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Learn about a powerful Julia package for developing and solving dynamic stochastic general equilibrium (DSGE) models in this 34-minute conference talk from JuliaCon 2024. Explore how to handle complex model features including forward-looking expectations, nonlinearity, and high dimensionality through user-friendly syntax and automatic variable declaration. Master the package's effective steady state solver for fast model prototyping, work with nonlinear model solutions up to third order pruned perturbation, and implement gradient-based samplers like NUTS and HMC for model estimation. Discover unique capabilities that distinguish it from alternatives like DifferentiableStateSpaceModels.jl, particularly for larger models without analytical non-stochastic steady state solutions. Designed for central bankers, regulators, graduate students, and academic researchers focused on DSGE modeling.
