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Report
Online Estimation of DSGE Models
This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for ?online? estimation, and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts of DSGE models with and without financial frictions and document the benefits of conditioning DSGE model forecasts on nowcasts ...
Report
Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)
This paper discusses prior elicitation for the parameters of dynamic stochastic general equilibrium (DSGE) models and provides a method for constructing prior distributions for a subset of these parameters from beliefs about the moments of the endogenous variables. The empirical application studies the role of price and wage rigidities in a New Keynesian DSGE model and finds that standard macro time series cannot discriminate among theories that differ in the quantitative importance of nominal frictions.
Working Paper
Evaluating DSGE model forecasts of comovements
This paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. The authors construct posterior predictive checks to evaluate the calibration of conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation DSGE model as well as the Smets and Wouters (2007) model using real-time data. They find that ...
Working Paper
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 ...
Working Paper
DSGE model-based forecasting of non-modelled variables
This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). The authors use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to the state variables of the DSGE model. Predictions for the non-core variables are obtained by applying their measurement equations to DSGE model- generated forecasts of the state variables. Using a medium-scale New Keynesian DSGE model, the authors apply their approach to generate and evaluate ...
Working Paper
Learning and monetary policy shifts
This paper estimates a dynamic stochastic equilibrium model in which agents use a Bayesian rule to learn about the state of monetary policy. Monetary policy follows a nominal interest rate rule that is subject to regime shifts. The following results are obtained. First, the author's policy regime estimates are consistent with the popular view that policy was marked by a shift to a high-inflation regime in the early 1970s, which ended with Volcker's stabilization policy at the beginning of the 1980s. Second, while Bayesian posterior odds favor the "full-information" version of the model in ...
Working Paper
Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)
In Bayesian analysis of dynamic stochastic general equilibrium (DSGE) models, prior distributions for some of the taste-and-technology parameters can be obtained from microeconometric or presample evidence, but it is difficult to elicit priors for the parameters that govern the law of motion of unobservable exogenous processes. Moreover, since it is challenging to formulate beliefs about the correlation of parameters, most researchers assume that all model parameters are independent of each other. We provide a simple method of constructing prior distributions for a subset of DSGE model ...
Working Paper
On the fit and forecasting performance of New Keynesian models
The paper provides new tools for the evaluation of DSGE models and applies them to a large-scale New Keynesian dynamic stochastic general equilibrium (DSGE) model with price and wage stickiness and capital accumulation. Specifically, we approximate the DSGE model by a vector autoregression (VAR) and then systematically relax the implied cross-equation restrictions. Let --denote the extent to which the restrictions are being relaxed. We document how the in- and out-of-sample fit of the resulting specification (DSGE-VAR) changes as a function of --. Furthermore, we learn about the precise ...
Working Paper
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.
Working Paper
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 (JPE 2005) and Smets and Wouters (JEEA 2003). 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.