Frank Schorfheide

Bayesian analysis of DSGE models

This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the non-linear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models and a DSGE model that was solved with a second-order perturbation method.

Testing for Indeterminacy: An Application to U. S. Monetary Policy

This paper considers a prototypical monetary business cycle model for the U.S. economy, in which the equilibrium is undetermined if monetary policy is ‘inactive? In previous multivariate studies it has been common practice to restrict parameter estimates to values for which the equilibrium is unique. We show how the likelihood-based estimation of dynamic stochastic general equilibrium models can be extended to allow for indeterminacies and sunspot fluctuations. We propose a posterior odds test for the hypothesis that the data are best explained by parameters that imply determinacy. Our empirical results show that the Volcker-Greenspan policy regime is consistent with determinacy, whereas the pre-Volcker regime is not. We find that before 1979 non-fundamental sunspot shocks may have contributed significantly to inflation and interest rate volatility, but essentially did not affect output fluctuations.

Priors from General Equilibrium Models for VARs

This paper uses a simple New Keynesian monetary DSGE model as a prior for a vector autoregression and shows that the resulting model is competitive with standard benchmarks in terms of forecasting and can be used for policy analysis.

On the Fit of New Keynesian Models

This article provides new tools for the evaluation of dynamic stochastic general equilibrium (DSGE) models and applies them to a large-scale new Keynesian model. We approximate the DSGE model by a vector autoregression, and then systematically relax the implied cross-equation restrictions and document how the model fit changes. We also compare the DSGE models impulse responses to structural shocks with those obtained after relaxing its restrictions. We find that the degree of misspecification in this large-scale DSGE model is no longer so large as to prevent its use in day-to-day policy analysis, yet is not small enough to be ignored.

A Bayesian Look at New Open Economy Macroeconomics

This paper develops a small-scale two-country model following the new open economy macroeconomics paradigm. Under autarky, the model specializes to the familiar three-equation New Keynesian dynamic stochastic general equilibrium (DSGE) model. We discuss two challenges to successful estimation of DSGE models: potential model misspecification and identification problems. We argue that prior distributions and Bayesian estimation techniques are useful to cope with these challenges. We apply these techniques to the two-country model and fit it to data from the United States and the Euro area. We compare parameter estimates from closed and open economy specifications, study the sensitivity of parameter estimates to the choice of prior distribution, examine the propagation of monetary policy shocks, and assess the models ability to explain exchange-rate movements.

Computing sunspot equilibria in linear rational expectations models

Abstract We provide a computationally simple method of analyzing the effects of fundamental and sunspot shocks in linear rational expectations models when the equilibrium is indeterminate. Under indeterminacy sunspots can affect model dynamics through endogenous forecast errors. Moreover, the effect of fundamental shocks on forecast errors is not uniquely determined. The solution method is illustrated with a New Keynesian dynamic stochastic equilibrium model that can be solved analytically. Under a passive interest-rate rule, the response of inflation to an unanticipated interest rate cut is ambiguous: there are some equilibria in which inflation increases and others in which prices fall.

Learning and Monetary Policy Shifts

This paper estimates a dynamic stochastic equilibrium model in which monetary policy follows a nominal interest rate rule that is subject to regime switches in the target ination rate. Two specifications are considered: agents know the current state of monetary policy (full information) and agents use Bayesian updating to infer the policy regime (learning). First, our policy regime estimates are consistent with the view that policy was marked by a shift to a high-inflation regime in the early 1970s which ended with Volckers stabilization policy. Second, while Bayesian posterior odds favor the full-information version of the model, the fall of interest rates, actual and expected inflation in the early 1980s is better captured by the delayed response of the learning specication. Third, monetary policy shocks of up to two standard deviations essentially do not trigger the Bayesian learning mechanism. Yet due to non-linearities, interventions that lead to small initial interest rate changes may be associated with much larger effects on output and inflation than under full information. (Copyright: Elsevier)

Bayesian Analysis of DSGE Models—Rejoinder

We would like to thank all the discussants for their stimulating comments. While our article to a large extent reviews current practice of Bayesian analysis of Dynamic Stochastic General Equilibrium (DSGE) models the discussants provide many ideas to improve upon the current practice, thereby outlining a research agenda for the years to come. In our rejoinder we will briefly revisit some of the issues that were raised.

Learning-by-Doing as a Propagation Mechanism

This Paper suggests that skill accumulation through past work experience, or ‘learning-by-doing’ (LBD), can provide an important propagation mechanism in a dynamic stochastic general equilibrium model, as the current labour supply affects future productivity. Our econometric analysis uses a Bayesian approach to combine micro-level panel data with aggregate time series. Formal model evaluation shows that the introduction of the LBD mechanism improves the models ability to fit the dynamics of aggregate output and hours.

Estimation and Evaluation of DSGE Models: Progress and Challenges

Estimated dynamic stochastic equilibrium (DSGE) models are now widely used for empirical research in macroeconomics as well as for quantitative policy analysis and forecasting at central banks around the world. This paper reviews recent advances in the estimation and evaluation of DSGE models, discusses current challenges, and provides avenues for future research.