prepared for the workshop on ”Model evaluation in macroeconomics”
University of Oslo, 6-7. May 2005
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1
Motivation
• Real time data: Challenges for monetary policy
– Monetary policy desicions are taken in real time.
– Macroeconomic data are plagued with inaccuracy,
untimeliness and are subject to frequent revisions.
– How should monetary policy take this into account?
– Are revisions persistent?
– Are revisions predictable?
– Can monetary policy be robustified?
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2
Disposition
• The real-time database for Norway (quarterly vintages 1993:1-2002:4).
• Output gap estimation in real time.
• Monetary policy in real time.
• Optimal simple rules under output gap uncertainty.
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3
• Three main reasons for data revisions:
• Changes due to incomplete information (ordinary revision
cycle)
• Base-year changes
• Major data revisions
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4
• Data revisions in Norway 1993:1-2002:4
• Two major revisions of National Accounts data in less than
10 years
• GDP revised upwards
• Particularly strong upward revisions in the second half of
the 1990’s
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5
Table 1: Annual growth rate of Mainland Norway GDP over five-year periods
Vintage
Vintage 1995q1 Vintage 2002q1 Vintage 2003q4
Period
1974 to 1979
3,6
3,9
3,8
2,0
2,3
2,2
1979 to 1984
1,5
1,8
1,9
1984 to 1989
1,7
2,3
2,3
1989 to 1994
3,1
4,1
1994 to 1999
Table 2: Annual growth rate of Mainland Norway GDP over five-year periods
Period
1991 to 1996
1996 to 2001
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Vintage 2002q1
Vintage 2003q4
3,1
2,3
3,5
3,2
6
• Data revisions: news or noise? (Mankiw and Shapiro, 1986)
• Vintage data for mainland GDP 1993:1 to 2002:4 (labeled
e.g., a, b, c, . . .)
• Description (tables, graphs) log(Yta/Ytb) − log(Yta/Ytb)
• Are revisions characterized by news or noise?
• Let ytf = ytp + εt. Under the news view ytp ⊥ εt while under
the noise view εt ⊥ ytf
• Are revisions to GDP predictable?
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7
Final and Real-Time output growth over 4 quarters
.10
Accumulated revisions over 4, 8 and 12 quarters
.06
12 quarters
.08
.04
.06
Final
.04
.02
.02
.00
.00
4 quarters
-.02
-.02
Realtime
-.04
8 quarters
-.04
93 94 95 96 97 98 99 00 01 02 03
D4YFINAL
D4YREALTIME
(a) Final and Real time output growth
93 94 95 96 97 98 99 00 01 02 03
D4YFL_RT
D8YFL_RT
D12YFL_RT
(b) Accumulated revisions
Figure 1: Final and Real time output growth, accumulated revisions over 4,
8 and 12 quarters 1993.1 – 2002.1
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8
Log output ratio
benchmark 1-benchmark 2
0,08
0,08
0,06
0,06
0,04
0,04
0,02
0,02
0
Benchmark 1: Vintage 1995q1
Benchmark 2: Vintage 2002q1
Benchmark 3: Vintage 2003q4
0
-0,02
-0,02
-0,04
-0,04
-0,06
-0,06
-0,08
1970
-0,08
1975
1980
1985
1990
1995
2000
benchmark 1-benchmark 3
benchmark 2-benchmark 3
0,08
0,08
0,08
0,08
0,06
0,06
0,06
0,06
0,04
0,04
0,04
0,04
0,02
0,02
0,02
0,02
0
0
0
0
-0,02
-0,02
-0,02
-0,02
-0,04
-0,04
-0,04
-0,04
-0,06
-0,06
-0,06
-0,06
-0,08
1970
-0,08
-0,08
1970
1975
1980
1985
1990
1995
2000
-0,08
1975
1980
1985
1990
1995
2000
Figure 2: Log output ratios for three different vintages of Real Time data
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• Are revisions to GDP predictable?
• Real time growth data may deviate considerably from final
growth data.
• For Norwegian data the standard deviation of the regression
is around 1 percentage point, though for some numbers the
deviation is substantially higher
• For Norwegian data the standard deviation of the regression
is around 1 percentage point, though for some numbers the
deviation is substantially higher
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• Evolution of growth data in the subsequent quarters after
first time publication:
• No substantial revisions after one quarter (the standard
deviation unchanged)
• In the second and the third quarter after first time
publication the standard deviation is notably less, around
0.70 percentage point.
• After a year measurement errors are considerably lower,
though some errors remain
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Real time data: 2. observation versus final data
1992.Q4 – 2002.Q2
8
8
6
6
Final data
Final data
Real time data: 1. observation versus final data
1992.Q4 – 2002.Q2
4
Y=1.05x + 0.22
R2=0.69
S=1.11
2
4
Y=0.91x + 0.58
R2=0.71
S=1.08
2
0
0
0
2
4
6
8
Real time data
(a) 1. observation versus final data
0
2
4
6
8
Real time data
(b) 2. observation versus final data
Figure 3: Real-time observations versus final data 1992.4–2002.2
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12
Real time data: 4. observation versus final data
1992.Q4 – 2002.Q2
8
8
6
6
Final data
Final data
Real time data: 3. observation versus final data
1992.Q4 – 2002.Q2
4
Y=1.09x + 0.13
R2=0.88
S=0.70
2
4
Y=1.14x + 0.18
R2=0.89
S=0.69
2
0
0
0
2
4
6
8
Real time data
(a) 3. observation versus final data
0
2
4
6
8
Real time data
(b) 4. observation versus final data
Figure 4: Real-time observations versus final data 1992.4–2002.2
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• Are revisions to GDP predictable? (cont’d)
• Test of H0 : α = β = 0 in ∆ytf − ∆ytr = α + β∆ytr + εt
• Some information may exist, not embedded in the real time
data, which would help us predicting the final outcome.
• Test for additional information Ztp
• Tests for Real time macroeconomic information (labour
market variables, goods market variables, financial
indicators) indicate that final growth data cannot be
predicted beyond the information contained in the numbers
published in real time.
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Table 3: Omitted variable tests (OVT) for additional effects on revisions
from macroeconomic variables
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Labour market variables
New Jobs FOV T (3 ,30)=0.1872[0.8303]
Vacancies FOV T (3 ,30)=0.2616[0.7716]
Employment and vacancies FOV T (3 ,30)=0.1814[0.8350]
Unemployment FOV T (3 ,30)=0.2298[0.7961]
Change in unemployment FOV T (3 ,30)=1.5212[0.2354]
Hours worked FOV T (3 ,30)=0.2616[0.7716]
Goods market variables
Industrial production
FOV T (3,30)=0.1144[0.8923]
D(Industrial production) FOV T (3 ,30)=0.3211[0.7279]
Retail sales
FOV T (3 ,30)=0.069[0.9338]
D(Retail sales) FOV T (3 ,30)=0.2422[0.7864]
New orders FOV T (3 ,30)=0.0671[0.9352]
D(New orders) FOV T (3 ,30)=0.3681[0.6952]
Industrial investment FOV T (3 ,30)=0.2616[0.7716]
D(Industrial investment) FOV T (3 ,30)=0.4538[0.6397]
Bankruptcies FOV T (3 ,30)=0.3716[0.6928]
15
Table 4: Omitted variable tests (OVT) for additional effects on revisions
from macroeconomic variables
Financial market
Credit growth, C1
D(Credit growth, C1)
Credit growth, C2
D(Credit growth, C2)
Credit growth, C3
D(Credit growth, C3)
Nominal effective exchange rate
D(Nominal effective exchange rate)
Slope of the yield curve
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variables
FOV T (3 ,30)=0.0700[0.9324]
FOV T (3 ,30)=0.6087[0.5509]
FOV T (3 ,30)=0.0698[0.9327]
FOV T (3 ,30)=0.7627[0.4755]
FOV T (3 ,30)=0.0682[0.9342]
FOV T (3 ,30)=1.1621[0.3269]
FOV T (3 ,30)=0.3213[0.7277]
FOV T (3 ,30)=0.3036[0.7405]
FOV T (3 ,30)=0.8261[0.1922]
16
• Economic developments in Norway
• No authoritative determination of business cycles in Norway
• The last decades characterized by the deep recession in the
late 1980’s followed by a long expansion in the 1990’s
• The period covered by the Real-time data-base starts close
to the beginning of an expansion that peaks in the late
1990’s and ends with a trough in 2003
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Detrending output in practice
We assume that we can decompose the log of output, yt, in a trend
component, µt, and a cyclical component, zt:
yt = µt + zt
The cyclical component, zt, may be used as a measure of the output gap,
ygapt = yt − µt. There is considerable uncertainty with respect to the
measurement of potential output,
yt∗, and in this paper we will use estimates of the trend, µt, as our estimate
of the potential output.
We follow Orphanides and van Norden (1999) and consider a fairly wide
range of univariate models of the output gap. In the table below we
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18
present seven univariate models of the output gap ranging from simple
deterministic trend models, through filtering models (Hodrick-Prescott),
frequency domain models (band pass) to univariate unobserved
components (UC) models. Orphanides and van Norden (2001) have also
considered bivariate unobserved components models which are estimated
with Kalman filter algorithms.1
In addition to the seven univariate models, we estimate output gaps using
a production function model. We follow the approach in Nymoen and
Frøyland (2000), basing the calculations on a production function for the
sectors manufacturing, construction, services and distributive trade,
accounting for about 34 of production for mainland Norway.2 The
1
The estimation results for the UC-models in Orphanides and van Norden (2001) are based on Kalman
filter algorithms in the TSM-module in GAUSS. We are grateful to Simon van Norden for providing access
to his procedures written in RATS and Gauss for estimating the different models in Table 5.
2
In the univariate methods, we use GDP for mainland Norway. One reason for only taking account of
selected sectors in the Production Function model, is an assumption that the production function model is
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19
aggregated production function is assumed to be of a Cobb-Douglas type
with constant returns to scale. The elasticities are given by the income
factor shares of the two production factors. The weights can according to
the Ministry of Finance be estimated at 23 for person-hours and 13 for real
capital for mainland enterprises.
The equilibrium unemployment rate, trend factor productivity and
potential person-hours-employment are constructed by using a
Hodrick-Prescott filter on the actual series.
less applicable to production in the public sector and agriculture.
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Table 5: Output gap models
QT
UC
HP
PF
Trend/cycle decomposition
Quadratic trend
Unobserved component
(local trend model)
Harvey(1985),Clark(1987)
Hodrick-Prescott (λ = 1600)
Production Function
yt = µt + zt
µt = α + βt + γt2 + εt
µt = δt + µt−1 + ηt
δt = δt−1 + νt
zt = ρ1zt−1 + ρ2zt−2 + εt
T
µt = argmin t=1 {(yt − µt )2 + λ[∆2µt+1 ]}
µt = α̂ + 23 lt∗ + 13 kt∗ + tf p∗t ,
The Production Function based estimates of potential output relies on the
parameters of the Cobb-Douglas function, but also on data for the long
run trends in employment, lt∗, real capital, kt∗, and total factor productivity,
tf p∗t . The equilibrium unemployment rate, trend factor productivity and
potential person-hours-employment are constructed by using a
Hodrick-Prescott filter on the actual series.
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Quadratic trend (QT)
Unobserved component (UC)
10
10
Quasi real-time gap
CL_FLGAP
CL_RTGAP
CL_QRGAP
Real-time gap
5
Final gap
5
Quasi real-time gap
0
0
Real-time gap
Final gap
-5
-5
QT_FLGAP
QT_RTGAP
QT_QRGAP
-10
-10
93
94
95
96
97
98
99
00
01
93
94
95
96
(a) QT
97
98
99
00
01
(b) UC
Hodrick Prescott (HP)
Production function (PF)
10
10
PF_FLGAP
PF_RTGAP
PF_QRGAP
5
5
Quasi real-time gap
Final gap
Quasi real-time gap
0
0
Real-time gap
Real-time gap
-5
Final gap
-5
HP1600_FLGAP
HP1600_RTGAP
HP1600_QRGAP
-10
-10
93
94
95
96
97
(c) HP
98
99
00
01
93
94
95
96
97
98
99
00
01
(d) PF
Figure 5: Final gaps, real-time gaps and quasi real-time gaps 1993.1–2002.1
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Final Output Gap
Upper and Lower bounds
10
10
8
8
6
6
4
4
2
2
0
0
-2
-2
-4
-4
-6
-6
-8
-8
-10
1971
-10
1976
1981
1986
1991
1996
2001
Figure 6: Final output gaps (upper and lower bounds 1971.1–2003.3)
An example of thick modeling (Granger and Jeon, 2004)
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Final Output Gap
Real-time Output Gap
Upper and Lower bounds
Upper and Lower bounds
10
10
10
10
8
8
8
8
6
6
6
6
4
4
4
4
2
2
2
2
0
0
0
0
-2
-2
-2
-2
-4
-4
-4
-4
-6
-6
-6
-6
-8
-8
-8
-8
-10
1993
-10
1994
1995
1996
1997
1998
(a) Final
1999
2000
2001
-10
1993
-10
1994
1995
1996
1997
1998
1999
2000
2001
(b) Real time
Figure 7: Final and Real time output gaps 1993.1–2002.1
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24
Some apparent features include:
- Output gaps measured by the alternative models generally move in the
same direction, both as Real Time and Final gaps.
- Measured in Real Time, most of the gaps are positive for most of the
period but turn negative the last few years. Calculated on Final data,
on the other hand, the gaps are negative in the first part of the period
and positive in the last part.
- The size of the output gaps covers a wide range, particularly measured
as Real Time gaps.
- Measured as Final output gaps, the difference between most of the
models is markedly reduced after the first years of the period.
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25
• Total Revisions are generally large and persistent
• High degree of serial correlation. Coefficients ranging from
0.28 for PF model to 0.93 for QT model
• In five of the eight models, the absolute value of the mean
of total revisions is larger than the absolute value of the
mean output gap
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• Other revisions is the the main contributor to Total
revisions for all models except the LT model
• Data revisions are smaller and less serially correlated
• Standard deviations of Data revisions are smaller than
standard deviations of Other revisions
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Total Revisions
8
8
6
6
4
4
PF
2
2
0
0
-2
-2
-4
HP
-4
UC
-6
-6
-8
-8
QT
-10
-12
1993
-10
-12
1994
1995
1996
1997
1998
1999
2000
2001
(a) Total revisions
Figure 8: Total revisions 1993.2–2002.1
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28
Total Revisions
Upper and Lower bounds
8
8
6
6
4
4
2
2
0
0
-2
-2
-4
-4
-6
-6
-8
-8
-10
-10
-12
1993
-12
1994
1995
1996
1997
1998
1999
2000
2001
(a) Total revisions
Figure 9: Total revisions 1993.1–2001.4
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29
Data Revisions
Other Revisions
8
8
8
8
6
6
6
6
4
4
4
2
2
2
0
0
0
0
-2
-2
-2
-4
-4
-6
-6
-6
-6
-8
-8
-8
-8
-10
-10
-10
-10
-12
1993 1994 1995 1996 1997 1998 1999 2000 2001
-12
-12
1993 1994 1995 1996 1997 1998 1999 2000 2001
-12
4
-2
-4
QT
PF
UC
HP
(a) Data revisions
PF
2
UC
HP
QT
-4
(b) Other revisions
Figure 10: Data revisions and Other revisions 1993.1–2002.1
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30
Data Revisions
Other Revisions
Upper and Lower bounds
Upper and Lower bounds
8
8
8
8
6
6
6
6
4
4
4
4
2
2
2
2
0
0
0
0
-2
-2
-2
-2
-4
-4
-4
-4
-6
-6
-6
-6
-8
-8
-8
-8
-10
-10
-10
-10
-12
1993
-12
-12
1993
-12
1994
1995
1996
1997
1998
1999
(a) Data revisions
2000
2001
1994
1995
1996
1997
1998
1999
2000
2001
(b) Other revisions
Figure 11: Data revisions and Other revisions 1993.1–2002.1
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31
Quadratic trend (QT)
Unobserved component (UC)
10
10
5
5
Data revision
0
0
Total revision
Other revisions
-5
-10
Data revision
Other revisions
-5
Total revision
-10
93
94
95
96
97
98
99
00
01
93
94
95
(a) QT
96
97
98
99
00
01
00
01
(b) UC
Hodrick Prescott (HP)
Production function (PF)
10
10
5
Data revision
5
Data revision
0
0
Other revisions
Other revisions
Total revision
-5
Total revision
-5
-10
-10
93
94
95
96
97
(c) HP
98
99
00
01
93
94
95
96
97
98
99
(d) PF
Figure 12: Total revisions, data revisions and other revisions 1993.1–2002.1
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32
Table 6: Summary Statistics: Output gaps and Total Revisions 1993q1 2002q1
METHOD MEAN SD
MIN
MAX CORR AR
Quadratic Trend
RTGAP
4.23
2.00 -0.67
7.30
0.33
QRGAP
5.22
2.26
0.22
11.10
0.64
FLGAP
-0.22
3.70 -7.43
4.97
1.00
Revisions
-4.39
3.57 -10.35 1.51
0.93
UC model (Clark)
RTGAP
-0.25
0.43 -1.06
1.02
0.22
QRGAP
-0.17
0.72 -1.41
2.81
0.30
FLGAP
0.28
3.14 -5.98
4.95
1.00
Revisions
0.58
3.10 -5.28
4.91
0.82
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33
Table 7: Summary Statistics: Output gaps and Total Revisions 1993q1 2002q1
METHOD MEAN SD
MIN MAX CORR AR
HP1600
RTGAP
0.16
1.56 -2.25 3.09
-0.01
QRGAP
0.41
1.70 -2.35 4.83
0.39
FLGAP
0.20
1.39 -3.59 4.23
1.00
Revisions
0.02
2.13 -4.92 3.06
0.73
Production Function
RTGAP
-0.43
1.96 -5.69 2.68
0.87
QRGAP
0.06
2.20 -5.10 5.05
0.95
FLGAP
-1.08
2.83 -7.21 3.12
1.00
Revisions
-0.65
1.51 -4.34 2.55
0.28
ØEi/7. May 2005
34
• Output gap estimates
• Correlations between Real time and Final estimates of the
output gap range from 0 for the HP1600 model to 0.87 for
the PF model.
• The ratio of the standard deviation of the Total revision to
the standard deviation of the Final estimate of the output
gap (proxy for the Noice-to-Signal ratio) is close to, or
higher than 1 for all models except the PF model, which
exhibits a ratio of 0.53.
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35
• Output gap estimates (cont’d)
• For most models, Real time and Final estimates produce
opposite signs in the output gap with a frequency of around
0.50. The PF model is an exception, with a frequency of
only 0.06.
• The absolute value of the revision exceeds the absolute
value of the final gap with a frequency of 0.50 - 0.70 for
most of the models. Again, the PF model is an exception
with a frequency of 0.17.
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36
Table 8: Summary statistics for Output
METHOD
CORR
Quadratic trend (QT)
0.33
0.22
Unobserved component (UC)
-0.01
Hodrick-Prescott (HP)
0.87
Production Function (PF)
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gaps: 1993q1 - 2002q1
NS OPSIGN XSIZE
0.97
0.44
0.64
0.99
0.53
0.53
1.53
0.53
0.75
0.53
0.06
0.17
37
• Output gap estimates (cont’d)
• Real time data produce in general unreliable estimates of
the output gap for Norwegian data.
• Using comparable models, the results are in line with
findings for US, Australian and Canadian data.
• Results from the Production Function model are somewhat
more encouraging than results from univariate models.
• Further work, using more sophisticated models, will be
useful.
ØEi/7. May 2005
38
• The models fall into three categories:
• Inflexible trends (QT)
• Moderately flexible trends (UC)
• Strongly flexible trends (HP, PF)
ØEi/7. May 2005
39
Growth rates of potential output
6
6
5
5
HP
4
3
4
UC
3
QT
2
2
PF
1
0
1971
1
0
1976
1981
1986
1991
1996
2001
Figure 13: Growth rates of potential output in the QT, UC, HP and PF models.
ØEi/7. May 2005
40
HP-weights (1600)
Centered HP-weights (1600)
.24
.24
.20
t
t
.20
.16
.16
.12
t-4
.12
.08
.04
.00
-.04
1970
1975
1980
t
t-4
t-8
1985
1990
t-12
t-16
t-20
1995
t-8
.08
t-12
t-16
.04
t-4
t-8
.00
2000
t-12
-.04
-60
t-24
t-28
Two-sided
(a) HP-weights (1600)
-50
-40
-30
-20
-10
0
20
30
(b) Centered HP-weights (1600)
HP-weights (50000)
Centered HP-weights (50000)
.10
t
.08
t-4
.10
t
.08
t-4
.06
t-8
.06
.04
t-12
.02
t-16
t-20
t-24
t-28
.00
-.02
1970
10
t-8
t-12
.04
t-16
t-20
.02
.00
1975
1980
t
t-4
t-8
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1985
t-12
t-16
t-20
1990
1995
t-24
t-28
Two-sided
(c) HP-weights (50000)
2000
-.02
-60
-50
-40
-30
-20
-10
0
10
(d) Centered HP-weights (50000)
20
30
41
Table 9: Revision Statistics: Details 1993q1 METHOD
MEAN SD
MIN
MAX
Quadratic Trend (QT)
Total Revision
-4.39
3.57 -10.35 1.51
0.99
1.14 -1.87
3.79
Data Revision
-5.38
2.87 -8.58 -0.33
Other Revision
Unobserved component (UC)
Total Revision
0.58
3.10 -5.28
4.91
0.08
0.55 -1.49
1.79
Data Revision
0.50
3.03 -5.45
4.75
Other Revision
Hodrick-Prescott (HP)
Total Revision
0.02
2.13 -4.92
3.06
0.25
0.91 -2.37
3.25
Data Revision
-0.23
1.75 -2.77
2.21
Other Revision
Production Function (PF)
Total Revision
-0.65
1.51 -4.34
2.55
0.49
1.20 -1.50
4.53
Data
ØEi/7. May
2005 Revision
-1.14
1.00 -2.84
0.70
Other Revision
2002q1
AR
N/S
0.93
0.19
1.03
1.53
0.41
1.65
0.82
-0.43
0.89
1.01
0.18
0.98
0.73
-0.02
0.97
1.53
0.68
1.09
0.28
0.09
0.97
0.58
0.46
0.54
42
Table 10: Summary statistics for Total revisions: 1993q1 - 2002q1
METHOD
Quadratic Trend (QT)
Unobserved component (UC)
Hodrick-Prescott (HP)
Production Function (PF)
ØEi/7. May 2005
MEAN
-4.39
0.58
0.02
-0.65
SD
3.57
3.10
2.13
1.51
AR
0.93
0.82
0.73
0.28
N/S(FL)
1.53
1.01
1.53
0.58
N/S(RT)
2.83
7.34
1.37
0.84
MEAN(DTrev)
0.99
0.08
0.25
0.49
43
• Optimal reaction under output gap uncertainty
– We use a calibrated version of a fairly standard aggregated New
Keynesian macromodel for illustration
– The observed output gap in real time, yto, enters the interest rate rule.
– The measurement error process εot is assumed to follow an AR(1)
process.
– Output gap estimates based on final data and real-time data are
used to estimate ρ.
– We optimize coefficients in the interest rate rule on the basis of a
grid search using the simple loss function L = πt2 + λyt2 .
ØEi/7. May 2005
44
The model
πt = 0.8πt−1 + 0.2Etπt+1 + 0.2yt−1 + 0.1zt + επt
(1)
yt = 0.85yt−1 + 0.1Etyt+1 − 0.1(it−1 − Et−1πt) + 0.05zt−1 + εyt (2)
zt = 0.4zt−1 + 0.6Etzt+1 − 0.2{(it − Etπt+1) − (ift − Etπtf )} + εzt(3)
it = αiit−1 + απ πt + αy yto + α∆y ∆yto
(4)
yto = yt + εot
(5)
εot = 0.7εot−1 + ηto,
ØEi/7. May 2005
σ̂ηto = 1.3
(6)
45
Equation (1) is a “hybrid” open-economy New Keynesian Phillips curve,
where πt is the rate of (CPI) inflation, yt is the “true” (but unobservable)
output gap, zt is the (log of) the real exchange rate, measured as deviation
from the equilibrium real exchange rate, and επt is a cost-push shock.
Equation (2) represents aggregate demand, where it is the nominal
short-term interest rate. it − Etπt+1 is then the real interest rate, and the
neutral real interest rate is for simplicity normalised to zero. Equation (3)
is the UIP condition, where a lag is introduced to better capture observed
dynamics in the real exchange rate (short-run deviations from UIP).
ØEi/7. May 2005
46
Unobserved increase in the true output gap (productivity slowdown).
Number of periods on the horizontal axis.
0.8
1
it
0.6
yt
0.8
0.6
0.4
0.4
0.2
0.2
yto
0
0
-0.2
0
4
8
12
16
20
24
28
32
36
40-0.2
0
0.4
4
8
12
16
0.2
πt
0.3
20
24
28
32
36
40
zt
-0
0.2
-0.2
0.1
-0.4
0
-0.6
0
-0.1
0
4
ØEi/7. May 2005
8
12
16
20
24
28
32
36
40
4
8
12
16
20
24
28
32
36
40
-0.8
Figure 15:
47
Table 11: Optimal coefficients in the interest rate rule for varying degree of
persistence in output gap mismeasurement (benchmark is no uncertainty).
εot = ρεot−1 + ηto, period loss function Lt = πt2 + λyt2
λ
0
0.5
1.0
1.5
2.0
ØEi/7. May 2005
ρ
0.70
0.28
0.70
0.28
0.70
0.28
0.70
0.28
0.70
0.28
-
απ
4.6
4.6
9.9
3.5
3.7
9.9
2.7
2.8
7.9
2.3
2.5
7.1
2.1
2.3
6.5
αy
0.4
0.6
3.0
0.8
1.2
6.8
0.7
1.2
7.6
0.6
1.2
8.2
0.6
0.7
8.6
α∆y
0.0
0.0
2.0
0.9
0.6
3.2
0.9
0.6
2.6
0.9
0.6
2.2
1.0
0.6
2.0
S.E.(π )
S.E.(y )
E[L]
0.71
0.70
0.61
0.81
0.79
0.75
0.91
0.90
0.86
0.98
0.96
0.92
1.03
1.01
0.98
1.53
1.44
1.28
1.25
1.15
0.90
1.15
1.03
0.75
1.10
0.98
0.69
1.07
0.96
0.64
0.50
0.49
0.37
1.44
1.28
0.96
2.15
1.87
1.30
2.78
2.37
1.56
3.37
3.38
1.77
48
• Consequences for optimal monetary policy in the model
exercise
– Output gap mismeasurements give rise to excessive
variability in output and prices.
– Numerically, output gap mismeasurements reduce the
optimal coefficients considerably compared the full
information case.
– In particular, the weight on the output gap should be
smaller.
– Output gap mismeasurements tend to be persistent. A
case for responding to the change in the gap.
(Orphanides et al, 2000).
ØEi/7. May 2005
49
• Main welfare costs in the model exercise
– Monetary policy does not respond quickly enough to
changes in the true output gap.
– Inflation moves too far away from the target and closes
the output gap too slowly.
– Caveat: The results on the optimal monetary policy
response may be model dependent and a robustness
check is called for.
ØEi/7. May 2005
50
• Wrapping up:
– Data revisions may change our perception of economic
growth both in the near but also more distant past.
– Output gap revisions are often large. Estimates are
subject to data uncertainty, parameter uncertainty and
(more generally) model uncertainty, and may turn out as
unreliable indicators of the current state of the economy.
– Real time output gap uncertainty may seriously misguide
interest rate setting.
– Output gap uncertainty seems to cause a considerable
change in the optimal monetary policy response to
macroeconomic disturbances (coefficient reduction).
ØEi/7. May 2005
51
References
*References
Granger, C. W. J. and Y. Jeon (2004). Thick modeling. Economic
Modelling , 21 , 323–343.
Mankiw, N. G. and M. D. Shapiro (1986). News or noise: An analysis of
GNP revisions. Survey of Current Business, (May), 20–25.
Nymoen, R. and E. Frøyland (2000). Output gap in the Norwegian
economy - different methodologies, same result? Economic Bulletin
2000/2 , 71 , 46–52.
Orphanides, A. and S. van Norden (1999). The reliability of output gap
ØEi/7. May 2005
52
estimates in real time. Upublished Paper, Board of Governors of the
Federal Reserve System and Ecole des Hautes Etudes Commerciales,
Montreal.
Orphanides, A. and S. van Norden (2001). The reliability of inflation
forecasts based on output gap estimates in real time. Upublished Paper,
Board of Governors of the Federal Reserve System and Ecole des Hautes
Etudes Commerciales, Montreal and CIRANO.
ØEi/7. May 2005
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