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DSGE Modelling at Central Banks: Quo Vadis?

Frank Smets

Royal Economic Society Annual Conference30 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.

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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

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• 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.

Introduction

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• 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 well-defined 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)

Introduction

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• Advantages of the DSGE approach:

– The general equilibrium structure lends itself to telling economically coherent stories and structuring forecast-related 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.

Introduction

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• 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.

Introduction

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II. Structure of Bayesian DSGE models used at the ECB

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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

Households- consumption/saving decisions- labour supply

Production- Combine labour and capital

Markets: imperfect competition & price and wage setting as a markup

Monetary Policy

Government

Core structure of both modelsSmets-Wouters (2003, 2007)

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Models Overview: NAWM

Households- consumption/saving decisions- labour supply

Production- Combine labour and capital

Markets: imperfect competition & price and wage setting as a markup

Monetary Policy

Government

Exports

Imports

Rest of the World

Exchange Rate,

Foreign Demand,

Oil price …

Final Goods

Combining domestic and imported goods

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

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Models Overview: CMR

Households- consumption/saving decisions- labour supply

Production- Combine labour and capital

Markets: imperfect competition & price and wage setting as a markup

Monetary Policy

Government

Financial IntermediationLiabilities- Deposits(components of M1, M3)

Assets- Reserves- Loans

Portfolio Decisions

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

Macroeconomic Variables

Structural Shocks

Demand

Technological Forces (e.g TFP)

Exogenous Shifts in Markups

Policy

International (in NAWM)

Financial (in CMR)

National accounts,Inflation, 3-month interest rate

International (in NAWM)[exchange rate; deflators; foreign demand; foreign prices; U.S. FFR; competitors' export prices]

Financial (in CMR) [M3; M1; credit; stock-market index; external finance premium; 10-year and 3-month interest rate spread]

Model as an approximation of

reality

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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

• Response is qualitative same across models• Considering uncertainty, quantitative differences small

0 5 10 15 20

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

Quarters

Simple Model

NAWM

CMR

0 5 10 15 20

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

Quarters

Simple Model

NAWM

CMR

0 5 10 15 20-20

0

20

40

60

Quarters

Simple Model

NAWM

CMR

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Monetary Policy Transmission: NAWM

0 5 10 15 20-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

Quarters

OutputConsumptionInvestment

0 5 10 15 20-0.4

-0.3

-0.2

-0.1

0

0.1

Quarters

ExportImports

0 5 10 15 20-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

Quarters

real Ex. Rate

0 5 10 15 20-0.25

-0.2

-0.15

-0.1

-0.05

0

Quarters

Domestic InflationConsumption Inflation

0 5 10 15 20-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

Quarters

Rental Rate CapitalWagesMarginal Costs

Additional channels in open economy:

•Appreciation of currency•Drop in exports amplifies output reaction

•Reduction in import prices implies stronger inflation response

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Monetary Policy Transmission: NAWM

Quarters 0 5 10 15 20

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

Quarters

0 5 10 15 20

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

Quarters 0 5 10 15 20

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

NAWM

Constant Nominal Ex. Rate

Output Inflation Real Effective Exchange Rate

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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

Price Developments Originally Unforeseen Can Affect Financial Position and Real Economy

2 4 6 8 10 12 14 16 18 20

5

10

15

20

25

30

basi

s po

ints

Marginal

Average

2 4 6 8 10 12 14 16 18 20

0.05

0.1

0.15

0.2

0.25

0.3

Pe

rce

nt

Bankruptcy Rate

Output

5 10 15 20-0.45

-0.35

-0.25

-0.15

-0.05P

erc

en

t

CMR baseline

CMR with indexed financial contracts

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Historical analysis

Model as a lens to interpret

reality

Macroeconomic Variables

Structural Shocks

• 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 areaNAWM

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GDP growth in the euro areaCMR

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• 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

Examples of scenario analysis

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4. The financial crisis and recent criticism

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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.

Recent criticism 1

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• 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)

Procyclicality of the financial system

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Procyclicality in investment

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.519

98Q

1

1998

Q3

1999

Q1

1999

Q3

2000

Q1

2000

Q3

2001

Q1

2001

Q3

2002

Q1

2002

Q3

2003

Q1

2003

Q3

2004

Q1

2004

Q3

2005

Q1

2005

Q3

2006

Q1

2006

Q3

2007

Q1

2007

Q3

2008

Q1

2008

Q3

Invest/GDP

Equity/GDP

Credit/GDP

EFP

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Procyclicality in housing markets

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

2003Q

1

2003Q

3

2004Q

1

2004Q

3

2005Q

1

2005Q

3

2006Q

1

2006Q

3

2007Q

1

2007Q

3

2008Q

1

2008Q

3

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Loans to HHDs for house purchase/GDP Residential Investment/GDP

House prices/GDP EFP HHDs

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• 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.

Procyclicality of the financial system

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• 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.

Procyclicality

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• 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.

Liquidity and margin requirements

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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

Recent criticism 2

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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

Recent criticism 2

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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.

Recent criticism 2

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Conclusions

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• 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?

Conclusions