DSGE model-based estimation of the New Keynesian Phillips curve

This paper surveys estimates of New Keynesian Phillips curve (NKPC) parameters that have been obtained by fitting fully specified dynamic stochastic general equilibrium (DSGE) models to U.S. data. We examine various sources of identification in the context of a simple analytical model. DSGE model-based NKPC estimates tend to be fragile and sensitive to the model specification, in particular if marginal costs are treated as an unobserved variable. Estimates of the NKPC slope lie between 0 and 4. If the observations span the labor share, which is in most instances the model-implied measure of marginal costs, then the slope estimates fall into a much narrower range of 0.005 to 0.135. No consensus has emerged with respect to the importance of lagged inflation in the Phillips curve.Inflation (Finance) ; Phillips curve

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 nonlinear 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.Macroeconomics ; Vector autoregression

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.

Labor-Supply Shifts and Economic Fluctuations

We investigate the role of labor-supply shifts in economic fluctuations. A new VAR identification scheme for labor supply shocks is proposed. According to our VAR analysis of post-war U.S. data, labor-supply shifts account for about half the variation in hours and one-fifth of variation in aggregate output. To assess the role of labor-supply shifts in a more structural framework, estimates from a dynamic stochastic general equilibrium (DSGE) model with stochastic variation in home production technology are compared to those from the VAR. To obtain a VAR identification scheme that is consistent with the DSGE model, we cannot solely rely on ``zero-restrictions''. Instead we indirectly specify a proper prior distribution for impulse responses and variance decompositions and update it through the sample information. Our method provides an alternative to recently proposed identification schemes that rely on inequality restrictions on the direction of impulse responses.Fluctuation of Hours, VAR Identification, Home Production, Bayesian Econometrics

Sticky prices versus monetary frictions: an estimation of policy trade-offs

We develop a two-sector monetary model with a centralized and decentralized market. Activities in the centralized market resemble those in a standard New Keynesian economy with price rigidities. In the decentralized market agents engage in bilateral exchanges for which money is essential. The model is estimated and evaluated based on postwar U.S. data. We document its money demand properties and determine the optimal long-run inflation rate that trades off the New Keynesian distortion against the distortion caused by taxing money and hence transactions in the decentralized market. We find that target rates of -1% or less are desirable, which contrasts with policy recommendations derived from a cashless New Keynesian model.

Monetary Policy Analysis with Potentially Misspecified Models

Policy analysis with potentially misspecified dynamic stochastic general equilibrium (DSGE) models faces two challenges: estimation of parameters that are relevant for policy trade-offs and treatment of estimated deviations from the cross-equation restrictions. This paper develops and explores policy analysis approaches that are either based on a generalized shock structure for the DSGE model or the explicit modelling of deviations from cross-equation restrictions. Using post-1982 U.S. data we first quantify the degree of misspecification in a state-of-the-art DSGE model and then document the performance of different interest-rate feedback rules. We find that many of the policy prescriptions derived from the benchmark DSGE model are robust to the various treatments of misspecifications considered in this paper, but that quantitatively the cost of deviating from such prescriptions varies substantially.

Monetary policy analysis with potentially misspecified models

The paper proposes a novel method for conducting policy analysis with potentially misspecified dynamic stochastic general equilibrium (DSGE) models and applies it to a New Keynesian DSGE model along the lines of Christiano, Eichenbaum, and Evans (JPE2005) and Smets and Wouters (JEEA2003). We first quantify the degree of model misspecification and then illustrate its implications for the performance of different interest-rate feedback rules. We find that many of the prescriptions derived from the DSGE model are robust to model misspecification.Monetary policy

Bayesian Inference for Econometric Models using Empirical Likelihood Functions

Estimators based on moment conditions of the form E[g(X,t)], where t is a finite-dimensional parameter vector of interest, are a popular tool in applied econometrics. Unlike likelihood-based estimators, moment-based estimators do not require the researcher to specify the probability distribution of the random vector X in detail. While the use of inappropriate auxiliary assumptions about the distribution of X potentially leads to misspecification bias, reasonable distributional assumptions may improve the precision of the estimator substantially, in particular in small samples. Most Bayesian inference procedures in econometrics are based on fully specified parametric models. Empirical likelihood functions enable Bayesian inference with semiparametric models. We propose to combine an empirical likelihood function with a prior distribution to conduct Bayesian inference. A prior is constructed by completing the moment-based model with a probability distribution that satisfies the moment constraints. Heuristically, we augment the actual sample with artificial observations from a parametric completion. We examine the large-sample behavior of the posterior and develop Markov-Chain- Monte-Carlo methods to generate draws from the posterior and conduct small-sample inferencesEmpirical Likelihood, Bayesian Inference

Inflation Dynamics in a Small Open Economy Model Under Inflation Targeting: Some Evidence From Chile

The paper estimates a small open economy DSGE model, specified along the lines of Galí and Monacelli (REStud 2005) and Lubik and Schorfheide (JME 2007), on Chilean data for the full inflation targeting period (1999-2007). We study the specification of the policy rule followed by the Central Bank, the dynamic response of inflation to domestic and external shocks, and how these dynamics change under different policy parameters. We use the DSGE-VAR methodology (Del Negro and Schorfheide 2007) to assess the robustness of the conclusions to the presence of model misspecification.