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Daniel F. Waggoner
Researcher at Federal Reserve Bank of Atlanta
Publications - 91
Citations - 4344
Daniel F. Waggoner is an academic researcher from Federal Reserve Bank of Atlanta. The author has contributed to research in topics: Monetary policy & Dynamic stochastic general equilibrium. The author has an hindex of 31, co-authored 90 publications receiving 4009 citations. Previous affiliations of Daniel F. Waggoner include Federal Reserve System.
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Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference
TL;DR: In this paper, rank conditions for structural vector autoregressions (SVARs) are defined and checked as a matrix-filling problem and applied to a wide class of identifying restrictions, including linear and certain nonlinear restrictions.
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Methods for Inference in Large Multiple-Equation Markov-Switching Models
TL;DR: In this paper, a detailed explanation of methods to work to overcome the difficulties of Bayesian inference for hidden Markov chain models is given. But the authors do not consider the model specification issues that apply particularly to structural vector autoregressions.
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Minimal state variable solutions to Markov-switching rational expectations models ☆
TL;DR: In this paper, a method for deriving minimal state variable (MSV) equilibria of a general class of Markov switching rational expectations models and a new algorithm for computing them is presented.
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Conditional forecasts in dynamic multivariate models
Daniel F. Waggoner,Tao Zha +1 more
TL;DR: This article developed Bayesian methods for computing the exact finite-sample distribution of conditional forecasts in the vector autoregressive (VAR) framework and applied them to both structural and reduced-form VAR models.
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Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications
TL;DR: Algorithms to independently draw from a family of conjugate posterior distributions over the structural parameterization when sign and zero restrictions are used to identify structural vector autoregressions (SVARs) are developed.