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This package implements the New York Fed dynamic stochastic general equilibrium (DSGE) model and provides general code to estimate many user-specified DSGE models. The package is introduced in the Liberty Street Economics blog post, “The FRBNY DSGE Model Meets Julia.”
This package implements the Sequential Monte Carlo (SMC) sampling algorithm, an alternative to Metropolis Hastings Markov Chain Monte Carlo sampling for approximating posterior distributions. Our implementation features an adaptive schedule and what we term generalized tempering for “online” estimation, as outlined in our paper, “Online Estimation of DSGE Models.”
This package implements common computational routines for state-space models. Provided algorithms include the Kalman filter; Chandrasekhar recursions; Tempered Particle Filter; Hamilton and Koopman Kalman smoothers; as well as Carter and Kohn and Durbin and Koopman simulation smoothers.
Coming Soon
Post-PAP Implementation Code
Package for automatic implementation of methods in Sarfati and Vilfort (2025).