
Volume 8, Issue 1, November 2006
The Research Agenda: Jesús FernándezVillaverde and Juan F. RubioRamírez on Estimating DSGE Models
Jesús FernándezVillaverde and Juan F. RubioRamírez are both Associate Professors of Economics at Duke University. They have written several papers about how to take dynamic general equilibrium models to the data.
FernándezVillaverde's
RePEc/IDEAS entry and RubioRamírez's RePEc/IDEAS entry.
Our research agenda has focused on the estimation of dynamic stochastic
general equilibrium (DSGE) models. In particular, we have worked on the
likelihoodbased approach to inference.
DSGE models are the standard tool of quantitative macroeconomics. We use
them to organize our thinking, to measure the importance of different
phenomena, and to provide policy prescriptions. However, since Kydland and
Prescott's immensely influential 1982 paper, the profession has fought about
how to take these models to the data. Three issues are at stake: first, how
to determine the values of the parameters that describe preferences and
technology (the unfortunately named "structural" parameters); second, how
to measure the fit of the model; and third, how to decide which of the
existing theories better accounts for the observed data.
Kydland and Prescott proposed to "calibrate" their model, i.e., to select
parameter values by matching some moments of the data and by borrowing from
microeconomic evidence. Calibration was a reasonable choice at the time.
Macroeconomists were unsure about how to compute their models efficiently, a
necessary condition to perform likelihoodbased inference. Moreover, even if
economists had known how to do so, most of the techniques required for
estimating DSGE models using a likelihood approach did not exist. Finally,
as recalled by Sargent (2005), the early results on estimation brought much
disappointment. The models were being blown out of the water by likelihood
ratio tests despite the feeling that those models could teach practitioners
important lessons. Calibration offered a way out. By focusing only on a very
limited set of moments of the model, researchers could claim success and
keep developing the theory.
The landscape changed dramatically in the 1990s. There were developments
along three fronts. First, macroeconomists learned how to efficiently
compute equilibrium models with rich dynamics. There is not much point in
estimating very stylized models that do not even have a remote chance of
fitting the data well. Second, statisticians developed simulation techniques
like Markov chain Monte Carlo (MCMC), which we require to estimate DSGE
models. Third, and perhaps most important, computer power has become so
cheap and readily available that we can now do things that were unthinkable
20 years ago.
One of the things we can now do is to estimate nonlinear and/or nonnormal
DSGE models using a likelihood approach. This statement begets two
questions: 1) Why do we want to estimate those DSGE models? and 2) How do we
do it?
Why Do We Want to Estimate Nonlinear and/or Nonnormal DSGE Models?
Let us begin with some background. There are many reasons why the likelihood
estimation of DSGE models is an important topic. First of all, a rational
expectations equilibrium is a likelihood function. Therefore, if you trust
your model, you have to trust its likelihood. Second, the likelihood
approach provides a coherent and systematic procedure to estimate all the
parameters of interest. The calibration approach may have made sense back in
the 1980s when we had only a small bundle of parameters to select values
for. However, current models are richly parameterized. Neither a loose
application of the method of moments (which is what moment matching in
calibration amounts to) nor some disparate collection of microeconomic
estimates will provide us with the discipline to quantify the behavior of
the model. Parameters do not have a life of their own: their estimated
values are always conditional on one particular model. Hence, we cannot
import these estimated values from one model to another. Finally, the
likelihood yields excellent asymptotic properties and sound small sample
behavior.
However, likelihoodbased estimation suffers from a fundamental problem: the
need to evaluate the likelihood function of the DSGE model. Except in a few
cases, there is no analytical or numerical procedure to write down the
likelihood.
The standard solution in the literature has been to find the linear
approximation to the policy functions of the model. If, in addition, we
assume that the shocks to the economy are normally distributed, we can apply
the Kalman filter and evaluate the likelihood implied by the approximated
policy functions. This strategy depends on the accuracy of the approximation
of the exact policy functions by a linear relation and on the presence of
normal shocks. Each of those two assumptions is problematic.
Linear Policy Functions
When we talk about linearization, the first temptation is to sweep it under
the rug as a small numerical error. However, the impact of linearization is
grimmer than it looks. We explore this assertion in our paper "Convergence
Properties of the Likelihood of Computed Dynamic Models", published in
Econometrica and coauthored with Manuel Santos. In that paper, we
prove that second order approximation errors in the policy function, like
those generated by linearization, have first order effects on the likelihood
function. Moreover, we demonstrate that the error in the approximated
likelihood is compounded with the size of the sample. Period by period,
small errors in the policy function accumulate at the same rate at which the
sample size grows. Thus, the approximated likelihood diverges from the exact
one as we get more and more observations.
We have documented how those theoretical insights are quantitatively
relevant for reallife applications. The main piece of evidence is in our
paper "Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear
Likelihood", published in the Journal of Applied Econometrics. The
paper compares the results of estimating the linearized version of a DSGE
model with the results from estimating the nonlinear version. In the first
case, we evaluate the likelihood of the model with the Kalman filter. In the
second case, we evaluate the likelihood with the particle filter (which we
will discuss below). Our findings highlight how linearization has a
nontrivial impact on inference. First, both for simulated and for U.S.
data, the nonlinear version of the model fits the data substantially
better. This is true even for a nearly linear case. Second, the differences
in terms of point estimates, although relatively small in absolute values,
have substantive effects on the behavior of the model.
Other researchers have found similar results when they take DSGE models to
the data. We particularly like the work of Amisano and Tristiani (2005) and
An (2005). Both papers investigate New Keynesian models. They find that the
nonlinear estimation allows them to identify more structural parameters, to
fit the data better, and to obtain more accurate estimates of the welfare
effects of monetary policies.
Normal Shocks
The second requirement for applying the Kalman filter to estimate DSGE
models is the assumption that the shocks driving the economy are normally
distributed. Since nearly all DSGE models make this assumption, this
requirement may not look dangerous. This impression is wrong: normality is
extremely restrictive.
Researchers put normal shocks in their models out of convenience, not for
any substantive reason. In fact, fat tails are such a pervasive feature of
the data that normality is implausible. More thoughtful treatments of the
shocks deliver huge benefits. For example, the fit of an ARMA process to
U.S. output data improves dramatically when the innovations are distributed
as studentt's (a density with fat tails) instead of normal ones (Geweke,
1993 and 1994).
A simple way to generate fat tails, and one that captures the evidence of
volatility clustering in the data, is to have timevarying volatility in the
shocks. Why macroeconomists have not focused more effort on the topic is a
puzzle. After all, Engle (1982), in the first work on timevarying
volatility, picked as his application of the ARCH model the process for
United Kingdom inflation. However, that route was not followed. Even today,
and beyond our own work on the issue, only Justiniano and Primiceri (2006)
take seriously the idea that shocks in a DSGE model may have a richer
structure than normal innovations.
Timevarying volatility of the shocks is not only a device to achieve a
better fit, it is key to understanding economic facts. Think about the
"Great Moderation." Kim and Nelson (1999), McConnell and PérezQuirós (2000), and Stock and Watson (2002) have documented a decline in the
variance of output growth since the mid 1980s. Moreover, there is a
narrowing gap between growth rates during booms and recessions. What has
caused the change in observed aggregate volatility? Was it due to better
conducting of monetary policy by the Fed? Or was it because we did not
suffer large shocks like the oil crises of the 1970s? We can answer that
question only if we estimate structural models where we let both the
monetary policy rule and the volatility of the shocks evolve over time. We
will elaborate below on how to explore policy change as a particular case of
parameter drifting.
There are two possibilities to introduce timevarying variance in shocks.
One is stochastic volatility. The other one is Markov regimeswitching
models. We have worked more on the first approach since it is easier to
handle. However, as we will explain below, we are currently exploring the
second one.
A common feature of both stochastic volatility and regimeswitching models
is that they induce fundamental nonlinearities and fat tails.
Linearization, by construction, precludes any possibility of assessing
timevarying volatility. If we linearize the laws of motion for the shocks,
as someone who wanted to rely on the Kalman filter would be forced to do,
the volatility terms would drop. Justiniano and Primiceri (2006) have got
around that problem by pioneering the use of partially linear models in a
specially clever way. Unfortunately, there is only so much we can do even
with partially linear models. We need a general procedure to tackle
nonlinear and/or nonnormal problems.
How Do We Do It?
Our previous arguments point out the need to evaluate the likelihood
function of the nonlinear and/or nonnormal solution of DSGE models. But,
how can we do that? This is where our paper, "Estimating Macroeconomic
Models: A Likelihood Approach," comes in. This paper shows how a simulation
technique known as the particle filter allows us to evaluate that likelihood
function. Once we have the likelihood, we can estimate the parameters of the
model by maximizing the likelihood (if you are a classical econometrician)
or by combining the likelihood with a prior density for the model parameter
to form a posterior distribution (if you are a Bayesian one). Also, we can
compare how well different economies explain the data with likelihood ratio
tests or Bayes factors.
The particle filter is a sequential Monte Carlo method that tracks the
unobservable distribution of states of a dynamic model conditional on
observables. The reason we are keenly interested in tracking such
distribution is that, with it, we can obtain a consistent evaluation of the
likelihood of the model using a straightforward application of the law of
the large numbers.
The particle filter substitutes the population conditional distribution of
states, which is difficult if not impossible to characterize, by an
empirical distribution generated by simulation. The twist of ingenuity of
the particle filter is that the simulation is generated through a device
known as sequential importance resampling (SIR). SIR ensures that the Monte
Carlo method achieves sufficient accuracy in a reasonable amount of time.
Hence, the particle filter delivers the key object that we need to estimate
nonlinear and/or nonnormal DSGE models: an efficient evaluation of the
likelihood function of the model.
To illustrate our method, we follow Greenwood, Hercowitz, and Krusell (1997
and 2000). These authors have vigorously defended the importance of
technological change specific to new investment goods for understanding
postwar U.S. growth and aggregate fluctuations. We estimate a version of
their business cycle model. The model has three shocks: to preferences, to
neutral technology, and to investmentspecific technology. All three shocks
display stochastic volatility. Also, there are two unit roots and
cointegration relations derived from the balanced growth path properties of
the economy. We solve the model using second order approximations and apply
the particle filter to evaluate the likelihood function.
The data reveal three facts. First, there is strong evidence for the
presence of stochastic volatility in U.S. data. Capturing this phenomenon
notably improves the fit of the model. Second, the decline in aggregate
volatility has been a gradual trend and not, as suggested by the literature,
the result of an abrupt drop in the mid 1980s. The fall in volatility
started in the late 1950s, was interrupted in the late 1960s and early
1970s, and resumed around 1979. Third, changes in the volatility of
preference shocks account for most of the variation in the volatility of
output growth over the last 50 years.
Summarizing, our paper shows how to conduct an estimation of nonlinear
and/or nonnormal DSGE models, that such estimation is feasible in real
life, and that it helps us to obtain many answers we could not otherwise
generate.
Complementary Papers
Parallel to our main line of estimation of nonlinear and/or nonnormal DSGE
models, we have written other papers that complement our work.
The first paper in this line of research is "Comparing Dynamic Equilibrium
Economies to Data: a Bayesian Approach," published in the Journal
of Econometrics. This paper studies the properties of the Bayesian approach
to estimation and comparison of dynamic economies. First, we show that
Bayesian methods have a classical interpretation: asymptotically, the
parameter point estimates converge to their pseudotrue values, and the best
model under the KullbackLeibler distance will have the highest posterior
probability. Both results hold even if the models are nonnested,
misspecified, and nonlinear. Second, we illustrate the strong small sample
behavior of the approach using a wellknown example: the U.S. cattle cycle.
Bayesian estimates outperform maximum likelihood, and the proposed model is
easily compared with a set of Bayesian vector autoregressions.
A second paper we would like to mention is "A,B,C's (and D)'s for
Understanding VARs", written with Thomas Sargent and Mark Watson. This
paper analyzes the connections between DSGE models and vector
autoregressions (VARs), a popular empirical strategy. An approximation to
the equilibrium of a DSGE model can be expressed in terms of a linear state
space system. An associated linear state space system determines a vector
autoregression for observables available to an econometrician. We provide a
simple algebraic condition to check whether the impulse response of the VAR
resembles the impulse response associated with the economic model. If the
condition does not hold, the interpretation exercises done with VARs are
misleading. Also, the paper describes many interesting links between DSGE
models and empirical representations. Finally, we give four examples that
illustrate how the condition works in practice.
In "Comparing Solution Methods for Dynamic Equilibrium Economies",
published in the Journal of Economic Dynamics and Control and
joint with Boragan Aruoba, of the University of Maryland, we assess different
solution methods for DSGE models. This comparison is relevant because when
we estimate DSGE models, we want to solve them quickly and accurately. In
the paper, we compute and simulate the stochastic neoclassical growth model
with leisure choice by implementing first, second, and fifth order
perturbations in levels and in logs, the finite elements method, Chebyshev
polynomials, and value function iteration for several calibrations. We
document the performance of the methods in terms of computing time,
implementation complexity, and accuracy, and we present some conclusions and
pointers for future research.
This paper motivated us to think about the possibility of developing new and
efficient solution techniques for dynamic models. A first outcome of this
work has been "Solving DSGE Models with Perturbation Methods and a Change
of Variables," also published in the Journal of Economic Dynamics
and Control. This paper explores the changes of variables technique to
solve the stochastic neoclassical growth model with leisure choice. We build
upon Kenn Judd's idea of changing variables in the computed policy functions
of the economy. The optimal change of variables for an exponential family
reduces the average absolute Euler equation errors of the solution of the
model by a factor of three. We demonstrate how changes of variables can
correct for variations in the risk level of the economy even if we work with
firstorder approximations to the policy functions. Moreover, we can keep a
linear representation of the laws of motion of the model if we employ a
nearly optimal transformation. We finish by discussing how to employ our
results to estimate DSGE models
What is Next?
The previous paragraphs were just a summary of the work we have done on the
estimation of DSGE models. But there is plenty of work ahead of us.
Currently, we are working on a commissioned article for the NBER
Macroeconomics Annual. This paper will study the following question: How
stable over time are the socalled "structural parameters" of DSGE models?
At the core of these models, we have the parameters that define the
preferences and technology that describe the environment. Usually, we assume
that these parameters are structural in the sense of Hurwicz (1962): they
are invariant to interventions, including shocks by nature. Their invariance
permits us to exploit the model fruitfully as a laboratory for quantitative
analysis. At the same time, the profession is accumulating more and more
evidence of parameter instability in dynamic models. We are undertaking the
first systematic analysis of parameter instability in the context of a
"state of the art" DSGE model. One important application of this research
is that we can explore changes in monetary policy over time. If you model
monetary policy as a feedback function, you can think about the policy
change as a change in the parameters of that feedback function, i.e., as one
particular example of parameter drifting.
A related project is our work on seminonparametric estimation of DSGE
models. The recent DSGE models used by the profession are complicated
structures. They rely on many parametric assumptions: utility function,
production function, adjustment costs, structure of stochastic shocks, etc.
Some of those parametric choices are based on restrictions imposed by the
data on theory. For example, the fact that labor income share has been
relative constant since 1950s suggests a CobbDouglas production function.
Unfortunately, many other parametric assumptions are not. Researchers choose
parametric forms for those functions based only on convenience. How
dependent are our findings on the previous parametric assumptions? Can we
make more robust assumptions? Our conversations with Xiaohong Chen have
convinced us that this in a worthwhile avenue of improvement. We are
pursuing the estimation of DSGE models when we relax parametric assumptions
along certain aspects of the model with the method of Sieves, which Xiaohong
has passionately championed.
We would also like to better understand how to compute and estimate models
with Markov regimeswitching. Those models are a nice alternative to
stochastic volatility models. They allow for less variation in volatility,
hence gaining much efficiency. Also, they may better capture phenomena such
as the abrupt break in U.S. interest rates in 1979. Regimeswitching models
present interesting challenges in terms of computation and estimation.
Finally, we are interested in the integration of microeconomic heterogeneity
within estimated DSGE models. James Heckman has emphasized again and again
that individual heterogeneity is the defining feature of micro data (see
Browning, Hansen, and Heckman, 1999, for the empirical importance of
individual heterogeneity and its relevance for macroeconomists). Our macro
models need to move away from the basic representative agent paradigm and
include richer configurations. The work of Victor RíosRull in this
area has been path breaking. Of course, this raises the difficult challenge
of how to effectively estimate these economies. We expect to tackle some of
those difficulties in the near future.
References:
An, S. (2005). "Bayesian Estimation of DSGE Models: Lessons from Second
Order Approximations." Mimeo, University of Pennsylvania.
Amisano, G. and O. Tristani (2005). "Euro Area Inflation
Persistence in an Estimated Nonlinear DSGE Model." Mimeo, European Central Bank.
Aruoba, S.B., J. FernándezVillaverde and J. RubioRamí
rez (2006). " Comparing Solution Methods for Dynamic Equilibrium
Economies." Journal of Economic Dynamics and Control 30,
24472508.
Browning, M., L.P. Hansen, and J.J. Heckman (1999). " Micro Data
and General Equilibrium Models." in: J.B. Taylor and M. Woodford (eds.),
Handbook of Macroeconomics, volume 1, chapter 8, pages 543633
Elsevier.
FernándezVillaverde, J. and J. RubioRamírez (2004).
" Comparing Dynamic Equilibrium Models to Data: A Bayesian Approach."
Journal of Econometrics 123, 153187.
FernándezVillaverde, J. and J. RubioRamírez (2005a).
" Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear
Likelihood." Journal of Applied Econometrics, 20, 891910.
FernándezVillaverde, J. and J. RubioRamírez (2005b).
" Estimating Macroeconomic Models: A Likelihood Approach." NBER
Technical Working Paper T0321.
FernándezVillaverde, J. and J. RubioRamírez (2006).
" Solving DSGE Models with Perturbation Methods and a Change of Variables."
Journal of Economic Dynamics and Control 30, 25092531.
FernándezVillaverde, J., J. RubioRamírez, T.J.
Sargent, and M. Watson (2006). " A,B,C's (and D)'s for Understanding
VARs." Mimeo, Duke University.
FernándezVillaverde, J., J. RubioRamírez, and M.S.
Santos (2006). " Convergence Properties of the Likelihood of Computed
Dynamic Models." Econometrica 74, 93119.
Geweke, J.F. (1993). " Bayesian Treatment of the Independent
Studentt Linear Model." Journal of Applied Econometrics 1993, 8,
S19S40.
Geweke, J.F. (1994). "Priors for Macroeconomic Time Series and
Their Application." Econometric Theory 10, 609632.
Greenwood, J, Z. Hercowitz, and P. Krusell (1997). " LongRun
Implications of InvestmentSpecific Technological Change."
American Economic Review 87, 342362.
Greenwood, J, Z. Hercowitz, and P. Krusell (2000). " The Role of
InvestmentSpecific Technological Change in the Business Cycle."
European Economic Review 44, 91115.
Hurwicz, L. (1962). "On the Structural Form of Interdependent
Systems". In E. Nagel, P. Suppes, and A. Tarski (eds.), Logic,
Methodology and Philosophy of Science. Stanford University Press.
Justiniano A. and G.E. Primiceri (2006). " The Time Varying
Volatility of Macroeconomic Fluctuations." NBER working paper 12022.
Kim, C. and C.R. Nelson (1999) " Has the U.S. Economy Become More
Stable? A Bayesian Approach Based on a MarkovSwitching Model of the
Business Cycle." Review of Economics and Statistics 81, 608616.
McConnell, M.M. and G. PérezQuirós (2000). " Output
Fluctuations in the United States: What Has Changed Since the Early
1980's?" American Economic Review 90, 14641476.
Sargent, T.J. (2005). " An Interview with Thomas J. Sargent by
George W. Evans and Seppo Honkapohja." Macroeconomic Dynamics 9,
561583.
Stock, J.H. and M.W. Watson (2002). " Has the Business Cycle
Changed, and Why?" NBER Macroeconomics Annual 17, 159218.

EconomicDynamics: In a previous Newsletter (April 2006), PierreOlivier Gourinchas
argued
that the US imbalance in the current account is not as bad as one
thinks
once the expected valuation effect is taken into account: US assets
held
by foreigners will have a lower return than foreign assets held by
Americans. Is there also such an effect in heavily indebted emerging
countries, such as Mexico?

Enrique Mendoza: Yes there is a similar effect but there are important
details worth noting. Countries like Mexico, the Asian Tigers and
China and India, as well as many oil exporters, have built very large
positions in foreign exchange reserves, which consist mostly of U.S.
treasury bills. To give you an idea of the magnitudes, out of the U.S.
net foreign asset position as a share of world GDP of 7 percent in
2005, emerging Asia (the "Tigers" plus China and India) accounts for
about 4 percentage points! In this case, the fall in the value of the
dollar and the low yields on U.S. Tbills played a nontrivial role. The
difference is that Treasury bills are a riskfree asset, whereas in
comparisons visavis industrial countries the differences in returns
pertain to equity, FDI, corporate bonds and other risky assets. This
observation highlights a puzzling fact: the U.S. portfolio of foreign
assets includes a large negative position in government bonds (and
largely visàvis developing countries) but a positive position in private securities (and particularly visàvis other industrial countries).
But perhaps the more important point raised by your question is
whether the global imbalances are good or bad. In this regard, the
work Vincenzo Quadrini, Victor RiosRull and I have been doing has
interesting implications. On the one hand, there is nothing wrong with
the large negative current account and net foreign assets of the U.S.
because we can obtain them as the result of the integration of capital
markets across economies populated by heterogeneous agents and with
different levels of financial development. We document empirical
evidence showing that indeed capital market integration has been a
global phenomenon, but financial development has not. In our analysis,
the observed external imbalances are perfectly consistent with
solvency conditions and there is no financial crisis as some of the
gurus in the financial media have predicted. We can also explain the
U.S. portfolio structure (i.e., a large negative position in public
debt and yet a positive position in private risky assets) as another
outcome of financial globalization without financial development. On
the other hand, we find that agents in the mostfinancially developed
country make substantial welfare gains (of close to 2 percent in the
Lucas measure of utilitycompensating variations in consumption) at
the expense of similarly substantial welfare costs for the lessfinancially developed country. To make matters worse, the burden of
these costs is unevenly distributed, affecting more the agents with
lower levels of wealth in the poorest country.

ED: Sudden stops are current account reversals that coincide with a
rapid
and deep drop in real activity. What is your take on why there is such
a
sharp drop in GDP during sudden stops?

EM:
The short answer is a credit collapse, but let me explain. Let's
split the output collapse of a Sudden Stop into two phases. The
initial stage, on impact in the same quarter as the current account
reversal, and the second stage, which is the recession in the periods
that follow. Growth accounting I have done for Mexico shows that
standard measures of capital and labor explain very little of the
initial output drop, while changes in capacity utilization and demand
for imported intermediate goods played an important role, along with a
still important contribution of a decline in TFP that we still need to
understand better. In the second stage, the collapse in investment of
the initial stage starts to affect demand for other inputs and
production, so it starts to play a role as well. These changes can be
ultimately linked to the loss of credit market access reflected in the
current account reversal if we consider environments in which credit
frictions result in constraints linking access to credit to the market
value of incomes or assets used as collateral. In models with these
features, the constraints can become suddenly binding as a result of
typical shocks to "fundamentals" like the world interest rate, the
terms of trade or "true" domestic TFP when economic agents are
operating at high leverage ratios (e.g., South East Asia in 1997).
Agents rush to firesale assets to meet these constraints, but when
they do they make assets and goods prices fall, tightening credit
conditions further, and producing the classic debtdeflation spiral
that Irving Fisher envisioned in his classic 1933 article.

ED: But how exactly does a debtdeflation cause the two stages of output
drop that you mentioned, and how large can we expect these effects to be?

EM:
For the first stage, the decline in the value of collateral
assets, and in the holdings of those assets, tightens access to credit
for working capital, thus reducing factor demands, capacity
utilization and output. Here the key issue is not just that some or
all costs of production are paid with credit, but that the access to
that credit is vulnerable to occasionally binding collateral
constraints. In addition, if the deflation hits adversely relative
prices in some sectors (e.g. the relative price of nontradables, as it
occurs in Sudden Stops), the value of the marginal product of factor
demands falls in those sectors, and leads them to contract. If they
are a large sector of the economy, as is the case with the
nontradables sector in emerging economies, then aggregate GDP can also
fall sharply. For stage two, the decline in the capital stock induced
by the initial investment collapse, and the possibility of continued
weakness in credit access for working capital, can explain the
recession beyond the initial quarter. Recovery can then be fast or
slow depending on "luck" (i.e., terms of trade, world interest rates,
"true" TFP, etc.) and/or the speed of the endogenous adjustment that
returns the economy to leverage ratios at which the collateral
constraints and the debtdeflation spiral do not bind.
My research on models with these features shows that the debt
deflation mechanism produces large amplification and asymmetry in the
responses of macro aggregates to shocks of standard magnitudes,
conditional on highleverage states that trigger the credit
constraints. Moreover, current account reversals in these models are
an endogenous outcome, rather than an exogenous assumption as in a
large part of the Sudden Stops literature. The declines in investment
and consumption, and the current account reversals, are very similar
to the ones observed in Sudden Stops. The output collapse is large,
but still not as large as in the data. On the other hand,
precautionary saving behavior implies that longrun business cycle
dynamics are largely invariant to the presence of the credit
constraints. Interestingly, this is also a potential explanation for
the large accumulation of net foreign assets in emerging economies
that I mentioned in response to your first question: this can be viewed
as a Neomercantilist policy to build a war chest of foreign reserves
to self insure against Sudden Stops. All these findings are documented
in my 2006 piece in the AER Papers & Proceedings and in a recent NBER
working paper.

ED: In your first answer, you argue that there are substantial costs from
living in a country with underdeveloped financial markets. In the second,
sudden stops happens at least in part due to an extensive use of credit
markets instead of internal funds in financing economic activity. What is
then the policy advice?

EM: Actually, the two arguments are quite consistent if you think
about them this way. In the model of global imbalances, a country's
degree of financial development is measured by the degree of market
completeness, or contract enforcement, that its own institutional and
legal arrangements support. If agents cannot steal at all, then the
model delivers the predictions of the standard ArrowDebreu complete
markets framework. If agents can steal 100 percent of the excess of
their income under any particular state of nature relative to the
"worst state of nature," then the model delivers the predictions of a
setup in which only nonstate contingent assets are allowed to exist.
However, as Manuel Amador showed in a recent discussion of our Global
Imbalances paper, this enforceability constraint can also be expressed
as a borrowing constraint that limits debt not to exceed a fraction of
the value of the borrowers' income. Now, this is the same as one
variant of the credit constraints used in the Sudden Stop models I
have studied (particularly one that limits debt denominated in units
of tradable goods not to exceed a fraction of the value of total
income, which includes income from the nontradables sector valued in
units of tradables).
In both, the model of global imbalances and the Sudden Stop models,
the main problem is the existence of credit constraints affecting
borrowers from financially underdeveloped countries that originate in
frictions in credit markets, such as limited enforcement. In both
models, domestic borrowers face these frictions whether they borrow at
home or abroad (although the Sudden Stop models are representative
agent models, so all the borrowing at equilibrium is from the rest of
the world). Given this similarity between the models, you can expect
that the policy advice is broadly the same: The optimal policy is to
foster financial development by improving the contractual environment
of credit markets. Actually, in the Imbalances paper, the less
developed country can avoid the welfare costs of globalization by just
bringing its enforcement level to par with that of the most
financially developed country: it does not need to eliminate the
enforcement problem completely. Assuming improving financial
institutions and contract enforcement is not possible, or that it
takes too long, policies like the build up of foreign reserves as self
insurance, or proposals going around now for partially completing
markets by having international organizations support markets for
bonds linked to GDP or terms of trade, or to prevent asset price
crashes using mechanisms akin to price guarantees on the emerging
markets asset class, are a distant second best, but still much
preferred to remaining vulnerable to the deep recessions associated
with Sudden Stops.
References
Fisher, I. 1933. "The DebtDeflation Theory of Great Depressions", Econometrica, vol. 1, pp. 337357.
Gourinchas, P.O. 2006. " The Research Agenda: PierreOlivier Gourinchas on Global Imbalances and Financial Factors", EconomicDynamics Newsletter, vol. 7 (1).
Mendoza, E. G. 2006. "Lessons from the DebtDeflation Theory of Sudden Stops", American Economic Review Papers and Proceedings, vol 96 (2), pp. 411416 (extended version: NBER working paper 11966).
Mendoza, E.G. 2006. "Endogenous Sudden Stops in a Business Cycle Model with Collateral Constraints: A Fisherian Deflation of Tobin's Q". Mimeo, NBER working paper 12564.
Mendoza, E. G. , V. Quadrini and J.V. RíosRull 2006. "Financial Integration, Financial Deepness and Global Imbalances". Mimeo, University of Maryland.
Review of Economic Dynamics: Letter from the Editor
The Review of Economic Dynamics has had a great year. In my message last
year, I said that I wanted the RED to have more of the energy and excitement
of the SED conference. We've made tremendous progress in that direction.
I'm very excited about the quality of publications. If you're not reading
the RED regularly, you're missing out on great papers like:
"Robustness and information processing," K. Kasa (Jan 06)
"Redistribution, taxes, and the median voter," J. Benhabib and M. Bassetto
(Apr 06)
"Understanding differences in hours worked," R. Rogerson (Jul 06)
"Credibility and endogenous societal discounting," C. Sleet and S.
Yeltekin (Jul 06)
"Changes in women's hours of market work: The role of returns to
experience," C. Olivetti (Oct 06)
"Entry costs and stock market participation over the life cycle," S. Alan
(Oct 06)
(By the way, for those of you who want your papers processed quickly, the
above papers published in July and October 2006 were all submitted for the
first time
in September 2005 or after. At least two of them went through multiple
rounds of refereeing/editing in that time. Processing rates at the RED are
VERY fast.)
We've had a remarkable increase in submissions, without any attenuation in
quality. In all of 2005, we had 142 new submissions (not counting
resubmissions)  that was the most in
the history of the journal. In 2006, we've already had over 200 (again
not counting resubmissions). (Note that most journals include
resubmissions in their statistics about submissions.) It looks like
more and more people are following what I called "The Rule" in my letter
from last year: send your paper to a top five general interest journal or
send it to RED.
We've made some institutional changes too. As David Levine says in his
Presidential letter, the Advisory Board and I have decided
to institute term limits for editors and associate editors. This will ensure
a steady flow of new intellectual energy into the journal. As part of this
process, we have two new editors joining us: Dirk Krueger and Urban Jermann
of the University of Pennsylvania. (Dirk is officially coming on board in
January; Urban is already with us.) We are very excited about having these
remarkable scholars being part of our editorial team.
At the same time, Gary Hansen and Richard Rogerson will be stepping down as
editors as of January 2007. (They will finish handling all of their current
papers.) They've done a great job through their years of service of making
the journal into what it is today. The journal and the Society thanks for
them for their dedication. I am happy to say that they will be staying with
the RED as associate editors.
Let me close by saying thanks. Thanks first to my fellow editors and
associate editors, who do a fantastic job of turning good papers into great
ones with their ideas and insights. Thanks too to all of our referees 
they do a marvelous job of reading and evaluating many difficult papers
quickly.
Most of all, I'd like to thank our authors. I've really enjoyed being the
editor of the RED, and that's largely because of you. There's so much
exciting and interesting work out there, and it's incredibly rewarding for
me to be (even a small) part of bringing that work to fruition. Keep your
submissions coming!
Sincerely,
Narayana Kocherlakota, CoOrdinating Editor
Review of Economic Dynamics
Society for Economic Dynamics: Letter from the President
Dear SED Members and Friends:
The 2006 meetings of the SED, held in Vancouver, British Columbia, were of
our usual great quality. This year we ran about 20% more sessions than last
year, with about twelve parallel session at a time for the three days of the
conference. The program chairs Matthias Doepke and Esteban RossiHansberg
put together a fabulous scientific program, and the local organizers David
Andolfatto, Henry Siu, and Mick Devereux made sure that everything went
smoothly. I got to attend a number of sessions, and they were first rate.
The 2007 meetings will be held in Prague (Czech Republic) June 28  30.
Details are at http://www.economicdynamics.org/sed2007.htm and some of the
planned events are already listed there. The program chairs are Ricardo
Lagos, Noah Williams, and the local organizing committee is Radim Bohacek,
and Michal Kejak. Radim and Michal have lined up a great venue at the
Profesni dum. Our plenary speakers are going to be Dilip Abreu, Robert
Shimer and Kenneth Wolpin. The submission deadline is February 15, 2007,
space is limited, and I expect a great program, so get working on those
papers.
This is a good opportunity to thank Boyan Jovanovic for his outstanding
leadership in continuing to build the society  the meetings have been
fantastic, and the books are comfortably in the green. I'd also like to take
note of Narayana Kocherlakota's outstanding work as the new coordinating
editor of RED. As RED is now a wellestablished journal, the executive
committee has instituted a scheme of term limits for the editorial board,
and I expect to see many of you on the board in future years.
I look forward to seeing you in Prague.
Sincerely,
David Levine, President
Society for Economic Dynamics
Society for Economic Dynamics: 2007 Meeting Call for Papers
The 18th annual meetings of the Society for Economic Dynamics will be held
June 2830, 2007 in Prague, Czech Republic. The plenary speakers are Dilip
Abreu (Princeton), Robert Shimer (Chicago), and Kenneth Wolpin
(Pennsylvania). The program cochairs are Ricardo Lagos (NYU) and Noah
Williams (Princeton).
The program will be made up from a selection of invited and submitted
papers. The Society now welcomes submissions for the Prague program.
Submissions may be from any area in economics. A program committee will
select the papers for the conference. The deadline for submissions is
February 15, 2007. Further details and instructions on how to submit a
paper are available at:
http://www.EconomicDynamics.org/sed2007.htm
Impressum
The EconomicDynamics Newsletter is a free supplement
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Dynamics
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responsible
editors are Christian
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