Computing Equilibria in Dynamic Stochastic Macro-Models with Heterogeneous Agents

2017 ◽  
pp. 185-230 ◽  
Author(s):  
Johannes Brumm ◽  
Felix Kubler ◽  
Simon Scheidegger
Keyword(s):  
Heterogeneous Agents ◽  
Dynamic Stochastic

EQUILIBRIUM IN A SIMPLIFIED DYNAMIC, STOCHASTIC ECONOMY WITH HETEROGENEOUS AGENTS

1991 ◽  
pp. 245-272 ◽  
Author(s):  
Ioannis Karatzas ◽  
Peter Lakner ◽  
John P. Lehoczky ◽  
Steven E. Shreve
Keyword(s):  
Heterogeneous Agents ◽  
Dynamic Stochastic

scholarly journals On non-computability of dynamic stochastic general equilibrium

2020 ◽  
Author(s):  
William Glaciel
Keyword(s):  
General Equilibrium ◽  
Heterogeneous Agents ◽  
Dynamic Stochastic General Equilibrium ◽  
Dsge Models ◽  
Dynamic Stochastic

As can be seen in Richter and Wong (1999), non-computability of general equilibrium has been recognized in economics. However, despite general non-computability, equilibrium can indeed be computed in specific cases. In this paper, further restriction on computability of equilibrium is provided in contexts of dynamic stochastic general equilibrium (DSGE) models with heterogeneous agents, demonstrating that non-computability concerns apply more generally than often understood.

Bayesian Estimation of DSGE Models

2015 ◽  
Author(s):  
Edward P. Herbst ◽  
Frank Schorfheide
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Likelihood Function ◽  
Academic Research ◽  
Dynamic Stochastic General Equilibrium ◽  
Computational Techniques ◽  
Dsge Models ◽  
Monte Carlo Techniques ◽  
Dynamic Stochastic ◽  
Theoretical Foundations

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. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

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