Bayesian Analysis of DSGE Models

By Sungbae An, Frank Schorfheide. 2006

1,576 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/24ada9e49f47008a21bfe6925953ec1396fee081

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In Economics and Econometrics

Citations in the corpus

A Model of the Fed's View on Inflation (2017, neutral): “For a review of results using full-information methods to estimate dynamic stochastic general equilibrium (DSGE) models, see (An and Schorfheide, 2007).”
The Quantitative Importance of News Shocks in Estimated DSGE Models (2012, background): “Recent overviews are presented in An and Schorfheide (2007) and Fernández-Villaverde (2009).”
Forecasting: theory and practice (2020, background): “ in shocks identiﬁcation and forecasting horseraces for the last 15 years. Estimation became possible thanks to computational progress and adoption of Bayesian techniques (for technical details, see: An and Schorfheide, 2007; Herbst and Schorfheide, 2016; Fernandez-´ Villaverde and Guerron-Quintana,”
Policy Risk and the Business Cycle (2014, background): “An and Schorfheide, 2007, on the “dilemma of absurd parameter estimates”).”
Sticky information and model uncertainty in survey data on inflation expectations (2007, background): “In Branch (2004) agents make a discrete choice between (non-nested) alternatives.(2) We (non-parametrically) estimate the density functions implied by these models and compare the fit to the histogram of the actual survey data.”
ArCo: An artificial counterfactual approach for high-dimensional panel time-series data (2018, methodology): “Furthermore, under a modern macroeconomics perspective, reduced-forms for Dynamic Stochastic General Equilibrium (DSGE) models are written as vector autoregressive moving average (VARMA) processes, which, in turn, are nested in the general specification in (6) (FernándezVillaverde, Rubio-Ramírez, Sa”
Appendix: Monetary Policy Drivers of Bond and Equity Risks (2020, positive): “These stylized facts raise the question what macroeconomic forces determine the risk properties of US Treasury bonds, and particularly their changes over time.”
Identification and frequency domain quasi-maximum likelihood estimation of linearized dynamic stochastic general equilibrium models (2012, positive): “DOI: 10.3982/QE126 conducted in the time domain using a state space representation with the aid of Kalman or particle filtering; see An and Schorfheide (2007) and Fernández-Villaverde (2010) for reviews of related literature.”
Government Investment and Fiscal Stimulus (2010, methodology): “The model is log linearized, solved by Sims’s (2001) solution method, and estimated by Bayesian techniques as described in An and Schorfheide (2007).”
How to Maximize the Likelihood Function for a DSGE Model (2010, neutral): “For instance, the Bayesian estimation procedures described by An & Schorfheide (2007) require a maximization of the likelihood function multiplied by the prior distributions to get good starting values for the MCMC analysis.”
Forecasting with DSGE Models (2010, positive): “Bayesian Estimation of DSGE Models We adopt the empirical approach outlined in Smets and Wouters (2003) and An and Schorfheide (2007) and estimate the NAWM employing Bayesian inference methods. This involves obtaining the posterior distribution of the model’s parameters based on its log-linear state”
How Structural Are Structural Parameters? [with Comments and Discussion] (2007, neutral): “The term emt is a random shock distributed ac cording to Jf(0,1).”
How Much Nominal Rigidity is There in the US Economy? Testing a New Keynesian DSGE Model Using Indirect Inference (2009, methodology): “Our method is to focus first on individual variables and then in groups by estimating the best ARMA(i, j) in the case of a single variable and a VAR(1) for a group of variables.”
Monetary and Macroprudential Policy in an Estimated DSGE Model of the Euro Area (2013, neutral): “C t = 1 C C H;t C 1 C + (1 ) 1 C C F;t C 1 C C C 1 ; where C > 0: (2)”