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 first-order
perturbation (linearization around deterministic steady state).
Solution uses the method of undetermined coefficients (Klein, 2000
<doi:10.1016/S0165-1889(99)00045-7>). Likelihood evaluated via the
Kalman filter. Bayesian estimation uses adaptive Random-Walk
Metropolis-Hastings with prior specification. Additional tools include
Kalman smoothing, historical shock decomposition, local identification
diagnostics, parameter sensitivity analysis, second-order perturbation,
occasionally binding constraints, impulse-response functions,
forecasting, and robust standard errors.
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