Transcript Macromodels at Central Banks: Quo Vadis?

DSGE Modelling at Central Banks:
Quo Vadis?
Frank Smets
Royal Economic Society Annual Conference
30 March 2010
The views expressed are my own and not necessarily those of the ECB.
This presentation is based on work by many ECB colleagues, in particular Kai Christoffel,
Günter Coenen, Roberto Motto, Massimo Rostagno and Anders Warne.
1
Outline
1. Introduction
2. Structure of Bayesian DSGE models at the ECB
3. Some empirical applications
4. The financial crisis and recent criticism
5. Conclusions and way forward
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I. Introduction
3
Introduction
• Monetary policy makers need a wide range of
macro-econometric models/tools
• for forecasting
• and for policy analysis
• More and more institutions (Fed, ECB, IMF,
Sveriges Riksbank, BoC, Norges Bank, CNB, …)
include Bayesian Dynamic Stochastic General
Equilibrium (DSGE) models in their tool kit for
monetary policy analysis and forecasting.
4
Introduction
• Bayesian DSGE models combine
• a sound micro-founded DSGE structure, characterised by
the derivation of behavioural relationships from the
optimising behaviour of agents subject to technological
and budget constraints and the specification of a welldefined general equilibrium,
 which makes it suitable for policy analysis;
• with a full-system Bayesian likelihood estimation,
 which provides a good probabilistic description of the
observed data and a good forecasting performance.
• See Smets and Wouters (2005)
5
Introduction
• Advantages of the DSGE approach:
– The general equilibrium structure lends itself to telling
economically coherent stories and structuring forecastrelated discussions accordingly.
– Information about “deep” structural parameters can be
used to calibrate/estimate the model (e.g. breaks, short
time-series) and the model structure (e.g. cross-equation
restrictions) helps to identify parameters and the type of
shocks and to reduce the risk of over-fitting.
– Less subject to the Lucas critique and more suitable for
policy analysis.
– Puts a premium on expectations
– Better feel for which parameters are likely to be policy
invariant and which ones are not.
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Introduction
• Advantages of Bayesian likelihood approach:
– Formalises the use of prior information and helps
identification, thereby also making the estimation
algorithm of the highly restricted model much more
stable.
– Delivers a full characterisation of the parameter and
shock uncertainty, allowing to construct probability
distributions for unobserved variables (e.g. output gap)
and derived functions (e.g. forecasts)
– Very flexible approach to deal with measurement error,
unobservable variables, different sources of information.
– Provides a framework for empirically evaluating models
and the appropriate input for model averaging and
Bayesian decision making under model uncertainty.
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II. Structure of Bayesian DSGE
models used at the ECB
8
Motivation
• Currently, two Bayesian DSGE models are routinely
used at the ECB:
• New Area Wide
Model (NAWM) (Christoffel et al, 2008)
• Used for forecasting (in the context of the quarterly ECB Projection
Exercises) and policy analysis;
• There is also a calibrated version, relatively richer in detail and open
for topic-driven extensions (e.g. fiscal policy).
• Christiano-Motto-Rostagno
(CMR) Model
• Supports the cross checking through monetary/financial scenarios
• Both models have a similar core, based on Smets
and Wouters (2003, 2007), but
• NAWM includes a detailed international block
• CMR includes a detailed financial block
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Models Overview: Core Structure
Monetary Policy
Households
- consumption/saving decisions
- labour supply
Markets: imperfect competition &
price and wage setting as a markup
Government
Production
- Combine labour and capital
Core structure of both models
Smets-Wouters (2003, 2007)
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Models Overview: NAWM
Monetary Policy
Households
- consumption/saving decisions
- labour supply
Markets: imperfect competition &
price and wage setting as a markup
Government
Production
- Combine labour and capital
Equations:
• about 50 endogenous
variables
• autoregressive processes
for the model's structural
shocks and the AR/VAR
models for government
consumption and the
foreign variables
• measurement equations
and identities
Exports
Rest of the World
Final Goods
Exchange Rate,
Foreign Demand,
Oil price …
Combining domestic and
imported goods
Imports
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Models Overview: CMR
Portfolio Decisions
Monetary Policy
Households
- consumption/saving decisions
- labour supply
Markets: imperfect competition &
price and wage setting as a markup
Government
Financial Intermediation
Liabilities
- Deposits
(components
of M1, M3)
Assets
- Reserves
- Loans
Production
- Combine labour and capital
External Financing
• The CMR expands on the core structure with a monetary and financial block
which interacts directly with consumption, investment and price setting.
• The different focus of the two models (international dimension in NAWM and
monetary/financial in CMR) make them usable for complementary purposes
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Data, Model and Shocks
Structural
Shocks
Model
as an approximation of reality
Demand
Technological Forces (e.g TFP)
Exogenous Shifts in Markups
Macroeconomic
Variables
National accounts,
Inflation, 3-month interest rate
International (in NAWM)
[exchange rate; deflators; foreign
demand; foreign prices; U.S. FFR;
competitors' export prices]
Policy
International (in NAWM)
Financial (in CMR)
Financial (in CMR)
[M3; M1; credit; stock-market index;
external finance premium; 10-year
and 3-month interest rate spread]
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3. Some empirical applications
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Monetary Policy Transmission
Unanticipated Increase in Interest Rate by 50 bp
Output
Inflation
Interest Rate
0.1
60
0
0.05
-0.1
0
Simple Model
NAWM
CMR
40
-0.05
-0.2
-0.1
-0.3
20
-0.15
-0.4
Simple Model
-0.5
NAWM
CMR
0
5
10
Quarters
15
Simple Model
-0.2
-0.25
20
0
NAWM
CMR
0
5
10
Quarters
15
20
-20
0
5
10
Quarters
15
20
• Response is qualitative same across models
• Considering uncertainty, quantitative differences small
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Monetary Policy Transmission: NAWM
0
0.1
0
-0.1
0
-0.1
0
-0.2
-0.05
-0.2
-0.3
-0.1
-0.4
-0.5
-0.1
-0.3
-0.4
-0.2
-0.15
-0.6
-0.5
-0.7
Output
Consumption
Investment
-0.8
-0.9
-0.3
0
5
10
15
Quarters
20
0.1
0
-0.1
-0.2
-0.3
-0.4
Rental Rate Capital
Wages
Marginal Costs
-0.6
-0.7
0
5
10
15
Quarters
-0.4
0
5
10
15
Quarters
Domestic Inflation
Consumption Inflation
real Ex. Rate
20
-0.7
0
5
10
15
Quarters
20
-0.25
0
5
10
15
Quarters
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Additional channels in open economy:
• Appreciation of currency
• Drop in exports amplifies output reaction
• Reduction in import prices implies
stronger inflation response
0.2
-0.5
-0.2
-0.6
Export
Imports
20
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Monetary Policy Transmission: NAWM
Output
Inflation
Real Effective Exchange Rate
0.2
0.1
0
0
0.05
-0.1
0
-0.2
-0.05
-0.2
-0.4
-0.1
-0.3
-0.15
-0.4
-0.6
-0.2
-0.8
-0.25
-0.5
-1
-0.3
NAW M
Constant Nominal Ex. Rate
-0.6
-0.35
0
5
10
Quarters
15
20
0
5
10
Quarters
15
-1.2
20
0
5
10
15
20
Quarters
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Monetary Policy Transmission: CMR
Deterioration in
Economic Activity and
Profit Prospects
Balance Sheet
Deterioration and Rise in
Bankruptcy Rate
Rise in External Finance
Premium (over and above
risk-free rate). Countercyclical
0.3
30
0.25
25
Bankruptcy Rate
20
basis points
Percent
0.2
Marginal
0.15
15
10
0.1
Average
5
0.05
2
4
6
8
10
12
14
16
18
2
20
4
6
8
10
12
14
16
18
20
Output
-0.05
CMR with indexed financial contracts
-0.15
Percent
Price Developments Originally
Unforeseen Can Affect
Financial Position and Real
Economy
-0.25
CMR baseline
-0.35
-0.45
5
10
15
20
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Historical analysis
Structural
Shocks
Model
as a lens to interpret reality
Macroeconomic
Variables
• Model as lens to interpret reality
• Identifying structural shocks
• Decompose variables into contributions from
shocks: Historical decomposition
• Example: How have the two models
interpreted the current recession?
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GDP growth in the euro area
NAWM
20
GDP growth in the euro area
CMR
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Examples of scenario analysis
• NAWM:
• Risks of deflation and the lower-bound on interest rates
• The financial crisis and potential output growth
developments
• Risks of a de-anchoring of inflation expectations
• The effects of fiscal consolidation
• CMR:
• Cross-checking economic with monetary analysis;
• The macro-economic impact of bank deleveraging and
recapitalisation;
• The economic impact of excess reserves in the banking
sector
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4.The financial crisis and recent
criticism
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Recent criticism 1
Too much reliance on “complete markets
hypothesis” and limited role for financial factors,
debt and default, and asymmetric information
problems.
• E.g. Goodhart, 2007; Buiter, 2009.
– Ignores the progress made:
– BGG (2000), CMR (2005), Iacoviello and Neri (2007), …
– DSGE models with heterogenous agents
– Portfolio choice and risk premia
– But a lot of this work is still in its infancy.
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Procyclicality of the financial system
• There is need to better understand the sources behind the
procyclicality of the financial system:
• „The mutually reinforcing mechanisms through which the
financial system can amplify business cycle fluctuations and
possibly cause and exacerbate financial instability“ (BIS)
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2008Q3
2008Q1
1.0
2007Q3
1.5
2007Q1
2006Q3
2006Q1
2005Q3
2005Q1
2004Q3
2004Q1
2003Q3
2003Q1
2002Q3
2002Q1
2001Q3
2001Q1
2000Q3
2000Q1
1999Q3
1999Q1
1998Q3
1998Q1
Procyclicality in investment
Invest/GDP
Equity/GDP
Credit/GDP
EFP
0.5
0.0
-0.5
-1.0
-1.5
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Procyclicality in housing markets
0.06
3.00
0.04
2.50
0.02
2.00
0
1.50
-0.02
1.00
-0.04
0.50
-0.06
0.00
20
03
Q
1
20
03
Q
3
20
04
Q
1
20
04
Q
3
20
05
Q
1
20
05
Q
3
20
06
Q
1
20
06
Q
3
20
07
Q
1
20
07
Q
3
20
08
Q
1
20
08
Q
3
-0.08
Loans to HHDs for house purchase/GDP
Residential Investment/GDP
House prices/GDP
EFP HHDs
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Procyclicality of the financial system
• Procyclical feedback mechanisms can take many forms:
• Procyclical capital requirements, collateral and margin
requirements, endogenous financial innovation,
behavioural mechanisms such as herding, information
cascades, bouts of optimism and pessimism,
measurement of risk, monetary and fiscal policies,
compensation schemes, risk taking due to moral hazard
(e.g. due to too-big-to-fail or insurance mechanisms)
• One can not model each of those mechanisms. An important
part of the research agenda is to focus on the basics.
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Procyclicality
• A lot of work is focusing on explicitly modelling the financial
sector (Gerali et al (2009), Meh and Cesaire (2009), Dib
(2009), CMR (2008), Gertler and Karadi (2009), Adrian and
Shin (2009), …) and the associated information problems.
• Empirical evidence suggests that the countercyclical
development of the price of risk has something to do with
the state of the financial sector:
• E.g. Gilchrist et al (2009): Corporate bond-based risk
premium is a very good predictor of recessions and is
related to bank lending standards.
• Need to develop both quantity (leverage) and price
indicators of this procyclicality:
• A lot of evidence on a positive relationship between low
short-term interest rates and risk-taking.
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Liquidity and margin requirements
• The pre-crisis literature (BGG, Kiyotaki-Moore) very much
focused on credit constraints or collateral requirements for
borrowers (households and firms);
• The crisis has shown the importance of liquidity (both
funding and market liquidity):
• Kiyotaki and Moore (2009) is a start at analysing liquidity
“the “resaleability” of assets”.
• However, these “resaleability” constraints are still
exogenous. Need a theory of endogenous liquidity.
• Also need a theory of maturity transformation.
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Recent criticism 2
The assumption of rational (or model-consistent)
expectations and perfect information is too strong.
– E.g. De Grauwe, 2008.
– Useful, consistent, benchmark. By bringing out expectations
explicitly, their impact can be discussed.
– Also here a lot of work is going on
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Recent criticism 2
While deep micro-foundations are difficult to handle,
practical alternative approaches are available:
– Adaptive expectations: Milani (2006), Slobodyan and
Wouters (2010), De Grauwe (2008), Reis (2007), Gaspar,
Smets and Vestin (2010), Adam, Marcet and Nicolin
(2008).
– Imperfect information: Svensson and Woodford (2005),
Collard, Dellas and Smets (2008)
– Rational inattention (Sims, 2007; Mackowiak and
Wiederholt, 2009);
Multiple equilibria: A new life?
– And the interaction with the leverage cycle
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Recent criticism 2
One important agenda for future research is to include
measures of expectations in our estimation strategies:
• Del Negro and Eusepi (2009):Tests for alternative
theories of expectation formation for inflation.
• This will also help in better identifying “news” shocks.
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Conclusions
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Conclusions
• Bayesian DSGE models provide an empirically and
theoretically coherent picture useful for monetary policy
analysis.
• Like most policy models, they are work in progress:
– The financial crisis has highlighted a number of their
shortcomings.
• Questions:
– What should be the priorities for further model
development?
– What is the optimal size of the main policy model?
– Should we abandon linearity? If so, what non-linearities
are most important?
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