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Motivation Models Results Conclusion
Realtime forecasting withmacro-finance models in the
presence of a zero lower bound
Michelle Lewis and Leo Krippner
RBNZ
21 March 2016
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Outline
1 Motivation
2 Models
3 Results
4 Conclusion
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Outline
1 Motivation
2 Models
3 Results
4 Conclusion
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Literature
Spread of yield curve to forecast activitye.g. Estrella; Stock and Watson
Advancements in yield curve modellinge.g. Singleton; Diebold and Rudebusch; Krippner
Relationship between yield curve factors andeconomic conceptse.g. Diebold et al.; Piazzesi et al.; Bernanke et al.
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Contribution
Test macro-finance models forecastingperformance in a true real-time environment
Allow for the zero lower bound
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Outline
1 Motivation
2 Models
3 Results
4 Conclusion
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Slide of Diebold’s slide
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Yield curve models
RNS(t, τ) = L(t)+S(t)
(1− e−φτ
φτ
)+B(t)
(1− e−φτ
φτ− e−φτ
)−VE (τ)
Zero lower bound mechanism
r(t) = max{0, r(t)}
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Zero lower bound mechanism
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Level, Slope, and Bow
1990 1995 2000 2005 2010 20151
2
3
4
5
6
7
8
9
10
11
Years
Per
cent
Realtime estimates
1990 1995 2000 2005 2010 2015
−6
−4
−2
0
Years
Per
cent
1990 1995 2000 2005 2010 2015−12
−10
−8
−6
−4
−2
0
2
4
Years
Per
cent
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Forecasting models
Macro-only VAR
Yields-only VARs
Macro-finance VARs
ytπtrtLtStBt
=
C10
C20
C30
C40
C50
C60
+
a11 a12 a13 a14 a15 a16
a21 a22 a23 a24 a25 a26
a31 a32 a33 a34 a35 a36
a41 a42 a43 a44 a45 a46a51 a52 a53 a54 a55 a56a61 a62 a63 a64 a65 a66
yt−1
πt−1
rt−1
Lt−1
St−1
Bt−1
+
e1t
e2t
e3t
e4t
e5t
e6t
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Data get revised
US inflation
1985 1990 1995 2000 2005 2010 2015−2
−1
0
1
2
3
4
5
6
7
8
US capacity utilisation
1985 1990 1995 2000 2005 2010 2015−14
−12
−10
−8
−6
−4
−2
0
2
4
6
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Data releases take time
Time Variable r L S B π y ⁞ t-3 t-2 t-1 t O O t+1 x x x x x x t+2 x x x x x x ⁞ x x x x x x
Note: Figure illustrates the missing data in real-time, where inflation and
output data are not available in at time t. ’O’ is the now-cast and ’x’ is
the forecast.
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
US Data
Sample period 1986 Dec - 2014 Dec
Real-time exercise begins in 1996 Dec
Forecast performance measured against 2015Dec data vintage
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Outline
1 Motivation
2 Models
3 Results
4 Conclusion
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Results structure
Quasi real-time vs genuine real-time
RMSFE
Economic significance
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Quasi real-time forecasting
Table: InflationRMSFE Relative RMSFE
Item Macro BM MF Un† MF Partial MF Full AR†
1 1.05 0.97 *** 0.98 0.98 1.122 1.08 0.97 *** 0.98 1.00 1.223 1.09 0.96 *** 0.96 1.00 1.286 1.09 0.97 *** 0.95 1.02 1.3412 1.11 1.03 0.95 1.10 1.3324 1.48 0.84 *** 0.80 *** 0.89 1.0236 1.73 0.75 *** 0.72 *** 0.79 ** 0.8848 1.89 0.65 *** 0.61 *** 0.66 *** 0.83 *
’*’ is significant at the 10 percent level, ’**’ is significant at the5 percent level and ’***’ is significant at the 1 percent level.Diebold-Mariano-West one-sided tests were used, with theClark-West correction for nested models. Nested models areidentified with †.
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Real-time forecasting
Table: InflationItem Macro BM MF Un† MF Partial MF Full AR†
0 1.05 0.99 *** 1.00 0.99 1.111 1.08 0.95 *** 0.96 0.97 1.212 1.09 0.97 *** 0.98 0.99 1.283 1.09 0.97 *** 0.97 1.00 1.316 1.09 0.98 *** 0.95 1.02 1.3512 1.09 1.04 0.96 1.12 1.3524 1.47 0.87 *** 0.81 *** 0.93 1.0436 1.58 0.80 *** 0.74 *** 0.87 * 0.90 *48 1.71 0.71 *** 0.66 *** 0.72 *** 0.89
’*’ is significant at the 10 percent level, ’**’ is significant at the5 percent level and ’***’ is significant at the 1 percent level.Diebold-Mariano-West one-sided tests were used, with theClark-West correction for nested models. Nested models areidentified with †.
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Quasi real-time forecasting
Table: Capacity utilisation
Item Macro BM MF Un† MF Partial MF Full AR†
1 0.53 0.99 *** 0.99 0.99 1.002 0.85 0.97 *** 0.97 * 0.97 0.993 1.17 0.96 *** 0.96 * 0.96 * 0.99 *6 2.19 0.95 *** 0.96 * 0.96 * 0.9912 3.87 0.94 *** 0.95 ** 0.95 * 0.9924 5.70 0.90 ** 0.88 ** 0.90 ** 1.03 *36 6.87 0.81 ** 0.79 *** 0.79 *** 1.0748 8.11 0.74 * 0.74 ** 0.72 ** 1.09
’*’ is significant at the 10 percent level, ’**’ is significant at the5 percent level and ’***’ is significant at the 1 percent level.Diebold-Mariano-West one-sided tests were used, with theClark-West correction for nested models. Nested models areidentified with †.
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Real-time forecasting
Table: Capacity utilisation
Item Macro BM MF Un† MF Partial MF Full AR†
0 1.85 1.02 1.02 1.03 1.021 1.92 1.04 1.05 1.05 1.032 2.04 1.04 1.06 1.06 1.033 2.19 1.04 1.07 1.07 1.036 2.81 1.01 1.06 1.06 1.0312 3.90 0.98 ** 1.03 1.05 1.0324 4.24 0.94 ** 0.98 1.03 1.1536 4.63 0.82 * 0.85 ** 0.90 * 1.3248 5.28 0.73 * 0.78 * 0.90 1.43’*’ is significant at the 10 percent level, ’**’ is significant at the5 percent level and ’***’ is significant at the 1 percent level.Diebold-Mariano-West one-sided tests were used, with theClark-West correction for nested models. Nested models areidentified with †.
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Economic significance
Monitoring quarters: less than two years
Policy relevant quarters: two to four years
Rank model’s (economic) forecast performance foreach horizon
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Monitoring quarters: real-time
Monitoring quarters: less than two years
Rank Inflation Fed funds rate Capacity Utilisation Overall1 MF partial AR MF un MF un2 MF un MF un Macro BM MF partial3 Macro BM MF partial MF partial Macro BM4 MF full Macro BM AR AR5 AR MF full MF full MF full
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Policy relevant quarters: real-time
Policy relevant quarters: Two to four years
Rank Inflation Fed funds rate Capacity Utilisation Overall1 MF partial AR MF un MF partial2 MF un MF partial MF partial MF un3 MF full MF un MF full MF full = 34 AR MF full Macro BM AR = 35 Macro BM Macro BM AR Macro BM
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Outline
1 Motivation
2 Models
3 Results
4 Conclusion
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
Conclusion
Macro-finance models can improve macroforecast performance
But it’s overstated when using quasi real-timedata
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models
Motivation Models Results Conclusion
RBNZ Conferences and workshops
CEM December 2015James Hamilton, Tatsuyoshi Okimoto (ANU) askeynotesWelcome participation
February 2016 Housing-Macroprudentialworkshop
For policy-makers/practitionersKeynotes TBCWelcome participation
ERNI
Michelle Lewis and Leo Krippner Realtime forecasting with macro-finance models