Stephanie SchmittGrohé and Martín Uribe are
Professors of Economics at Duke University. Their main
line of interest lies in monetary macroeconomics, in
particular issues of optimal stabilisation policy.
SchmittGrohé's RePEc/IDEAS
entry. Uribe's RePEc/IDEAS
entry.
Much of our recent research has been devoted to
developing and applying tools for the evaluation of
macroeconomic stabilization policy. This choice of topic
was motivated by an important development in
businesscycle theory. By the late 1990s, a frictionless
model of the macroeconomy was viewed by many as no
longer providing a satisfactory account of aggregate
fluctuations. As a response, the new Keynesian paradigm
emerged as an alternative framework for understanding
business cycles. A key difference between the
neoclassical and the new Keynesian paradigms is that in
the latter, the presence of various nominal and real
distortions provide a meaningful role for stabilization
policy, opening the door once again, after decades of
dormancy, for policy evaluation.
Developing Tools For Policy Evaluation
An obstacle we encountered early on in executing the
research agenda described here was the lack of
appropriate tools to evaluate stabilization policies in
the context of distorted economies. An important part of
our effort was therefore devoted to developing such
tools.
Most models used in modern macroeconomics are too
complex to allow for exact solutions. For this reason,
researchers have appealed to numerical approximation
techniques. One popular and widely used approximation
technique is a firstorder perturbation method
delivering a linear approximation to the policy
function. One reason for the popularity of firstorder
perturbation techniques is that they do not suffer from
the `curse of dimensionality.' That is, problems with a
large number of state variables can be handled without
much computational demands. Because models that are
successful in accounting for many aspects of observed
business cycles are bound to be large (e.g., Smets and
Wouters, 2004; and Christiano, Eichenbaum, and Evans,
2003), this advantage of perturbation techniques is of
particular importance for policy evaluation. However, an
important limitation of firstorder approximation
techniques is that the solution displays the certainty
equivalence property. In particular, the firstorder
approximation to the unconditional means of endogenous
variables coincides with their nonstochastic steady
state values. This limitation restricts the range of
questions that can be addressed in a meaningful way
using firstorder perturbation techniques. One such
question that is of particular relevance for our
research agenda is welfare evaluation in stochastic
environments featuring distortions or market failures.
For example, Kim and Kim (2003) show that in a simple
twoagent economy, a welfare comparison based on an
evaluation of the utility function using a linear
approximation to the policy function may yield the
spurious result that welfare is higher under autarky
than under full risk sharing. The problem here is that
some second and higherorder terms of the equilibrium
welfare function are omitted while others are included.
Consequently, the resulting criterion is inaccurate to
order two or higher. The same problem arises under the
common practice in macroeconomics of evaluating a
secondorder approximation to the objective function
using a firstorder approximation to the decision rules.
For in this case, too, some secondorder terms of the
equilibrium welfare function are ignored while others
are not. See Woodford (2003, chapter 6) for a discussion
of conditions under which it is correct up to second
order to approximate the level of welfare using
firstorder approximations to the policy function. In
general, a correct secondorder approximation of the
equilibrium welfare function requires a secondorder
approximation to the policy function.
This is what we set out to accomplish in
SchmittGrohé and Uribe (2004a). Building on previous
work by Collard and Juillard, Sims, and Judd among
others, we derive a secondorder approximation to the
solution of a general class of discretetime rational
expectations models. Specifically, our technique is
applicable to nonlinear models whose equilibrium
conditions can be written as: E_{t}
f(y_{t+1},y_{t},x_{t+1},x_{t})=0,
where the vector x_{t} is predetermined
and the vector y_{t} is nonpredetermined.
The main theoretical contribution of SchmittGrohé
and Uribe (2004a) is to show that for any model
belonging to this general class, the coefficients on the
terms linear and quadratic in the state vector in a
secondorder expansion of the decision rule are
independent of the volatility of the exogenous shocks.
In other words, these coefficients must be the same in
the stochastic and the deterministic versions of the
model. Thus, up to second order, the presence of
uncertainty affects only the constant term of the
decision rules. But the fact that only the constant term
is affected by the presence of uncertainty is by no
means inconsequential. For it implies that up to second
order the unconditional mean of endogenous variables can
in general be significantly different from their
nonstochastic steady state values. Thus, secondorder
approximation methods can in principle capture important
effects of uncertainty on average rate of return
differentials across assets with different risk
characteristics and on the average level of consumer
welfare. An additional advantage of higherorder
perturbation methods is that like their firstorder
counterparts, they do not suffer from the curse of
dimensionality. This is because given the firstorder
approximation to the policy function, finding the
coefficients of a secondorder approximation simply
entails solving a system of linear equations.
The main practical contribution of SchmittGrohé and
Uribe (2004a) is the development of a set of MATLAB
programs that compute the coefficients of the
secondorder approximation to the solution to the
general class of models described above. This computer
code is publicly available at the authors' websites. Our
computer code coexists with others that have been
developed recently by Chris Sims and Fabrice Collard and
Michel Juillard to accomplish the same task. We believe
that the availability of this set of independently
developed codes, which have been shown to deliver
identical results for a number of example economies,
helps build confidence across potential users of
higherorder perturbation techniques.
Optimal Operational Monetary Policy for the U.S.
Economy
After the completion of the secondorder
approximation toolkit, we felt that we were suitably
equipped to undertake a systematic and rigorous
evaluation of stabilization policy. A contemporaneous
development that highly facilitated our work was the
emergence of estimated mediumscale dynamic general
equilibrium models of the U.S. economy with the ability
to explain the behavior of a relatively large number of
macroeconomic variables at businesscycle frequency
(e.g., Christiano, Eichenbaum, and Evans, 2003; and
Smets and Wouters, 2004).
A central characteristic of the studies on optimal
monetary policy that existed at the time we initiated
our research on policy evaluation, was that they were
conducted in the context of highly stylized
environments. An important drawback of that approach is
that highly simplified models are unlikely to provide a
satisfactory account of cyclical movements for but a few
macroeconomic variables of interest. For this reason,
the usefulness of this strategy to produce policy advise
for the real world is necessarily limited.
In a recent working paper (SchmittGrohé and Uribe,
2004b), we depart from the literature extant in that we
conduct policy evaluation within the context of a rich
theoretical framework capable of explaining observed
business cycle fluctuations for a wide range of nominal
and real variables. Following the lead of Kimball
(1995), the model emphasizes the importance of combining
nominal and real rigidities in explaining the
propagation of macroeconomic shocks. Specifically, the
model features four nominal frictions, sticky prices,
sticky wages, money in the utility function, and a
cashinadvance constraint on the wage bill of firms,
and four sources of real rigidities, investment
adjustment costs, variable capacity utilization, habit
formation, and imperfect competition in product and
factor markets. Aggregate fluctuations are assumed to be
driven by supply shocks, which take the form of
stochastic variations in total factor productivity, and
demand shocks stemming from exogenous innovations to the
level of government purchases. Altig et al. (2003) and
Christiano, Eichenbaum, and Evans (2003) argue that the
model economy for which we seek to design optimal
operational monetary policy can indeed explain the
observed responses of inflation, real wages, nominal
interest rates, money growth, output, investment,
consumption, labor productivity, and real profits to
productivity and monetary shocks in the postwar United
States. In this respect, SchmittGrohé and Uribe (2004b)
aspires to be a step ahead in the research program of
generating monetary policy evaluation that is of
relevance for the actual practice of central banking.
In our quest for the optimal monetary policy scheme
we restrict attention to what we call operational
interest rate rules. By an operational interestrate
rule we mean an interestrate rule that satisfies three
requirements. First, it prescribes that the nominal
interest rate is set as a function of a few readily
observable macroeconomic variables. In the tradition of
Taylor (1993), we focus on rules whereby the nominal
interest rate depends on measures of inflation,
aggregate activity, and possibly its own lag. Second,
the operational rule must induce an equilibrium
satisfying the zero lower bound on nominal interest
rates. And third, operational rules must render the
rational expectations equilibrium unique. This last
restriction closes the door to expectations driven
aggregate fluctuations.
The object that monetary policy aims to maximize in
our study is the expectation of lifetime utility of the
representative household conditional on a particular
initial state of the economy. Our focus on a conditional
welfare measure represents a fundamental departure from
most existing normative evaluations of monetary policy,
which rank policies based upon unconditional
expectations of utility. Exceptions are Kollmann (2003)
and SchmittGrohé and Uribe (2004c). As Kim et al.
(2003) point out, unconditional welfare measures ignore
the welfare effects of transitioning from a particular
initial state to the stochastic steady state induced by
the policy under consideration. Indeed, we document that
under plausible initial conditions, conditional welfare
measures can result in different rankings of policies
than the more commonly used unconditional measure. This
finding highlights the fact that transitional dynamics
matter for policy evaluation. In our welfare
evaluations, we depart from the widespread practice in
the neoKeynesian literature on optimal monetary policy
of limiting attention to models in which the
nonstochastic steady state is undistorted. Most often,
this approach involves assuming the existence of a
battery of subsidies to production and employment aimed
at eliminating the longrun distortions originating from
monopolistic competition in factor and product markets.
The efficiency of the deterministic steadystate
allocation is assumed for purely computational reasons.
For it allows the use of firstorder approximation
techniques to evaluate welfare accurately up to second
order, a simplification that was pioneered by Rotemberg
and Woodford (1999). This practice has two potential
shortcomings. First, the instruments necessary to bring
about an undistorted steady state (e.g., labor and
output subsidies financed by lumpsum taxation) are
empirically uncompelling. Second, it is ex ante not
clear whether a policy that is optimal for an economy
with an efficient steady state will also be so for an
economy where the instruments necessary to engineer the
nondistorted steady state are unavailable. For these
reasons, we refrain from making the
efficientsteadystate assumption and instead work with
a model whose steady state is distorted.
Departing from a model whose steady state is Pareto
efficient has a number of important ramifications. One
is that to obtain a secondorder accurate measure of
welfare it no longer suffices to approximate the
equilibrium of the model up to first order. Instead, we
obtain a secondorder accurate approximation to welfare
by solving the equilibrium of the model up to second
order. Specifically, we use the methodology and computer
code developed in SchmittGrohé and Uribe (2004a).
Our numerical work suggests that in the model economy
we study, the optimal operational interestrate rule
takes the form of a realinterestrate targeting rule.
For it features an inflation coefficient close to unity,
a mute response to output, no interestrate smoothing,
and is forward looking. The optimal rule satisfies the
Taylor principle because the inflation coefficient is
greater than unity albeit very close to 1. Optimal
operational monetary policy calls for significant
inflation volatility. This result stands in contrast to
those obtained in the related literature. The main
element of the model driving the desirability of
inflation volatility is indexation of nominal factor and
product prices to 1period lagged inflation. Under the
alternative assumption of indexation to longrun
inflation, the conventional result of the optimality of
inflation stability reemerges.
Open Questions
There remain many challenging unanswered questions in
this research program. One is to investigate the
sensitivity of the parameters of the optimal operational
policy rule to changes in the sources of uncertainty
driving business cycles. This question is of importance
in light of the ongoing quest in businesscycle research
to identify the salient sources of aggregate
fluctuations. One alternative would be to incorporate
the rich set of shocks identified in econometric
estimations of the model considered here (e.g., Smets
and Wouters, 2004).
The class of operational rules discussed here is
clearly not exhaustive. It would be of interest to
investigate whether the inclusion of macroeconomic
indicators other than those considered here would
improve the policymaker's ability to stabilize the
economy. In particular, the related literature has
emphasized the use of measures of the output gap that
are different from that used by us. Additionally, it has
been argued that in models with nominal wage and price
rigidities the optimal policy should target an average
of wage and price inflation as opposed to only price
inflation, which is the case we analyze.
The optimal policy problem we analyze takes the
central bank's inflation target as exogenously given. A
natural extension is to endogenize this variable.
However, in our theoretical framework, the optimal
inflation target is the one associated with the Friedman
rule. This is because the assumption of full indexation
to past inflation implies the absence of inefficient
price and wage dispersion in the long run. Thus the only
remaining nominal frictions are the demand for money by
households and firms. These frictions call for driving
the opportunity cost of holding money to zero in the
long run. In other words, the zero bound on nominal
interest rate binds in the nonstochastic steady state.
The perturbation technique we employ is ill suited to
handle this case. Therefore, analyzing the case of an
endogenous inflation target entails either changing the
model so that the Friedman rule is no longer optimal in
the longrun or adopting alternative numerical
techniques for computing welfare accurately up to
secondorder or higher.
One of our findings is that the initial state of the
economy plays a role in determining the parameters
defining the optimal interestrate rule. This finding
suggests that the optimal operational rule identified
here is time inconsistent. In SchmittGrohé and Uribe
(2004b), we assume that the government is able to commit
to the policy announcements made at time 0. It would be
of interest to characterize optimal operational rules in
an environment without commitment.
Finally, we limit attention to the special case of
passive fiscal policy, taking the form of a
balancedbudget rule with lumpsum taxation. It is well
known that the set of operational monetary rules depends
on the stance of fiscal policy. For instance, the
determinacy properties of the rational expectations
equilibrium associated with a particular monetary rule
can change as fiscal policy is altered. Therefore, it
would be of interest to introduce operational fiscal
rules as an additional policy instrument.
References
Altig, David, Lawrence J. Christiano, Martin
Eichenbaum, and Jesper Lindé (2003): Technology Shocks
and Aggregate Fluctuations manuscript, Northwestern
University.
Christiano, Lawrence J., Martin Eichenbaum, and
Charles Evans (2003): Nominal
Rigidities and the Dynamic Effects of a Shock to
Monetary Policy. Northwestern University.
Kim, Jinill, and Sunghyun Henry Kim (2003): Spurious
Welfare Reversals in International Business Cycle
Models, Journal of International Economics
vol. 60, pages 471500.
Kim, Jinill, Sunghyun Henry Kim, Ernst Schaumburg,
and Christopher Sims (2003): Calculating
and Using Second Order Accurate Solutions of Discrete
Time Dynamic Equilibrium Models, Finance and
Economics Discussion Series 200361, Board of Governors
of the Federal Reserve System.
Kimball, Miles S. (1995): The
Quantitative Analytics of the Basic Neomonetarist
Model, Journal of Money, Credit and Banking,
vol. 27, pages 12411277.
Kollmann, Robert (2003): Welfare Maximizing Fiscal
and Monetary Policy Rules mimeo, University of Bonn.
Rotemberg, Julio J., and Michael Woodford (1999): Interest
Rate Rules in an Estimated Sticky Price Model, in:
John B. Taylor, ed., Monetary policy rules NBER
Conference Report series. Chicago and London: University
of Chicago Press, pages 57119.
SchmittGrohé, Stephanie and Martín Uribe (2004a): Solving
Dynamic General Equilibrium Models Using a SecondOrder
Approximation to the Policy Function, Journal of
Economic Dynamics and Control, vol. 28, pages
755775.
SchmittGrohé, Stephanie, and Martín Uribe (2004b):
Optimal
Operational Monetary Policy in the
ChristianoEichenbaumEvans Model of the U.S. Business
Cycle, NBER working paper 10724.
SchmittGrohé, Stephanie and Martín Uribe (2004c): Optimal
Simple And Implementable Monetary and Fiscal Rules,
NBER working paper 10253.
Smets, Frank and Raf Wouters (2004): Comparing
shocks and frictions in US and Euro area business
cycles: a Bayesian DSGE approach, Working paper 61,
Nationale Bank van Belgie.
Taylor, John B. (1993): Discretion versus Policy
Rules in Practice Carnegie Rochester Conference
Series on Public Policy, vol. 39, pages 195214.
Thomas Holmes is Professor at the Department
of Economics of the University of Minnesota. He has
recently been working on the spatial distribution of
economic activity as well as basic issues in the
organization of production. Holmes' RePEc/IDEAS
entry.
 EconomicDynamics: In your RED article, you
demonstrate that if an industry is situated at an
inefficient location through accidents of history, it
will eventually migrate to efficient locations. Does
this result apply to globalization, which should thus
be regarded as inevitable? And why is the modeling of
dynamics so important in this regard?

Thomas Holmes: I have to confess that my result
in the RED article, "StepbyStep Migrations" wouldn't
usually apply to globalization. It is more about the
migration of industry within a country or even a
region within a country. But I am glad you asked the
question, because it is a great one for clarifying
exactly what my result does cover. And it gives me a
chance to plug some related work!
A large literature, e.g. Paul Krugman, Brian
Arthur, and others, has emphasized that when
agglomeration economies are important, industries can
get "stuck" in an inefficient location. No individual
firm is unilaterally willing to leave for the better
location because of the "glue" of agglomeration
benefits. In other words, there is a coordination
failure. My model differs from the previous literature
in that rather than being forced to take a big
discrete "jump" to go to some new location and forego
all agglomeration benefits, a firm can take a small
"step" in the direction of the new location. With a
small step, the firm retains some of the agglomeration
benefits of the old location, but also begins to enjoy
some of the advantages of the new location. I show
that industries never get stuck at locations that are
inefficient from the perspective of local optimality,
and I present a condition under which migration rates
are efficient. As an application of the theory, I
discuss the migration of the automobile industry in
the U.S. from Michigan to the South. It is clear that
this industry has moved in a stepbystep fashion.
So we see that this specific model doesn't apply to
globalization. If the textile industry is in North
Carolina, and the efficient location is Africa, by
taking a small step in this direction, firms would
have to set up shop in the Atlantic Ocean!
Nevertheless, the broad idea of the project that
migrations can take place in a stepbystep fashion
does apply to the issue of globalization. In a 1999
Journal of Urban Economics article, I observe that an
industry like textiles has many different kinds of
products. Agglomeration benefits are not so important
for the production of coarse cloth, but they are
important for advanced textiles. Lowend products tend
to migrate first, but these then set up a base of
agglomeration benefits that may draw in mediumlevel
products, which in turn may drawn in the next level
products, and so on.
The larger point of this set of papers is that the
attractive force of production efficiency can be
powerful even when agglomeration forces are important.
There will usually be somebody who will be drawn in by
production advantages of a better location and the
first migration makes the second one easier. The
modeling of dynamics in this analysis is crucial,
since stepbystep migrations are inherently a dynamic
phenomenon.
 ED: Modern Macroeconomics is based on strong
microfoundations, yet one of its essential components,
the production function, is still a black box. Your
recent work with Matt Mitchell is looking at the
interaction of skilled work, unskilled work and
capital. What should a macroeconomist used to a
CobbDouglas representative production function retain
from this work?

TH: One goal of this project is precisely to
get into the black box of the production function.
Important recent work, such as Krusell, Ohanian,
RiosRull, Violante, utilitizes capitalskill
complementarity properties of the production function
to explain phenomena such as changes in the skill
premium. But there is little in the way of
microfoundations of the production function that
delivers capitalskill complementarity.
My recent work with Matt Mitchell develops such a
micro model of production. The central idea of our
model is that unskilled labor relates to capital in
the same way that skilled labor relates to unskilled.
Unskilled labor has general ability in the performance
of mechanical tasks. An unskilled worker can easily
switch from the job of tightening a bolt, picking up a
paint brush, or emptying a trash. It may be possible
to substitute a machine to undertake any one of these
tasks, but this would generally require upfront
investment, a fixed cost, to design a machine specific
to this task. In an analogous way, skilled labor has
general ability in the performance of mental tasks. A
worker with a degree in engineering can be put in
charge of a production line and has general knowledge
to make appropriate decisions when unexpected problems
arise. Alternatively, a production process may be
redesigned and routinized to reduce the amount of
uncertainty, making it possible for an unskilled
worker to run the production line, instead. In this
analysis, the scale of production is crucial for
determining how to allocate tasks. For smallscale
production, e.g. for a prototype model, unskilled
labor will do the mechanical tasks and skilled labor
will manage the production line. For largescale
production, capital will do the mechanical tasks and
unskilled labor will manage the production line.
We use our model to (1) provide micro foundations
for capitalskill complementarity, (2) provide a
theory for how factor composition (e.g. capital
intensity and skilled worker share) varies with plant
size and (3) provide a theory of how expansion of
markets through increased trade affects the skill
premium. Our theory is consistent with certain facts
about factor allocation and factor price changes in
the 19th and 20th centuries.
Since you brought up Cobb Douglas, a good question
is whether or not our theory can provide micro
foundations of CobbDouglas, analogous to Houthakker.
The answer is no. Not only is our aggregate production
function not CobbDouglas, it isn't even homothetic.
In fact, one of the key points of our paper is that a
proportionate increase of all factors of
productionwhich is what happens when two similar
countries begin to tradecan change relative factor
prices.
I know it's an uphill battle to wean
macroeconomists off the CobbDouglas production
function. Macroeconomists love it not just because of
its tractability but also because of the constancy of
capital share. But if macroeconomists want to
understand phenomena like changes in the skill
premium, I believe they have to get out of a
CobbDouglas world. At this point, my model with Matt
is too primitive to make it suitable for a
quantitative macro analysis. But I believe that there
is a potential for a next generation of models in this
class to be useful for quantitative analysis.
References:
Holmes, Thomas J. (1999): How Industries Migrate
When Agglomeration Economies Are Important, Journal
of Urban Economics, vol. 45, pages 240263.
Holmes, Thomas J. (2004): Stepbystep
Migrations, Review of Economic Dynamics, vol.
7, pages 5268.
Holmes, Thomas J. and Matthew F. Mitchell (2003): A
Theory of Factor Allocation and Plant Size, Federal
Reserve Bank of Minneapolis Staff Report 325.
H. S. Houthakker (1955): The Pareto Distribution and
the CobbDouglas Production Function in Activity
Analysis, The Review of Economic Studies, vol.
23, pages 2731.
Krusell, Per, Lee Ohanian, JoséVíctor RíosRull, and
Giovanni Violante (2000): CapitalSkill
Complementarity and Inequality: A Macroeconomic
Analysis, Econometrica, vol. 68, pages
102953.
Society for Economic Dynamics:
Letter from the President
Dear SED Members and Friends:
The 2004 meetings of the SED, held in Florence,
Italy, were a great success. Ten and even eleven
parallel sessions ran at a time for three days, with 370
papers presented. For the high quality of the content we
should thank the program organizers Jeremy Greenwood and
Gianluca Violante.
The 2005 meetings will be held in Budapest, June
2325. The call for papers is at http://www.economicdynamics.org/currentSED.htm
and some of the planned events are already listed there.
The program cochairs are Rob Shimer and Marco Bassetto,
and the plenary speakers who this year will be Stephen
Morris, Jonathan Eaton, and Richard Rogerson. The
submission deadline is January 31.
Gary Hansen continues his dedicated and able
management of RED. We are happy to report that RED is
included in the 2003 edition of the Journal Citation
Reports published by ISI. Although ISI has been indexing
RED for several years now, this is the first year that
statistics about RED have appeared in print.
I urge you to submit your best work to RED, and I
hope to see all of you this June in Budapest.
Sincerely,
Boyan
Jovanovic, President Society for Economic Dynamics
Finn Kydland and Edward Prescott
win Nobel Prize
Finn
Kydland and Edward
Prescott are the 2004 recepient of the 2004 Nobel
Prize in Economics, as all readers of this Newsletter
certainly are aware of. They have been instrumental in
the development of the Society, be it as members, as
President (Prescott from 1992 to 1995), through
involvement in the Review of Economics Dynamics or in
the Society meetings, and by being advisors to many
members of the Society for Economic Dynamics. The
maximum number of degrees of separation between Finn or
Ed and any member of the SED must be very low!
More generally, their work has been instrumental in
reshaping macroeconomics into a modern, microfounded,
datadriven field. It has immensely expanded what we can
now do with theory, as it trigered a large development
of new tools that help answer more and more important
questions. Their impact has not just been academic, but
has had concrete implications in the conduct of policy,
in particular for central banks.
The Nobel Foundation has posted an interesting review
of their work. Be sure to read
it!
Society for Economic Dynamics:
Call for Papers, 2005 Meetings
The 2005 meetings of the Society for Economic
Dynamics will be held 2325 June 2005 in Budapest
(Hungary). The plenary speakers are Stephen Morris,
Jonathan Eaton and Richard Rogerson. The program
committee is composed of Robert Shimer and Marco
Basetto, and Max Gillman and Akos Valentinyi are the
local organizers.
The program will be made up from a selection of
invited and submitted papers. The Society welcomes
submissions for the program. Submissions may be from any
area in Economics. The program committee will select the
papers for the conference. As well as considering
individual papers for the conference, the program
committee will also entertain suggestions for fourpaper
sessions. The deadline for submissions is 31 January
2005. Details are available at http://www.EconomicDynamics.org/currentSED.htm.
Review: Bagliano and Bertola's
Models for Dynamic Macroeconomics
Models for Dynamic Macroeconomics
by Fabio Cesare
Bagliano and Giuseppe
Bertola
Yet another textbook on dynamic macroeconomics, one
may think. This book is different from the others in
terms of the target audience. It aims at advanced
undergraduates, say in an Honors program or in programs
that can go more in depth in Economics. It is also
suited for beginning graduates, say those in terminal
Master's programs for which the standard graduate
textbook may be overwhelming. It covers material that is
treated in an undergraduate textbook in a more technical
fashion, but without drawing on tools that go beyond
undergraduate mathematics. This allows to concentrate on
a rigorous treatment of Economics without burdening
students with many new mathematical concepts.
The topics are standard for any good treatment of
dynamic macroeconomics: permanent income and
consumption, precautionary savings, CCAPM, optimal
investment with adjustment costs, dynamics of the labor
market, dynamic general equilibrium, endogenous growth
as well as search models. It also includes exercices
with answers.
While is does not cover some popular topics, like
overlaping generations and business cycle models, it
provides the right amount of technical analysis and
economic intuition that is required of the target
audience. This book is an excellent addition to the
toolbox used in teaching dynamic macroeconomics.
Models for Dynamic Macroeconomics is published by
Oxford University Press.
Impressum
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