Bayesian Analysis of DSGE Models—Rejoinder

By Sungbae An, Frank Schorfheide. 2007

142 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cf62a9e8c5c7f10d9c8d0f45273e49123bf7901a

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In Economics and Econometrics

Citations in the corpus

Stock market conditions and monetary policy in a DSGE model for the U.S. (2010, methodology): “We estimate our model with Bayesian techniques (see An and Schorfheide, 2007 for an overview), implemented with DYNARE.18 We focus on U.S. post-WWII data, consistently with a large body of recent literature (Smets and Wouters, 2007, Justiniano and Primiceri, 2008a and 2008b, and Justiniano et al,…”
Investment shocks and business cycles (2010, methodology): “We use Bayesian methods to characterize the posterior distribution of the structural parameters of the model (see An and Schorfheide (2007) for a survey).”
Addressing Household Indebtedness: Monetary, Fiscal or Macroprudential Policy? (2017, methodology): “We estimate the parameters of the model using Bayesian likelihood methods (An and Schorfheide, 2007; Fernández-Villaverde, 2010) and U.S. macroeconomic and financial data.”
Monetary policy under uncertainty in an estimated model with labor market frictions (2008, methodology): “We estimate the model with Bayesian methods (see An and Schorfheide (2007) for a review).”
Macroeconomic Dynamics Near the ZLB: A Tale of Two Countries (2016, methodology): “The implementation of the posterior sampler follows An and Schorfheide (2007) and is described in the Online Appendix.9 We fix a subset of the parameters prior to the estimation.”
Remittances and the Dutch disease (2009, methodology): “The Bayesian estimation technique uses a general equilibrium approach that addresses the identification problems of reduced-form models (see An and Schorfheide, 2007, for a survey), and outperforms GMM and maximum likelihood methods in small samples (Fernández-Villaverde and Rubio-Ramírez, 2004).”
Testing the assumptions behind importance sampling (2009, unknown): “An and Schorfheide (2007).”
Size, Trend, and Policy Implications of the Underground Economy (2014, unknown): “Our inferential procedure is based on a Random Walk Metropolis-Hastings algorithm, as suggested, for instance, in An and Schorfheide (2007), in which the proposal distribution depends uniquely on the current state of the chain at time j, i.e., q(θ|θ(j)).”
Estimation with overidentifying inequality moment conditions (2009, unknown): “In principle, one could estimate the entire model using likelihood-based techniques (see An and Schorfheide (2007) for a survey) to obtain estimates of the policy rule coefficients.”
Tailored randomized block MCMC methods with application to DSGE models (2010, methodology): “This problem was demonstrated by An and Schorfheide (2007) who noted that the algorithm was unable to escape the region surrounding one of the modes.”