dsge-package: dsge: Dynamic Stochastic General Equilibrium Models

In dsge: Dynamic Stochastic General Equilibrium Models

dsge: Dynamic Stochastic General Equilibrium Models

Description

Specify, solve, and estimate dynamic stochastic general equilibrium (DSGE) models by maximum likelihood and Bayesian methods. Supports both linear models via an equation-based formula interface and nonlinear models via string-based equations with perturbation up to third order (Schmitt-Grohe and Uribe, 2004 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0165-1889(03)00043-5")}). Solution uses the method of undetermined coefficients (Klein, 2000 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0165-1889(99)00045-7")}). Likelihood evaluated via the Kalman filter or a bootstrap particle filter (Gordon et al., 1993). Bayesian estimation uses adaptive Random-Walk Metropolis-Hastings or Particle Marginal Metropolis-Hastings (Andrieu et al., 2010 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1467-9868.2009.00736.x")}) with parallel chain support. Additional tools include Bayes factor model comparison with Kass-Raftery evidence scales, Ramsey optimal policy via linear-quadratic regulator, nonlinear perfect foresight via stacked-time Newton (Juillard et al., 1998), Kalman smoothing, historical shock decomposition, local identification diagnostics, parameter sensitivity analysis, occasionally binding constraints, impulse-response functions, forecasting, and robust standard errors.

Author(s)

Maintainer: Mustapha Wasseja Mohammed [e-mail]

dsge documentation built on Sept. 25, 2026, 5:08 p.m.