This package implements Lopez et al. (2018) "Risk-Adjusted Linearizations of Dynamic Equilibrium Models" in Julia. The original companion code for the paper implements the method using MATLAB's Symbolic Math Toolbox. RiskAdjustedLinearizations.jl takes advantage of Julia's speed and flexibility so that the method can be used for solving and estimating large-scale Dynamic Stochastic General Equilibrium (DSGE) models.
DSGE.jl: implements the New York Fed dynamic stochastic general equilibrium (DSGE) model and provides general code to estimate many user-specified DSGE models.
SMC.jl: implements the Sequential Monte Carlo (SMC) sampling algorithm, an alternative to Metropolis Hastings Markov Chain Monte Carlo sampling for approximating posterior distributions. The SMC algorithm implemented is based upon and extends Edward Herbst and Frank Schorfheide's paper "Sequential Monte Carlo Sampling for DSGE Models" and the code accompanying their book, Bayesian Estimation of DSGE Models
StateSpaceRoutines.jl: implements some common routines for state-space models, such as the Kalman filter and Durbin-Koopman smoother.
ModelConstructors.jl: contains the building blocks of model objects. These tools are used extensively by DSGE.jl but are generic to other model objects.