Download DSGE Models

Dynamic stochastic general equilibrium
Dynamic stochastic general equilibrium modeling (abbreviated DSGE or
sometimes SDGE or DGE) is a branch of applied general equilibrium theory that
is influential in contemporary macroeconomics. The DSGE methodology
attempts to explain aggregate economic phenomena, such as economic growth,
business cycles, and the effects of monetary and fiscal policy, on the basis of
macroeconomic models derived from microeconomic principles. One of the main
reasons macroeconomists seek to build microfounded models is that, unlike more
traditional macroeconometric forecasting models, microfounded models should
not, in principle, be vulnerable to the Lucas critique. Furthermore, since the
microfoundations are based on the preferences of the decision-makers in the
model, DSGE models feature a natural benchmark for evaluating the welfare
effects of policy changes (for discussion of both points, see Woodford, 2003,
pp. 11–12 and Tovar, 2008, pp. 15–16).
Structure of DSGE models
Like other general equilibrium models, DSGE models aim to describe the
behavior of the economy as a whole by analyzing the interaction of many
microeconomic decisions. The decisions considered in most DSGE models
correspond to some of the main quantities studied in macroeconomics, such as
consumption, saving, investment, and labor supply and labor demand. The
decision-makers in the model, often called 'agents', may include households,
business firms, and possibly others, such as governments or central banks.
Furthermore, as their name indicates, DSGE models are dynamic, studying how
the economy evolves over time. They are also stochastic, taking into account the
fact that the economy is affected by random shocks such as technological change,
fluctuations in the price of oil, or changes in macroeconomic policy-making. This
contrasts with the static models studied in Walrasian general equilibrium theory,
applied general equilibrium models and some computable general equilibrium
models.
For a coherent description of the macroeconomy, DSGE models must spell out
the following economic 'ingredients'.
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Preferences: the objectives of the agents in the economy must be
specified. For example, households might be assumed to maximize a
utility function over consumption and labor effort. Firms might be
assumed to maximize profits.
Technology: the productive capacity of the agents in the economy must
be specified. For example, firms might be assumed to have a production
function, specifying the amount of goods produced, depending on the
amount of labor and capital they employ. Technological constraints on
agents' decisions might also include costs of adjusting their capital stocks,
their employment relations, or the prices of their products.
Institutional framework: the institutional constraints governing
economic interactions must be specified. In many DSGE models, this
might just mean that agents must obey some exogenously imposed budget
constraints, and that prices are assumed to adjust until markets clear. It
might also mean specifying the rules of monetary and fiscal policy, or
even how policy rules and budget constraints change depending on a
political process.
Traditional macroeconometric forecasting models used by central banks in the
1970s, and even today, estimated the dynamic correlations between prices and
quantities in different sectors of the economy, and often included thousands of
variables. Since DSGE models start from microeconomic principles of
constrained decision-making, instead of just taking as given observed
correlations, they are technically more difficult to solve and analyze. Therefore
they usually abstract from so many sectoral details, and include far fewer
variables: just a few variables in theoretical DSGE papers, or on the order of a
hundred variables in the experimental DSGE forecasting models now being
constructed by central banks. What DSGE models give up in sectoral detail, they
attempt to make up in logical consistency.
Advantages and disadvantages of DSGE modeling
By specifying preferences (what the agents want), technology (what the agents
can produce), and institutions (the way they interact), it is possible (in principle,
though challenging in practice) to solve the DSGE model to predict what is
actually produced, traded, and consumed, and how these variables evolve over
time in response to various shocks. In principle, it is also possible to make
predictions about the effects of changing the institutional framework.
In contrast, as Robert Lucas pointed out, such a prediction is unlikely to be valid
in traditional macroeconometric forecasting models, since those models are
based on observed past correlations between macroeconomic variables. These
correlations can be expected to change when new policies are introduced,
invalidating predictions based on past observations.
Given the difficulty of constructing accurate DSGE models, most central banks
still rely on traditional macroeconometric models for short-term forecasting.
However, the effects of alternative policies are increasingly studied using DSGE
methods. Since DSGE models are constructed on the basis of assumptions about
agents' preferences, it is possible to ask whether the policies considered are
Pareto optimal, or how well they satisfy some other social welfare criterion
derived from preferences (Woodford, 2003, p. 12).
Schools of DSGE modeling
At present two competing schools of thought form the bulk of DSGE modeling.[1]
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Real business cycle (RBC) theory builds on the neoclassical growth
model, under the assumption of flexible prices, to study how real shocks
to the economy might cause business cycle fluctuations. The paper of
Kydland and Prescott (1982) is often considered the starting point of RBC
theory and of DSGE modeling in general.[2] The RBC point of view is
surveyed in Cooley (1995).
New-Keynesian DSGE models build on a structure similar to RBC
models, but instead assume that prices are set by monopolistically
competitive firms, and cannot be instantaneously and costlessly adjusted.
The paper that first introduced this framework was Rotemberg and
Woodford (1997). Introductory and advanced textbook presentations are
given by Galí (2008) and Woodford (2003). Monetary policy implications
are surveyed by Clarida, Galí, and Gertler (1999).
Example
The European Central Bank (ECB) has developed a DSGE model, often called
the Smets-Wouters model, which it uses to analyze the economy of the Eurozone
as a whole (in other words, the model does not analyze individual European
countries separately). The model is intended as an alternative to the Area-Wide
Model (AWM), a more traditional empirical forecasting model which the ECB
has been using for several years. The ECB webpage that describes the SmetsWouters model also discusses the advantages of building a DSGE model instead
of relying on more traditional methods.
The equations in the Smets-Wouters model describe the choices of three types of
decision makers: households, who made an optimal trade-off between
consumption and worked hours, under a budget contraint; firms, who optimize
theirs labor and capital to employ; and the central bank, which controls monetary
policy. The parameters in the equations were estimated using Bayesian statistical
techniques so that the model approximately describes the dynamics of GDP,
consumption, investment, prices, wages, employment, and interest rates in the
Eurozone economy. In order to accurately reproduce the sluggish behavior of
some of these variables, the model incorporates several types of frictions that
slow down adjustment to shocks, including sticky prices and wages, and
adjustment costs in investment.
Controversy
Willem Buiter of the London School of Economics has argued that DSGE
models rely excessively on an assumption of complete markets, and are unable to
describe the highly nonlinear dynamics of economic fluctuations, making
training in 'state-of-the-art' macroeconomic modeling "a privately and socially
costly waste of time and resources".[3]
N. Gregory Mankiw, regarded as one of the founders of New Keynesian DSGE
modeling, has also argued that
'New classical and new Keynesian research has had little impact on
practical macroeconomists who are charged with ... policy. ... From the
standpoint of macroeconomic engineering, the work of the past several
decades looks like an unfortunate wrong turn.'[4]
Michael Woodford, replying to Mankiw, argues that DSGE models are
commonly used by central banks today, and have strongly influenced policy
makers like Ben Bernanke. However, he argues that what is learned from DSGE
models is not so different from traditional Keynesian analysis:
'It is true that the modeling efforts of many policy institutions can
reasonably be seen as an evolutionary development within the
macroeconomic modeling program of the postwar Keynesians; thus if one
expected, with the early New Classicals, that adoption of the new tools
would require building anew from the ground up, one might conclude that
the new tools have not been put to use. But in fact they have been put to
use, only not with such radical consequences as had once been
expected.'[5]
Narayana Kocherlakota, President of the Federal Reserve Bank of Minneapolis,
acknowledges that DSGE models were not very useful for analyzing the financial
crisis of 2007-2010.[6] Nonetheless, he argues that the applicability of these
models is improving, and that there is growing consensus among
macroeconomists that DSGE models need to incorporate both price stickiness
and financial market frictions.
The United States Congress hosted hearings on macroeconomic modeling
methods on July 20, 2010, to investigate why macroeconomists failed to foresee
the Financial crisis of 2007-2010. Robert Solow blasted DSGE models currently
in use:
'I do not think that the currently popular DSGE models pass the smell test.
They take it for granted that the whole economy can be thought about as if
it were a single, consistent person or dynasty carrying out a rationally
designed, long-term plan, occasionally disturbed by unexpected shocks,
but adapting to them in a rational, consistent way... The protagonists of
this idea make a claim to respectability by asserting that it is founded on
what we know about microeconomic behavior, but I think that this claim
is generally phony. The advocates no doubt believe what they say, but
they seem to have stopped sniffing or to have lost their sense of smell
altogether.'[7]
V.V. Chari pointed out, however, that state-of-the-art DSGE models are more
sophisticated than their critics suppose:
'The models have all kinds of heterogeneity in behavior and decisions...
people's objectives differ, they differ by age, by information, by the
history of their past experiences. '
Chari also argued that current DSGE models frequently incorporate frictional
unemployment, financial market imperfections, and sticky prices and wages, and
therefore imply that the macroeconomy behaves in a suboptimal way which
monetary and fiscal policy may be able to improve.[8]
Commenting on the Congressional session, The Economist asked whether agentbased models might better predict financial crises than DSGE models.[9]
References
1. ^ Cantore et al (2010) have suggested that the difference between RBC
and New Keynesian models, when controlling for key supply channels,
can be limited.
2. ^ Kydland, F.E.; Prescott, E.C. (1982), "Time to build and aggregate
fluctuations", Econometrica: Journal of the Econometric Society 50 (6):
1345–1370,
http://web.mit.edu/dancao/OldFiles/Public/Network%20Idea/Time%20to
%20Build%20and%20Aggregate%20Fluctuation.pdf, retrieved 2010-0723
3. ^ Buiter, Willem (2009-03-03). "The unfortunate uselessness of most
'state of the art’ academic monetary economics". ft.com/maverecon.
Financial Times. http://blogs.ft.com/maverecon/2009/03/the-unfortunateuselessness-of-most-state-of-the-art-academic-monetary-economics/.
Retrieved 2010-07-23.
4. ^ Mankiw, N. Gregory (2006), "The Macroeconomist as Scientist and
Engineer", The Journal of Economic Perspectives 20 (4): 29–46,
http://www.economics.harvard.edu/files/faculty/40_Macroeconomist_as_
Scientist.pdf, retrieved 2010-07-23
5. ^ Woodford, Michael (2008-01-04), "Convergence in Macroeconomics:
Elements of a New Synthesis", annual meeting of the American
Economics Association.,
http://www.columbia.edu/~mw2230/Convergence_AEJ.pdf
6. ^ Kocherlakota, Narayana (May 2010). "Modern Macroeconomic Models
as Tools for Economic Policy". Banking and Policy Issues Magazine.
Federal Reserve Bank of Minneapolis.
http://www.minneapolisfed.org/publications_papers/pub_display.cfm?id=
4428. Retrieved 2010-07-23.
7. ^ Prepared Statement of Robert Solow, Professor Emeritus, MIT, to the
House Committee on Science and Technology, Subcommittee on
Investigations and Oversight: “Building a Science of Economics for the
Real World,” July 20, 2010
8. ^ Testimony before the Committee on Science and Technology, U.S.
House of Representatives, V.V. Chari, Univ. of Minnesota and Federal
Reserve Bank of Minneapolis” July 20, 2010
9. ^ Agents of change, The Economist, July 22, 2010.
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Bank of England (2005), 'The Bank of England quarterly model':
http://www.bankofengland.co.uk/publications/other/beqm/index.htm (See
especially Chapters 1 and 3.)
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Cristiano Cantore, Miguel León-Ledesma, Peter McAdam and Alpo
Willman (2011), 'Shocking stuff: technology, hours, and factor
substitution', European Central Bank, Working Paper No. 1278:
http://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1278.pdf
European Central Bank: The Smets-Wouters (2003) model
Fabio Canova (2007), Methods for Applied Macroeconomic Research.
Princeton University Press, ISBN 0-691-11504-4.
Richard Clarida, Jordi Gali, and Mark Gertler (1999), 'The science of
monetary policy: a New-Keynesian perspective.' Journal of Economic
Literature 37, pp. 1661–707.
Thomas Cooley, ed., (1995), Frontiers of Business Cycle Research.
Princeton University Press, ISBN 069104323X.
DeJong, D. N. with C. Dave (2007), Structural Macroeconometrics.
Princeton University Press, ISBN 0691126488.
Galí, Jordi (2008), Monetary Policy, Inflation, and the Business Cycle.
Princeton University Press, ISBN 9780691133164.
Robert E. Lucas, Jr. (1976), 'Econometric policy evaluation: a critique.'
Carnegie-Rochester Conference Series on Public Policy' 1, pp. 19–46.
Julio Rotemberg and Michael Woodford (1997), 'An optimization-based
econometric framework for the evaluation of monetary policy.' NBER
Macroeconomics Annual 12, pp. 297–346.
Camilo Tovar (2008), 'DSGE models and central banks', Bank for
International Settlements working paper #258.
Michael Woodford (2003), Interest and Prices: Foundations of a Theory
of Monetary Policy. Princeton University Press, ISBN 0691010498.
Narayana Kocherlakota (2010), 'Modern Macroeconomic Models as Tools
for Economic Policy.' The Region, May 2010.
Argia Sbordone, Andrea Tambalotti, Krishna Rao, and Kieran Walsh
(2010), 'Policy analysis using DSGE models: an introduction'. Federal
Reserve Bank of New York Economic Policy Review 16 (2).