dsge: Dynamic Stochastic General Equilibrium Models

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 <doi:10.1016/S0165-1889(03)00043-5>). Solution uses the method of undetermined coefficients (Klein, 2000 <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 <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.

install.packages("dsge")

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