realtime forecasting with macro-finance models in the presence … · 2016. 5. 5. · rbnz...

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


Outline

1 Motivation

2 Models

3 Results

4 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.



Contribution

Test macro-finance models forecastingperformance in a true real-time environment

Allow for the zero lower bound



Outline

1 Motivation

2 Models

3 Results

4 Conclusion



Slide of Diebold’s slide



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)}



Zero lower bound mechanism



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



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



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



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.



US Data

Sample period 1986 Dec - 2014 Dec

Real-time exercise begins in 1996 Dec

Forecast performance measured against 2015Dec data vintage



Outline

1 Motivation

2 Models

3 Results

4 Conclusion



Results structure

Quasi real-time vs genuine real-time

RMSFE

Economic significance



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 †.



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




Quasi real-time forecasting

Table: Capacity utilisation


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




Real-time forecasting

Table: Capacity utilisation


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 †.



Economic significance

Monitoring quarters: less than two years

Policy relevant quarters: two to four years

Rank model’s (economic) forecast performance foreach horizon



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



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



Outline

1 Motivation

2 Models

3 Results

4 Conclusion



Conclusion

Macro-finance models can improve macroforecast performance

But it’s overstated when using quasi real-timedata



RBNZ Conferences and workshops

CEM December 2015James Hamilton, Tatsuyoshi Okimoto (ANU) askeynotesWelcome participation

February 2016 Housing-Macroprudentialworkshop

For policy-makers/practitionersKeynotes TBCWelcome participation

ERNI


realtime forecasting with macro-finance models in the presence … · 2016. 5. 5. · rbnz...

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