Dsge Models In Macroeconomics
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DSGE Models in Macroeconomics
Author: Nathan Balke
language: en
Publisher: Emerald Group Publishing
Release Date: 2012-11-29
This volume of Advances in Econometrics contains articles that examine key topics in the modeling and estimation of dynamic stochastic general equilibrium (DSGE) models. Because DSGE models combine micro- and macroeconomic theory with formal econometric modeling and inference, over the past decade they have become an established framework for analy
Introduction to Dynamic Macroeconomic General Equilibrium Models
Author: Jose Luis Torres Chacon
language: en
Publisher: Vernon Press
Release Date: 2015-03-01
This book offers an introductory step-by-step course to Dynamic Stochastic General Equilibrium modelling. Modern macroeconomic analysis is increasingly concerned with the construction, calibration and/or estimation and simulation of Dynamic General Equilibrium (DGE) models. The book is intended for graduate students as an introductory course to DGE modelling and for those economists who would like a hands-on approach to learning the basics of modern dynamic macroeconomic modelling. The book starts with the simplest canonical neoclassical DGE model and then gradually extends the basic framework incorporating a variety of additional features, such as consumption habit formation, investment adjustment cost, investment-specific technological change, taxes, public capital, household production, non-ricardian agents, monopolistic competition, etc. The book includes Dynare codes for the models developed that can be downloaded from the book's homepage.
Bayesian Estimation of DSGE Models
Author: Edward P. Herbst
language: en
Publisher: Princeton University Press
Release Date: 2015-12-29
Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.