Norges Bank
How do central banks evaluate their
models? The case of NEMO
Kjetil Olsen and Leif Brubakk
Modelling group
Norges Bank
Norges Bank
Overriding evaluation criteria from a central
bank perspective:
“How useful the model proves to be in helping the
policy makers conduct monetary policy”
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The context
• Norges Bank has operated a flexible inflation
targeting regime since March 2001
• Key questions to be answered:
– What should interest rates be now and in the
future?
– What constitutes robust monetary policy?
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Key tasks
• Identifying shocks / making projections
• Risk analysis and policy analysis
• Communication
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A core model within a suite of models
• Benefits to a ‘suite of models approach’
• But for structuring and disciplining the discussion and
ensuring consistency, useful to synthesise
information within a flexible core model
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A core model within a suite of models
• Benefits to a ‘suite of models approach’
• But for structuring and disciplining the discussion and
ensuring consistency, useful to synthesise
information within a flexible core model
• Our solution: FPAS
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The Forecasting and Policy System
Other
theoretical
& empirical
models
Current/ Nearterm analysis
Regional
network
Core
model
Qualitative
information
and
judgement
Forecasts/
scenarios
Policy
advice
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Three dimensions
Tool for policy decisions
Data
Theory
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Key attributes for a central bank core
macroeconomic model
• It must be tractable
• It must be interpretable
• It must work with, rather than against, the policymakers
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A core macro model must work with
the policymakers
Must reflect consensus views about monetary policy
• In the long-run:
– No trade off between level of inflation and growth rate
of output
– Inflation is decided by monetary policy
• Monetary policy therefore has a role to play. In the core
model it must be both necessary and possible that
monetary policy anchors inflation at the target.
• Expectations must play an explicit role and be
endogenous
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Model choices in a larger context
• Tension in policy-making:
– Disciplined debate and decision-making
– The risk of narrow thinking
• Reflected in many choices:
– One model versus a suite
– Imposing structure versus “letting the data speak”
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Summary: Key design and evaluation criteria for
a core macroeconomic model:
1.
A core macro model must explain data. Moreover, it must be
confronted with as much empirical evidence as possible.
2.
A core macro model must reflect consensus views about
monetary policy, including the premises on which inflation
targeting as a framework for monetary policy is built, and
about key economic relationships. In particular, a core macro
model must incorporate that
–
there is no trade off in the long term between the level of
inflation and the growth rate of real output (but there is in
the short- to medium term);
–
monetary policy has a clear role of anchoring inflation at
the target level;
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Summary: Key design and evaluation criteria for
a core macroeconomic model:
3.
A core macro model must reflect that agents not only take
account of today’s economic policy, but also form expectations
regarding future policy, and act accordingly. Expectations must
be endogenous and modelled explicitly.
4.
A core macro model must be interpretable, i.e have a clear
and consistent economic structure and a well-defined steady
state.
5.
A core macro model must be tractable, that is, neither too big
or complex nor too small or stylised.
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Norges Bank’s choice: NEMO
•
Medium size DSGE model, founded on New Neoclassical Synthesis
•
NEMO represent consensus views about monetary policy
–
–
–
No trade-off between inflation and output in the long term
Monetary policy affects the real economy in the short to medium term
Monetary policy has clear role; providing a nominal anchor
•
Expectations play an explicit role
•
Mechanisms and disturbances are economically interpretable in a
consistent fashion
•
Well-defined steady-state.
•
Nominal and real rigidities to match stylised facts
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Quantifying NEMO
1.
First step: Calibration based on a range of information:
–
–
–
–
–
–
–
2.
Great ratios
Identified VARs
Moment-analysis/stylised facts (Stock/Watson)
Single equation (econometric) analysis
Micro evidence
Evidence from other models and countries
Experience and knowledge within the institution
Next step: System estimation
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Empirical evaluation
• Many different “classical” approaches for estimating
DSGE models considered in the literature (see RugeMurcia, 2003): ML, GMM, SMM and Indirect
inference.
• Mixed results, models too stylised to capture the
data.
• However, recently some promising work using
Bayesian techniques (Smets and Wouters, Adolfson
et al.)
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Empirical evaluation
• Bayesian approach attractive for several reasons:
– Allows for likelihood based inference
– Incorporates prior beliefs in formal and consistent way
– When priors are informative, more efficient estimates are
obtained.
– Priors reduce the problem of flat likelihood
• Model evaluation along lines of Del Negro et al.
– Both in-sample and out-of-sample fit against a large set of
models
• Other evaluation approaches
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Conclusion
• Empirical model evaluation is important
• But not the only concern for a central bank regarding
the evaluation of how useful a core model is in
helping to run monetary policy
• We believe NEMO can become a useful tool for our
purposes, as a core macro model within a suite of
models
Norges Bank
How do central banks evaluate their
models? The case of NEMO
Kjetil Olsen and Leif Brubakk
Modelling group
Norges Bank
The Stylised Facts: A Monetary Policy Shock
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GDP
Nominal interest rate
0.1
1.2
1.0
0.0
0.8
-0.1
0.6
-0.2
0.4
0.2
-0.3
0.0
-0.4
-0.2
-0.5
-0.4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1
16
2
3
4
5
6
7
Real exchange rate
0.4
8
9
10
11
12
13
10
11
12
13
14
15
16
Inflation
0.2
0.05
0.0
0.00
-0.2
-0.05
-0.4
-0.10
-0.6
-0.15
-0.8
-0.20
-1.0
-0.25
-1.2
-0.30
1
2
3
4
5
6
7
Source: Bjørnland (2005)
8
9
10
11
12
13
14
15
16
1
2
3
4
5
6
7
8
9
14
15
16
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Empirical evaluation
• Traditional econometric evaluation of each equation:
–
–
–
–
Fit
Parameter constancy
Theory consistency (satisfied by construction)
Tests against rival models
• The rival models may be used in the FPAS - to crosscheck the results from NEMO