Economic Dynamics Newsletter

Volume 8, Issue 1 (November 2006)

The EconomicDynamics Newsletter is a free supplement to the Review of Economic Dynamics (RED). It is published twice a year in April and November.

In this issue

Jesús Fernández-Villaverde and Juan F. Rubio-Ramírez on Estimating DSGE Models

Jesús Fernández-Villaverde and Juan F. Rubio-Ramírez 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. Fernández-Villaverde’s RePEc/IDEAS entry and Rubio-Ramírez’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 likelihood-based 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 likelihood-based 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 non-linear and/or non-normal 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 Non-linear and/or Non-normal 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, likelihood-based 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 real-life 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 non-linear 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 non-trivial impact on inference. First, both for simulated and for U.S. data, the non-linear 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 non-linear 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 student-t’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 time-varying 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 time-varying 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.

Time-varying 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 Pérez-Quirós (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 time-varying variance in shocks. One is stochastic volatility. The other one is Markov regime-switching 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 regime-switching models is that they induce fundamental non-linearities and fat tails. Linearization, by construction, precludes any possibility of assessing time-varying 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 non-linear and/or non-normal problems.

How Do We Do It?

Our previous arguments point out the need to evaluate the likelihood function of the non-linear and/or non-normal 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 non-linear and/or non-normal 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 investment-specific 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 non-linear and/or non-normal 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 non-linear and/or non-normal 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 Kullback-Leibler distance will have the highest posterior probability. Both results hold even if the models are non-nested, misspecified, and non-linear. Second, we illustrate the strong small sample behavior of the approach using a well-known 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 first-order 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 so-called “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 semi-nonparametric 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 Cobb-Douglas 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 regime-switching. 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. Regime-switching 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 Ríos-Rull 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.


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. Fernández-Villaverde and J. Rubio-Ramí rez (2006). “Comparing Solution Methods for Dynamic Equilibrium Economies.” Journal of Economic Dynamics and Control 30, 2447-2508.
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 543-633 Elsevier.
Fernández-Villaverde, J. and J. Rubio-Ramírez (2004). “Comparing Dynamic Equilibrium Models to Data: A Bayesian Approach.” Journal of Econometrics 123, 153-187.
Fernández-Villaverde, J. and J. Rubio-Ramírez (2005a). “Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood.” Journal of Applied Econometrics, 20, 891-910.
Fernández-Villaverde, J. and J. Rubio-Ramírez (2005b). “Estimating Macroeconomic Models: A Likelihood Approach.” NBER Technical Working Paper T0321.
Fernández-Villaverde, J. and J. Rubio-Ramírez (2006). “Solving DSGE Models with Perturbation Methods and a Change of Variables.” Journal of Economic Dynamics and Control 30, 2509-2531.
Fernández-Villaverde, J., J. Rubio-Ramírez, T.J. Sargent, and M. Watson (2006). “A,B,C’s (and D)’s for Understanding VARs.” Mimeo, Duke University.
Fernández-Villaverde, J., J. Rubio-Ramírez, and M.S. Santos (2006). “Convergence Properties of the Likelihood of Computed Dynamic Models.” Econometrica 74, 93-119.
Geweke, J.F. (1993). “Bayesian Treatment of the Independent Student-t Linear Model.” Journal of Applied Econometrics 1993, 8, S19-S40.
Geweke, J.F. (1994). “Priors for Macroeconomic Time Series and Their Application.” Econometric Theory 10, 609-632.
Greenwood, J, Z. Hercowitz, and P. Krusell (1997). “Long-Run Implications of Investment-Specific Technological Change.” American Economic Review 87, 342-362.
Greenwood, J, Z. Hercowitz, and P. Krusell (2000). “The Role of Investment-Specific Technological Change in the Business Cycle.” European Economic Review 44, 91-115.
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 Markov-Switching Model of the Business Cycle.” Review of Economics and Statistics 81, 608-616.
McConnell, M.M. and G. Pérez-Quirós (2000). “Output Fluctuations in the United States: What Has Changed Since the Early 1980’s?American Economic Review 90, 1464-1476.
Sargent, T.J. (2005). “An Interview with Thomas J. Sargent by George W. Evans and Seppo Honkapohja.” Macroeconomic Dynamics 9, 561-583.
Stock, J.H. and M.W. Watson (2002). “Has the Business Cycle Changed, and Why?NBER Macroeconomics Annual 17, 159-218.

Q&A: Enrique Mendoza on Financial Frictions, Sudden Stops and Global Imbalances

Erique Mendoza is Professor of International Economics & Finance at the University of Maryland and Resident Scholar at the International Monetary Fund. He has written extensively on international finance, in particular in emerging economies. Mendoza’s RePEc/IDEAS entry.

EconomicDynamics: In a previous Newsletter (April 2006), Pierre-Olivier 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 risk-free asset, whereas in comparisons vis-a-vis 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 vis-à-vis developing countries) but a positive position in private securities (and particularly vis-à-vis 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 Rios-Rull 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 most-financially developed country make substantial welfare gains (of close to 2 percent in the Lucas measure of utility-compensating variations in consumption) at the expense of similarly substantial welfare costs for the less-financially 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 fire-sale assets to meet these constraints, but when they do they make assets and goods prices fall, tightening credit conditions further, and producing the classic debt-deflation spiral that Irving Fisher envisioned in his classic 1933 article.
ED: But how exactly does a debt-deflation 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 debt-deflation 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 high-leverage 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 long-run 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 Neo-mercantilist 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 Arrow-Debreu 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 non-state 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.


Fisher, I. 1933. “The Debt-Deflation Theory of Great Depressions”, Econometrica, vol. 1, pp. 337-357.
Gourinchas, P.-O. 2006. “The Research Agenda: Pierre-Olivier Gourinchas on Global Imbalances and Financial Factors“, EconomicDynamics Newsletter, vol. 7 (1).
Mendoza, E. G. 2006. “Lessons from the Debt-Deflation Theory of Sudden Stops”, American Economic Review Papers and Proceedings, vol 96 (2), pp. 411-416 (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. Ríos-Rull 2006. “Financial Integration, Financial Deepness and Global Imbalances“. Mimeo, University of Maryland.
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 Rossi-Hansberg 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. 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 well-established 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.


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 28-30, 2007 in Prague, Czech Republic. The plenary speakers are Dilip Abreu (Princeton), Robert Shimer (Chicago), and Kenneth Wolpin (Pennsylvania). The program co-chairs 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.

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 re-submissions) – that was the most in the history of the journal. In 2006, we’ve already had over 200 (again not counting re-submissions). (Note that most journals include re-submissions 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!


Narayana Kocherlakota, Co-Ordinating Editor
Review of Economic Dynamics